diff --git a/cpp/memilio/geography/holiday_data.ipp b/cpp/memilio/geography/holiday_data.ipp
index 5bdf29791a..b7aab2dc8e 100644
--- a/cpp/memilio/geography/holiday_data.ipp
+++ b/cpp/memilio/geography/holiday_data.ipp
@@ -299,4 +299,4 @@ static const std::vector<std::pair<mio::Date, mio::Date>> holidays_16_th = {
 
 } // namespace de
 
-} //namespace holidays
+} // namespace holidays
diff --git a/cpp/memilio/geography/regions.cpp b/cpp/memilio/geography/regions.cpp
index 55a688e92d..fa94248e0a 100644
--- a/cpp/memilio/geography/regions.cpp
+++ b/cpp/memilio/geography/regions.cpp
@@ -30,6 +30,7 @@ namespace regions
 
 StateId get_state_id(int county)
 {
+    // integer division
     return StateId(county / 1000);
 }
 
diff --git a/cpp/memilio/geography/regions.h b/cpp/memilio/geography/regions.h
index 6901d15f22..47730ec7aa 100644
--- a/cpp/memilio/geography/regions.h
+++ b/cpp/memilio/geography/regions.h
@@ -103,7 +103,60 @@ get_holidays(StateId state);
 Range<std::pair<std::vector<std::pair<Date, Date>>::const_iterator, std::vector<std::pair<Date, Date>>::const_iterator>>
 get_holidays(StateId state, Date start_date, Date end_date);
 
+namespace de
+{
+
+struct EpidataFilenames {
+private:
+    EpidataFilenames(std::string& pydata)
+        : population_data_path(mio::path_join(pydata, "county_current_population.json"))
+    {
+    }
+
+public:
+    static EpidataFilenames county(std::string& pydata)
+    {
+        EpidataFilenames s(pydata);
+
+        s.case_data_path        = mio::path_join(pydata, "cases_all_county_age_ma7.json");
+        s.divi_data_path        = mio::path_join(pydata, "county_divi_ma7.json");
+        s.vaccination_data_path = mio::path_join(pydata, "vacc_county_ageinf_ma7.json");
+
+        return s;
+    }
+
+    static EpidataFilenames states(std::string& pydata)
+    {
+        EpidataFilenames s(pydata);
+
+        s.case_data_path        = mio::path_join(pydata, "cases_all_state_age_ma7.json");
+        s.divi_data_path        = mio::path_join(pydata, "state_divi_ma7.json");
+        s.vaccination_data_path = mio::path_join(pydata, "vacc_state_ageinf_ma7.json");
+
+        return s;
+    }
+
+    static EpidataFilenames country(std::string& pydata)
+    {
+        EpidataFilenames s(pydata);
+
+        s.case_data_path        = mio::path_join(pydata, "cases_all_age_ma7.json");
+        s.divi_data_path        = mio::path_join(pydata, "germany_divi_ma7.json");
+        s.vaccination_data_path = mio::path_join(pydata, "vacc_ageinf_ma7.json");
+
+        return s;
+    }
+
+    std::string population_data_path;
+    std::string case_data_path;
+    std::string divi_data_path;
+    std::string vaccination_data_path;
+};
+
+} // namespace de
+
 } // namespace regions
+
 } // namespace mio
 
 #endif //MIO_EPI_REGIONS_H
diff --git a/cpp/memilio/io/parameters_io.cpp b/cpp/memilio/io/parameters_io.cpp
index dff30087e7..d3bdd0ff33 100644
--- a/cpp/memilio/io/parameters_io.cpp
+++ b/cpp/memilio/io/parameters_io.cpp
@@ -19,6 +19,7 @@
 */
 
 #include "memilio/config.h"
+#include "memilio/io/parameters_io.h"
 
 #ifdef MEMILIO_HAS_JSONCPP
 
@@ -30,6 +31,45 @@
 
 namespace mio
 {
+IOResult<std::vector<ScalarType>> compute_divi_data(const std::vector<DiviEntry>& divi_data,
+                                                    const std::vector<int>& vregion, Date date)
+{
+
+    auto max_date_entry = std::max_element(divi_data.begin(), divi_data.end(), [](auto&& a, auto&& b) {
+        return a.date < b.date;
+    });
+    if (max_date_entry == divi_data.end()) {
+        log_error("DIVI data is empty.");
+        return failure(StatusCode::InvalidValue, "DIVI data is empty.");
+    }
+    auto max_date = max_date_entry->date;
+    if (max_date < date) {
+        log_error("DIVI data does not contain the specified date.");
+        return failure(StatusCode::OutOfRange, "DIVI data does not contain the specified date.");
+    }
+
+    std::vector<ScalarType> vnum_icu(vregion.size(), 0.0);
+
+    for (auto&& entry : divi_data) {
+        auto it      = std::find_if(vregion.begin(), vregion.end(), [&entry](auto r) {
+            return r == 0 || r == get_region_id(entry);
+        });
+        auto date_df = entry.date;
+        if (it != vregion.end() && date_df == date) {
+            auto region_idx      = size_t(it - vregion.begin());
+            vnum_icu[region_idx] = entry.num_icu;
+        }
+    }
+
+    return success(vnum_icu);
+}
+
+IOResult<std::vector<ScalarType>> read_divi_data(const std::string& path, const std::vector<int>& vregion, Date date)
+{
+    BOOST_OUTCOME_TRY(auto&& divi_data, mio::read_divi_data(path));
+    return compute_divi_data(divi_data, vregion, date);
+}
+
 IOResult<std::vector<std::vector<ScalarType>>>
 read_population_data(const std::vector<PopulationDataEntry>& population_data, const std::vector<int>& vregion)
 {
diff --git a/cpp/memilio/io/parameters_io.h b/cpp/memilio/io/parameters_io.h
index 9dee5046f2..53128a9f00 100644
--- a/cpp/memilio/io/parameters_io.h
+++ b/cpp/memilio/io/parameters_io.h
@@ -52,67 +52,26 @@ int get_region_id(const EpiDataEntry& data_entry)
 
 /**
  * @brief Extracts the number of individuals in critical condition (ICU) for each region 
- * on a specified date from the provided DIVI data.
- *
- * @tparam FP Floating point type (default: double).
+ * on a specified date from the provided DIVI data-
  *
  * @param[in] divi_data Vector of DIVI data entries containing date, region, and ICU information.
  * @param[in] vregion Vector of region IDs for which the data is computed.
  * @param[in] date Date for which the ICU data is computed.
- * @param[in, out] vnum_icu Output vector containing the number of ICU cases for each region.
- *
- * @return An IOResult indicating success or failure.
+ * @return An IOResult containing a vector with the number of ICU cases for each region, or an 
+ *         error if the function fails.
  */
-template <typename FP = ScalarType>
-IOResult<void> compute_divi_data(const std::vector<DiviEntry>& divi_data, const std::vector<int>& vregion, Date date,
-                                 std::vector<FP>& vnum_icu)
-{
-    auto max_date_entry = std::max_element(divi_data.begin(), divi_data.end(), [](auto&& a, auto&& b) {
-        return a.date < b.date;
-    });
-    if (max_date_entry == divi_data.end()) {
-        log_error("DIVI data is empty.");
-        return failure(StatusCode::InvalidValue, "DIVI data is empty.");
-    }
-    auto max_date = max_date_entry->date;
-    if (max_date < date) {
-        log_error("DIVI data does not contain the specified date.");
-        return failure(StatusCode::OutOfRange, "DIVI data does not contain the specified date.");
-    }
-
-    for (auto&& entry : divi_data) {
-        auto it      = std::find_if(vregion.begin(), vregion.end(), [&entry](auto r) {
-            return r == 0 || r == get_region_id(entry);
-        });
-        auto date_df = entry.date;
-        if (it != vregion.end() && date_df == date) {
-            auto region_idx      = size_t(it - vregion.begin());
-            vnum_icu[region_idx] = entry.num_icu;
-        }
-    }
-
-    return success();
-}
+IOResult<std::vector<ScalarType>> compute_divi_data(const std::vector<DiviEntry>& divi_data, const std::vector<int>& vregion, Date date);
 
 /**
  * @brief Reads DIVI data from a file and computes the ICU data for specified regions and date.
  *
- * @tparam FP Floating point type (default: double).
- *
  * @param[in] path Path to the file containing DIVI data.
  * @param[in] vregion Vector of region IDs for which the data is computed.
  * @param[in] date Date for which the ICU data is computed.
- * @param[in, out] vnum_icu Output vector containing the number of ICU cases for each region.
- *
- * @return An IOResult indicating success or failure.
+ * @return An IOResult containing a vector with the number of ICU cases for each region, or an 
+ *         error if the function fails.
  */
-template <typename FP = ScalarType>
-IOResult<void> read_divi_data(const std::string& path, const std::vector<int>& vregion, Date date,
-                              std::vector<FP>& vnum_icu)
-{
-    BOOST_OUTCOME_TRY(auto&& divi_data, mio::read_divi_data(path));
-    return compute_divi_data(divi_data, vregion, date, vnum_icu);
-}
+IOResult<std::vector<ScalarType>> read_divi_data(const std::string& path, const std::vector<int>& vregion, Date date);
 
 /**
  * @brief Reads population data from a vector of population data entries.
diff --git a/cpp/memilio/mobility/graph.h b/cpp/memilio/mobility/graph.h
index fade2f3a45..5e7f64edd8 100644
--- a/cpp/memilio/mobility/graph.h
+++ b/cpp/memilio/mobility/graph.h
@@ -305,66 +305,45 @@ class Graph
 private:
     std::vector<Node<NodePropertyT>> m_nodes;
     std::vector<Edge<EdgePropertyT>> m_edges;
-}; // namespace mio
+};
 
 /**
- * @brief Sets the graph nodes for counties or districts.
- * Reads the node ids which could refer to districts or counties and the epidemiological
- * data from json files and creates one node for each id. Every node contains a model.
- * @param[in] params Model Parameters that are used for every node.
- * @param[in] start_date Start date for which the data should be read.
- * @param[in] end_data End date for which the data should be read.
- * @param[in] data_dir Directory that contains the data files.
- * @param[in] population_data_path Path to json file containing the population data.
- * @param[in] is_node_for_county Specifies whether the node ids should be county ids (true) or district ids (false).
- * @param[in, out] params_graph Graph whose nodes are set by the function.
- * @param[in] read_func Function that reads input data for german counties and sets Model compartments.
- * @param[in] node_func Function that returns the county ids.
- * @param[in] scaling_factor_inf Factor of confirmed cases to account for undetected cases in each county.
- * @param[in] scaling_factor_icu Factor of ICU cases to account for underreporting.
+ * @brief Set test and trace capacity with uncertainty for the given models.
+ * 
+ * @param[in, out] nodes VectorRange of Node%s each containing a Model in which the data is set.
  * @param[in] tnt_capacity_factor Factor for test and trace capacity.
- * @param[in] num_days Number of days to be simulated; required to load data for vaccinations during the simulation.
- * @param[in] export_time_series If true, reads data for each day of simulation and writes it in the same directory as the input files.
- * @param[in] rki_age_groups Specifies whether rki-age_groups should be used.
  */
-template <typename FP, class TestAndTrace, class ContactPattern, class Model, class MobilityParams, class Parameters,
-          class ReadFunction, class NodeIdFunction>
-IOResult<void> set_nodes(const Parameters& params, Date start_date, Date end_date, const fs::path& data_dir,
-                         const std::string& population_data_path, bool is_node_for_county,
-                         Graph<Model, MobilityParams>& params_graph, ReadFunction&& read_func,
-                         NodeIdFunction&& node_func, const std::vector<FP>& scaling_factor_inf, FP scaling_factor_icu,
-                         FP tnt_capacity_factor, int num_days = 0, bool export_time_series = false,
-                         bool rki_age_groups = true)
-
+template <class Model, class TestAndTrace>
+void set_test_and_trace_capacity(const mio::VectorRange<Node<Model>>& nodes, ScalarType tnt_capacity_factor)
 {
-    BOOST_OUTCOME_TRY(auto&& node_ids, node_func(population_data_path, is_node_for_county, rki_age_groups));
-    std::vector<Model> nodes(node_ids.size(), Model(int(size_t(params.get_num_groups()))));
-    for (auto& node : nodes) {
-        node.parameters = params;
-    }
-
-    BOOST_OUTCOME_TRY(read_func(nodes, start_date, node_ids, scaling_factor_inf, scaling_factor_icu, data_dir.string(),
-                                num_days, export_time_series));
-
     for (size_t node_idx = 0; node_idx < nodes.size(); ++node_idx) {
 
-        auto tnt_capacity = nodes[node_idx].populations.get_total() * tnt_capacity_factor;
+        auto tnt_capacity = nodes[node_idx].property.populations.get_total() * tnt_capacity_factor;
 
-        //local parameters
-        auto& tnt_value = nodes[node_idx].parameters.template get<TestAndTrace>();
-        tnt_value       = UncertainValue<FP>(0.5 * (1.2 * tnt_capacity + 0.8 * tnt_capacity));
+        auto& tnt_value = nodes[node_idx].property.parameters.template get<TestAndTrace>();
+        tnt_value       = UncertainValue<ScalarType>(tnt_capacity);
         tnt_value.set_distribution(mio::ParameterDistributionUniform(0.8 * tnt_capacity, 1.2 * tnt_capacity));
+    }
+}
 
-        auto id = 0;
-        if (is_node_for_county) {
-            id = int(regions::CountyId(node_ids[node_idx]));
-        }
-        else {
-            id = int(regions::DistrictId(node_ids[node_idx]));
-        }
-        //holiday periods
-        auto holiday_periods = regions::get_holidays(regions::get_state_id(id), start_date, end_date);
-        auto& contacts       = nodes[node_idx].parameters.template get<ContactPattern>();
+/**
+ * @brief Set german state holidays for the given nodes.
+ * 
+ * Works for nodes of a graph depicting german counties or states.
+ * 
+ * @param[in, out] nodes VectorRange of Node%s each containing a Model in which the data is set.
+ * @param[in] start_date Date at the beginning of the simulation.
+ * @param[in] end_date Date at the end of the simulation.
+ */
+template <class FP, class Model, class ContactPattern>
+void set_german_holidays(const mio::VectorRange<Node<Model>>& nodes, const mio::Date& start_date,
+                         const mio::Date& end_date)
+{
+    for (size_t node_idx = 0; node_idx < nodes.size(); ++node_idx) {
+        auto state_id        = regions::get_state_id(nodes[node_idx].id);
+        auto holiday_periods = regions::get_holidays(state_id, start_date, end_date);
+
+        auto& contacts = nodes[node_idx].property.parameters.template get<ContactPattern>();
         contacts.get_school_holidays() =
             std::vector<std::pair<mio::SimulationTime<FP>, mio::SimulationTime<FP>>>(holiday_periods.size());
         std::transform(
@@ -372,21 +351,27 @@ IOResult<void> set_nodes(const Parameters& params, Date start_date, Date end_dat
                 return std::make_pair(mio::SimulationTime<FP>(mio::get_offset_in_days(period.first, start_date)),
                                       mio::SimulationTime<FP>(mio::get_offset_in_days(period.second, start_date)));
             });
+    }
+}
 
-        //uncertainty in populations
-        for (auto i = mio::AgeGroup(0); i < params.get_num_groups(); i++) {
+/**
+ * @brief Add uncertainty to the population of the given nodes.
+ * 
+ * @param[in, out] nodes VectorRange of Node%s each containing a Model in which the data is set.
+ */
+template <class Model>
+void set_uncertainty_on_population(const mio::VectorRange<Node<Model>>& nodes)
+{
+    for (size_t node_idx = 0; node_idx < nodes.size(); ++node_idx) {
+        for (auto i = mio::AgeGroup(0); i < nodes[0].property.parameters.get_num_groups(); i++) {
             for (auto j = Index<typename Model::Compartments>(0); j < Model::Compartments::Count; ++j) {
-                auto& compartment_value = nodes[node_idx].populations[{i, j}];
-                compartment_value =
-                    UncertainValue<FP>(0.5 * (1.1 * compartment_value.value() + 0.9 * compartment_value.value()));
+                auto& compartment_value = nodes[node_idx].property.populations[{i, j}];
+                compartment_value       = UncertainValue<ScalarType>(compartment_value.value());
                 compartment_value.set_distribution(mio::ParameterDistributionUniform(0.9 * compartment_value.value(),
                                                                                      1.1 * compartment_value.value()));
             }
         }
-
-        params_graph.add_node(node_ids[node_idx], nodes[node_idx]);
     }
-    return success();
 }
 
 /**
@@ -426,7 +411,7 @@ IOResult<void> set_edges(const fs::path& mobility_data_file, Graph<Model, Mobili
             commuting_weights =
                 (commuting_weights.size() == 0 ? std::vector<FP>(num_age_groups, 1.0) : commuting_weights);
             //commuters
-            auto working_population = 0.0;
+            FP working_population = 0.0;
             for (auto age = AgeGroup(0); age < populations.template size<mio::AgeGroup>(); ++age) {
                 working_population += populations.get_group_total(age) * commuting_weights[size_t(age)];
             }
diff --git a/cpp/models/ode_secir/parameters_io.cpp b/cpp/models/ode_secir/parameters_io.cpp
index 621efbf276..749aa45de4 100644
--- a/cpp/models/ode_secir/parameters_io.cpp
+++ b/cpp/models/ode_secir/parameters_io.cpp
@@ -43,28 +43,22 @@ namespace osecir
 
 namespace details
 {
-//overload for integers, so the comparison of data entry to integers is symmetric (required by e.g. equal_range)
-int get_region_id(int id)
-{
-    return id;
-}
 
-IOResult<void> read_confirmed_cases_data(
-    std::vector<ConfirmedCasesDataEntry>& rki_data, std::vector<int> const& vregion, Date date,
-    std::vector<std::vector<double>>& vnum_Exposed, std::vector<std::vector<double>>& vnum_InfectedNoSymptoms,
-    std::vector<std::vector<double>>& vnum_InfectedSymptoms, std::vector<std::vector<double>>& vnum_InfectedSevere,
-    std::vector<std::vector<double>>& vnum_icu, std::vector<std::vector<double>>& vnum_death,
-    std::vector<std::vector<double>>& vnum_rec, const std::vector<std::vector<int>>& vt_Exposed,
-    const std::vector<std::vector<int>>& vt_InfectedNoSymptoms,
-    const std::vector<std::vector<int>>& vt_InfectedSymptoms, const std::vector<std::vector<int>>& vt_InfectedSevere,
-    const std::vector<std::vector<int>>& vt_InfectedCritical, const std::vector<std::vector<double>>& vmu_C_R,
-    const std::vector<std::vector<double>>& vmu_I_H, const std::vector<std::vector<double>>& vmu_H_U,
-    const std::vector<double>& scaling_factor_inf)
+IOResult<void>
+compute_confirmed_cases_data(std::vector<ConfirmedCasesDataEntry>& case_data, const int region, Date date,
+                             std::vector<ScalarType>& num_Exposed, std::vector<ScalarType>& num_InfectedNoSymptoms,
+                             std::vector<ScalarType>& num_InfectedSymptoms, std::vector<ScalarType>& num_InfectedSevere,
+                             std::vector<ScalarType>& num_icu, std::vector<ScalarType>& num_death,
+                             std::vector<ScalarType>& num_rec, const std::vector<int>& t_Exposed,
+                             const std::vector<int>& t_InfectedNoSymptoms, const std::vector<int>& t_InfectedSymptoms,
+                             const std::vector<int>& t_InfectedSevere, const std::vector<int>& t_InfectedCritical,
+                             const std::vector<ScalarType>& mu_C_R, const std::vector<ScalarType>& mu_I_H,
+                             const std::vector<ScalarType>& mu_H_U, const std::vector<ScalarType>& scaling_factor_inf)
 {
-    auto max_date_entry = std::max_element(rki_data.begin(), rki_data.end(), [](auto&& a, auto&& b) {
+    auto max_date_entry = std::max_element(case_data.begin(), case_data.end(), [](auto&& a, auto&& b) {
         return a.date < b.date;
     });
-    if (max_date_entry == rki_data.end()) {
+    if (max_date_entry == case_data.end()) {
         log_error("RKI data file is empty.");
         return failure(StatusCode::InvalidFileFormat, "RKI file is empty.");
     }
@@ -75,134 +69,379 @@ IOResult<void> read_confirmed_cases_data(
     }
     auto days_surplus = std::min(get_offset_in_days(max_date, date) - 6, 0);
 
-    //this statement causes maybe-uninitialized warning for some versions of gcc.
-    //the error is reported in an included header, so the warning is disabled for the whole file
-    std::sort(rki_data.begin(), rki_data.end(), [](auto&& a, auto&& b) {
-        return std::make_tuple(get_region_id(a), a.date) < std::make_tuple(get_region_id(b), b.date);
-    });
+    for (auto&& entry : case_data) {
 
-    for (auto region_idx = size_t(0); region_idx < vregion.size(); ++region_idx) {
-        auto region_entry_range_it =
-            std::equal_range(rki_data.begin(), rki_data.end(), vregion[region_idx], [](auto&& a, auto&& b) {
-                return get_region_id(a) < get_region_id(b);
-            });
-        auto region_entry_range = make_range(region_entry_range_it);
-        if (region_entry_range.begin() == region_entry_range.end()) {
-            log_error("No entries found for region {}", vregion[region_idx]);
-            return failure(StatusCode::InvalidFileFormat,
-                           "No entries found for region " + std::to_string(vregion[region_idx]));
+        auto date_df = entry.date;
+        auto age     = size_t(entry.age_group);
+
+        if (date_df == offset_date_by_days(date, 0)) {
+            num_InfectedSymptoms[age] += scaling_factor_inf[age] * entry.num_confirmed;
+            // We intentionally do NOT multiply recovered with the scaling_factor_inf here.
+            // If we apply the scaling factor to recovered as well, we would implicitly
+            // assume that this factor holds for the entire historical period up to t0, which is not valid.
+            num_rec[age] += entry.num_confirmed;
         }
-        for (auto&& region_entry : region_entry_range) {
-
-            auto& t_Exposed            = vt_Exposed[region_idx];
-            auto& t_InfectedNoSymptoms = vt_InfectedNoSymptoms[region_idx];
-            auto& t_InfectedSymptoms   = vt_InfectedSymptoms[region_idx];
-            auto& t_InfectedSevere     = vt_InfectedSevere[region_idx];
-            auto& t_InfectedCritical   = vt_InfectedCritical[region_idx];
-
-            auto& num_InfectedNoSymptoms = vnum_InfectedNoSymptoms[region_idx];
-            auto& num_InfectedSymptoms   = vnum_InfectedSymptoms[region_idx];
-            auto& num_rec                = vnum_rec[region_idx];
-            auto& num_Exposed            = vnum_Exposed[region_idx];
-            auto& num_InfectedSevere     = vnum_InfectedSevere[region_idx];
-            auto& num_death              = vnum_death[region_idx];
-            auto& num_icu                = vnum_icu[region_idx];
-
-            auto& mu_C_R = vmu_C_R[region_idx];
-            auto& mu_I_H = vmu_I_H[region_idx];
-            auto& mu_H_U = vmu_H_U[region_idx];
-
-            auto date_df = region_entry.date;
-            auto age     = size_t(region_entry.age_group);
-
-            if (date_df == offset_date_by_days(date, 0)) {
-                num_InfectedSymptoms[age] += scaling_factor_inf[age] * region_entry.num_confirmed;
-                // We intentionally do NOT multiply recovered with the scaling_factor_inf here.
-                // If we apply the scaling factor to recovered as well, we would implicitly
-                // assume that this factor holds for the entire historical period up to t0, which is not valid.
-                num_rec[age] += region_entry.num_confirmed;
-            }
-            if (date_df == offset_date_by_days(date, days_surplus)) {
-                num_InfectedNoSymptoms[age] -=
-                    1 / (1 - mu_C_R[age]) * scaling_factor_inf[age] * region_entry.num_confirmed;
-            }
-            if (date_df == offset_date_by_days(date, t_InfectedNoSymptoms[age] + days_surplus)) {
-                num_InfectedNoSymptoms[age] +=
-                    1 / (1 - mu_C_R[age]) * scaling_factor_inf[age] * region_entry.num_confirmed;
-                num_Exposed[age] -= 1 / (1 - mu_C_R[age]) * scaling_factor_inf[age] * region_entry.num_confirmed;
-            }
-            if (date_df == offset_date_by_days(date, t_Exposed[age] + t_InfectedNoSymptoms[age] + days_surplus)) {
-                num_Exposed[age] += 1 / (1 - mu_C_R[age]) * scaling_factor_inf[age] * region_entry.num_confirmed;
+        if (date_df == offset_date_by_days(date, days_surplus)) {
+            num_InfectedNoSymptoms[age] -= 1 / (1 - mu_C_R[age]) * scaling_factor_inf[age] * entry.num_confirmed;
+        }
+        if (date_df == offset_date_by_days(date, t_InfectedNoSymptoms[age] + days_surplus)) {
+            num_InfectedNoSymptoms[age] += 1 / (1 - mu_C_R[age]) * scaling_factor_inf[age] * entry.num_confirmed;
+            num_Exposed[age] -= 1 / (1 - mu_C_R[age]) * scaling_factor_inf[age] * entry.num_confirmed;
+        }
+        if (date_df == offset_date_by_days(date, t_Exposed[age] + t_InfectedNoSymptoms[age] + days_surplus)) {
+            num_Exposed[age] += 1 / (1 - mu_C_R[age]) * scaling_factor_inf[age] * entry.num_confirmed;
+        }
+        if (date_df == offset_date_by_days(date, -t_InfectedSymptoms[age])) {
+            num_InfectedSymptoms[age] -= scaling_factor_inf[age] * entry.num_confirmed;
+            num_InfectedSevere[age] += mu_I_H[age] * scaling_factor_inf[age] * entry.num_confirmed;
+        }
+        if (date_df == offset_date_by_days(date, -t_InfectedSymptoms[age] - t_InfectedSevere[age])) {
+            num_InfectedSevere[age] -= mu_I_H[age] * scaling_factor_inf[age] * entry.num_confirmed;
+            num_icu[age] += mu_I_H[age] * mu_H_U[age] * scaling_factor_inf[age] * entry.num_confirmed;
+        }
+        if (date_df ==
+            offset_date_by_days(date, -t_InfectedSymptoms[age] - t_InfectedSevere[age] - t_InfectedCritical[age])) {
+            num_death[age] += entry.num_deaths;
+            num_icu[age] -= mu_I_H[age] * mu_H_U[age] * scaling_factor_inf[age] * entry.num_confirmed;
+        }
+    }
+
+    for (size_t i = 0; i < ConfirmedCasesDataEntry::age_group_names.size(); i++) {
+        // R(t0) = ΣC(t0) − I(t0) − H(t0) − U(t0) − D(t0)
+        // subtract currently infectious/hospitalized/ICU/dead
+        // Note: We divide I/H/U/D by scaling_factor_inf to "unscale" these contributions back to the
+        // reported level before subtracting from recovered. If we also applied the scaling factor to
+        // recovered, we would implicitly assume that the same underreporting applies to the entire
+        // history up to t0, which would be wrong. The scaling factor should reflect underreporting
+        // around t0 only.
+        num_rec[i] -= (num_InfectedSymptoms[i] / scaling_factor_inf[i] + num_InfectedSevere[i] / scaling_factor_inf[i] +
+                       num_icu[i] / scaling_factor_inf[i] + num_death[i] / scaling_factor_inf[i]);
+
+        auto try_fix_constraints = [region, i](ScalarType& value, ScalarType error, auto str) {
+            if (value < error) {
+                //this should probably return a failure
+                //but the algorithm is not robust enough to avoid large negative values and there are tests that rely on it
+                log_error("{:s} for age group {:s} is {:.4f} for region {:d}, exceeds expected negative value.", str,
+                          ConfirmedCasesDataEntry::age_group_names[i], value, region);
+                value = 0.0;
             }
-            if (date_df == offset_date_by_days(date, -t_InfectedSymptoms[age])) {
-                num_InfectedSymptoms[age] -= scaling_factor_inf[age] * region_entry.num_confirmed;
-                num_InfectedSevere[age] += mu_I_H[age] * scaling_factor_inf[age] * region_entry.num_confirmed;
+            else if (value < 0) {
+                log_info("{:s} for age group {:s} is {:.4f} for region {:d}, automatically corrected", str,
+                         ConfirmedCasesDataEntry::age_group_names[i], value, region);
+                value = 0.0;
             }
-            if (date_df == offset_date_by_days(date, -t_InfectedSymptoms[age] - t_InfectedSevere[age])) {
-                num_InfectedSevere[age] -= mu_I_H[age] * scaling_factor_inf[age] * region_entry.num_confirmed;
-                num_icu[age] += mu_I_H[age] * mu_H_U[age] * scaling_factor_inf[age] * region_entry.num_confirmed;
+        };
+
+        try_fix_constraints(num_InfectedSymptoms[i], -5, "InfectedSymptoms");
+        try_fix_constraints(num_InfectedNoSymptoms[i], -5, "InfectedNoSymptoms");
+        try_fix_constraints(num_Exposed[i], -5, "Exposed");
+        try_fix_constraints(num_InfectedSevere[i], -5, "InfectedSevere");
+        try_fix_constraints(num_death[i], -5, "Dead");
+        try_fix_constraints(num_icu[i], -5, "InfectedCritical");
+        try_fix_constraints(num_rec[i], -20, "Recovered");
+    }
+
+    return success();
+}
+
+IOResult<void> set_confirmed_cases_data(Model<ScalarType>& model, std::vector<ConfirmedCasesDataEntry>& case_data,
+                                        const int region, Date date, const std::vector<ScalarType>& scaling_factor_inf)
+{
+    const size_t num_age_groups = ConfirmedCasesDataEntry::age_group_names.size();
+    // allow single scalar scaling that is broadcast to all age groups
+    assert(scaling_factor_inf.size() == 1 || scaling_factor_inf.size() == num_age_groups);
+
+    // Set scaling factors to match num age groups
+    std::vector<ScalarType> scaling_factor_inf_full;
+    if (scaling_factor_inf.size() == 1) {
+        scaling_factor_inf_full.assign(num_age_groups, scaling_factor_inf[0]);
+    }
+    else {
+        scaling_factor_inf_full = scaling_factor_inf;
+    }
+
+    std::vector<int> t_InfectedNoSymptoms;
+    std::vector<int> t_Exposed;
+    std::vector<int> t_InfectedSymptoms;
+    std::vector<int> t_InfectedSevere;
+    std::vector<int> t_InfectedCritical;
+
+    std::vector<ScalarType> mu_C_R;
+    std::vector<ScalarType> mu_I_H;
+    std::vector<ScalarType> mu_H_U;
+    std::vector<ScalarType> mu_U_D;
+
+    std::vector<ScalarType> num_InfectedSymptoms(num_age_groups, 0.0);
+    std::vector<ScalarType> num_death(num_age_groups, 0.0);
+    std::vector<ScalarType> num_rec(num_age_groups, 0.0);
+    std::vector<ScalarType> num_Exposed(num_age_groups, 0.0);
+    std::vector<ScalarType> num_InfectedNoSymptoms(num_age_groups, 0.0);
+    std::vector<ScalarType> num_InfectedSevere(num_age_groups, 0.0);
+    std::vector<ScalarType> num_icu(num_age_groups, 0.0);
+
+    const size_t model_groups = (size_t)model.parameters.get_num_groups();
+    assert(model_groups == 1 || model_groups == num_age_groups);
+    for (size_t ag = 0; ag < num_age_groups; ag++) {
+        // If the model has fewer groups than casedata entries available,
+        // reuse group 0 parameters for all RKI age groups
+        const size_t group = (model_groups == num_age_groups) ? ag : 0;
+
+        t_Exposed.push_back(
+            static_cast<int>(std::round(model.parameters.template get<TimeExposed<ScalarType>>()[(AgeGroup)group])));
+        t_InfectedNoSymptoms.push_back(static_cast<int>(
+            std::round(model.parameters.template get<TimeInfectedNoSymptoms<ScalarType>>()[(AgeGroup)group])));
+        t_InfectedSymptoms.push_back(static_cast<int>(
+            std::round(model.parameters.template get<TimeInfectedSymptoms<ScalarType>>()[(AgeGroup)group])));
+        t_InfectedSevere.push_back(static_cast<int>(
+            std::round(model.parameters.template get<TimeInfectedSevere<ScalarType>>()[(AgeGroup)group])));
+        t_InfectedCritical.push_back(static_cast<int>(
+            std::round(model.parameters.template get<TimeInfectedCritical<ScalarType>>()[(AgeGroup)group])));
+
+        mu_C_R.push_back(model.parameters.template get<RecoveredPerInfectedNoSymptoms<ScalarType>>()[(AgeGroup)group]);
+        mu_I_H.push_back(model.parameters.template get<SeverePerInfectedSymptoms<ScalarType>>()[(AgeGroup)group]);
+        mu_H_U.push_back(model.parameters.template get<CriticalPerSevere<ScalarType>>()[(AgeGroup)group]);
+    }
+
+    BOOST_OUTCOME_TRY(compute_confirmed_cases_data(
+        case_data, region, date, num_Exposed, num_InfectedNoSymptoms, num_InfectedSymptoms, num_InfectedSevere, num_icu,
+        num_death, num_rec, t_Exposed, t_InfectedNoSymptoms, t_InfectedSymptoms, t_InfectedSevere, t_InfectedCritical,
+        mu_C_R, mu_I_H, mu_H_U, scaling_factor_inf_full));
+
+    if (model_groups == num_age_groups) {
+        for (size_t i = 0; i < model_groups; i++) {
+            model.populations[{AgeGroup(i), InfectionState::Exposed}]                     = num_Exposed[i];
+            model.populations[{AgeGroup(i), InfectionState::InfectedNoSymptoms}]          = num_InfectedNoSymptoms[i];
+            model.populations[{AgeGroup(i), InfectionState::InfectedNoSymptomsConfirmed}] = 0;
+            model.populations[{AgeGroup(i), InfectionState::InfectedSymptoms}]            = num_InfectedSymptoms[i];
+            model.populations[{AgeGroup(i), InfectionState::InfectedSymptomsConfirmed}]   = 0;
+            model.populations[{AgeGroup(i), InfectionState::InfectedSevere}]              = num_InfectedSevere[i];
+            // Only set the number of ICU patients here, if the date is not available in the data.
+            if (!is_divi_data_available(date)) {
+                model.populations[{AgeGroup(i), InfectionState::InfectedCritical}] = num_icu[i];
             }
-            if (date_df ==
-                offset_date_by_days(date, -t_InfectedSymptoms[age] - t_InfectedSevere[age] - t_InfectedCritical[age])) {
-                num_death[age] += region_entry.num_deaths;
-                num_icu[age] -= mu_I_H[age] * mu_H_U[age] * scaling_factor_inf[age] * region_entry.num_confirmed;
+            model.populations[{AgeGroup(i), InfectionState::Recovered}] = num_rec[i];
+            model.populations[{AgeGroup(i), InfectionState::Dead}]      = num_death[i];
+        }
+    }
+    else {
+        const auto sum_vec = [](const std::vector<ScalarType>& v) {
+            return std::accumulate(v.begin(), v.end(), ScalarType(0.0), [](const ScalarType& a, const ScalarType& b) {
+                return evaluate_intermediate<ScalarType>(a + b);
+            });
+        };
+        model.populations[{AgeGroup(0), InfectionState::Exposed}]                     = sum_vec(num_Exposed);
+        model.populations[{AgeGroup(0), InfectionState::InfectedNoSymptoms}]          = sum_vec(num_InfectedNoSymptoms);
+        model.populations[{AgeGroup(0), InfectionState::InfectedNoSymptomsConfirmed}] = 0;
+        model.populations[{AgeGroup(0), InfectionState::InfectedSymptoms}]            = sum_vec(num_InfectedSymptoms);
+        model.populations[{AgeGroup(0), InfectionState::InfectedSymptomsConfirmed}]   = 0;
+        model.populations[{AgeGroup(0), InfectionState::InfectedSevere}]              = sum_vec(num_InfectedSevere);
+        if (!is_divi_data_available(date)) {
+            model.populations[{AgeGroup(0), InfectionState::InfectedCritical}] = sum_vec(num_icu);
+        }
+        model.populations[{AgeGroup(0), InfectionState::Dead}]      = sum_vec(num_death);
+        model.populations[{AgeGroup(0), InfectionState::Recovered}] = sum_vec(num_rec);
+    }
+    if (std::accumulate(num_InfectedSymptoms.begin(), num_InfectedSymptoms.end(), ScalarType(0.0),
+                        [](const ScalarType& a, const ScalarType& b) {
+                            return evaluate_intermediate<ScalarType>(a + b);
+                        }) == 0.0) {
+        log_warning(
+            "No infections for unvaccinated reported on date {} for region {}. Population data has not been set.", date,
+            region);
+    }
+    return success();
+}
+
+IOResult<void> set_confirmed_cases_data(const mio::VectorRange<Node<Model<ScalarType>>>& model, const std::string& path,
+                                        Date date, const std::vector<ScalarType>& scaling_factor_inf)
+{
+    BOOST_OUTCOME_TRY(auto&& case_data, mio::read_confirmed_cases_data(path));
+
+    // sort case_data into regions and ignore once with no region associated
+    std::vector<std::vector<ConfirmedCasesDataEntry>> vcase_data{model.size()};
+    for (auto&& entry : case_data) {
+        auto it = std::find_if(model.begin(), model.end(), [&entry](auto m) {
+            return m.id == 0 || get_region_id(entry) == m.id;
+        });
+        if (it != model.end()) {
+            auto region_idx = size_t(it - model.begin());
+            vcase_data[region_idx].push_back(entry);
+        }
+    }
+
+    for (size_t region_idx = 0; region_idx < model.size(); ++region_idx) {
+        BOOST_OUTCOME_TRY(set_confirmed_cases_data(model[region_idx].property, vcase_data[region_idx],
+                                                   model[region_idx].id, date, scaling_factor_inf));
+    }
+    return success();
+}
+
+IOResult<void> set_population_data(Model<ScalarType>& model, const std::vector<ScalarType>& num_population,
+                                   const int region)
+{
+    if (std::accumulate(num_population.begin(), num_population.end(), ScalarType(0.0),
+                        [](const ScalarType& a, const ScalarType& b) {
+                            return evaluate_intermediate<ScalarType>(a + b);
+                        }) <= 0) {
+        log_warning("No population data available for region " + std::to_string(region) +
+                    ". Population data has not been set.");
+        return success();
+    }
+
+    auto num_groups  = model.parameters.get_num_groups();
+    auto data_groups = num_population.size();
+
+    if (data_groups == (size_t)num_groups) {
+        for (auto i = AgeGroup(0); i < num_groups; i++) {
+            model.populations.template set_difference_from_group_total<AgeGroup>({i, InfectionState::Susceptible},
+                                                                                 num_population[(size_t)i]);
+        }
+    }
+    else if ((size_t)num_groups == 1 && data_groups >= 1) {
+        const ScalarType total = std::accumulate(num_population.begin(), num_population.end(), ScalarType(0.0),
+                                                 [](const ScalarType& a, const ScalarType& b) {
+                                                     return evaluate_intermediate<ScalarType>(a + b);
+                                                 });
+        model.populations.template set_difference_from_group_total<AgeGroup>({AgeGroup(0), InfectionState::Susceptible},
+                                                                             total);
+    }
+
+    for (auto i = AgeGroup(0); i < num_groups; i++) {
+        for (auto j = Index<InfectionState>(0); j < InfectionState::Count; ++j) {
+            if (model.populations[{i, j}] < 0) {
+                log_warning("Compartment at age group {}, infection state {}, is negative: {}", size_t(i), size_t(j),
+                            model.populations[{i, j}]);
             }
         }
     }
+    return success();
+}
+
+IOResult<void> set_population_data(const mio::VectorRange<Node<Model<ScalarType>>>& model, const std::string& path)
+{
+    std::vector<int> vregion;
+    std::transform(model.begin(), model.end(), std::back_inserter(vregion), [](const auto& m) {
+        return m.id;
+    });
+    BOOST_OUTCOME_TRY(const auto&& num_population, read_population_data(path, vregion));
+
+    for (size_t region_idx = 0; region_idx < model.size(); ++region_idx) {
+        BOOST_OUTCOME_TRY(
+            set_population_data(model[region_idx].property, num_population[region_idx], model[region_idx].id));
+    }
+    return success();
+}
+
+IOResult<void> set_divi_data(Model<ScalarType>& model, const ScalarType num_icu, ScalarType scaling_factor_icu)
+{
+    ScalarType sum_mu_I_U = 0;
+    std::vector<ScalarType> mu_I_U;
+    auto num_groups = model.parameters.get_num_groups();
+    for (auto i = AgeGroup(0); i < num_groups; i++) {
+        sum_mu_I_U += model.parameters.template get<CriticalPerSevere<ScalarType>>()[i] *
+                      model.parameters.template get<SeverePerInfectedSymptoms<ScalarType>>()[i];
+        mu_I_U.push_back(model.parameters.template get<CriticalPerSevere<ScalarType>>()[i] *
+                         model.parameters.template get<SeverePerInfectedSymptoms<ScalarType>>()[i]);
+    }
+
+    for (auto i = AgeGroup(0); i < num_groups; i++) {
+        model.populations[{i, InfectionState::InfectedCritical}] =
+            scaling_factor_icu * num_icu * mu_I_U[(size_t)i] / sum_mu_I_U;
+    }
+
+    return success();
+}
+
+IOResult<void> set_divi_data(const mio::VectorRange<Node<Model<ScalarType>>>& model, const std::string& path, Date date,
+                             ScalarType scaling_factor_icu)
+{
+    // DIVI dataset will no longer be updated from CW29 2024 on.
+    if (!is_divi_data_available(date)) {
+        log_warning("No DIVI data available for date: {}. "
+                    "ICU compartment will be set based on Case data.",
+                    date);
+        return success();
+    }
+
+    std::vector<int> vregion;
+    std::transform(model.begin(), model.end(), std::back_inserter(vregion), [](const auto& m) {
+        return m.id;
+    });
+    BOOST_OUTCOME_TRY(auto&& num_icu, read_divi_data(path, vregion, date));
+
+    for (size_t region_idx = 0; region_idx < model.size(); ++region_idx) {
+        BOOST_OUTCOME_TRY(set_divi_data(model[region_idx].property, num_icu[region_idx], scaling_factor_icu));
+    }
+
+    return success();
+}
+
+} //namespace details
+
+IOResult<void> read_input_data(const mio::VectorRange<Node<Model<ScalarType>>>& model, Date date,
+                               const std::vector<ScalarType>& scaling_factor_inf, ScalarType scaling_factor_icu,
+                               const mio::regions::de::EpidataFilenames& epidata_filenames)
+{
+    BOOST_OUTCOME_TRY(details::set_divi_data(model, epidata_filenames.divi_data_path, date, scaling_factor_icu));
+
+    BOOST_OUTCOME_TRY(
+        details::set_confirmed_cases_data(model, epidata_filenames.case_data_path, date, scaling_factor_inf));
+    BOOST_OUTCOME_TRY(details::set_population_data(model, epidata_filenames.population_data_path));
+    return success();
+}
 
-    for (size_t region_idx = 0; region_idx < vregion.size(); ++region_idx) {
-        auto region = vregion[region_idx];
-
-        auto& num_InfectedNoSymptoms = vnum_InfectedNoSymptoms[region_idx];
-        auto& num_InfectedSymptoms   = vnum_InfectedSymptoms[region_idx];
-        auto& num_rec                = vnum_rec[region_idx];
-        auto& num_Exposed            = vnum_Exposed[region_idx];
-        auto& num_InfectedSevere     = vnum_InfectedSevere[region_idx];
-        auto& num_death              = vnum_death[region_idx];
-        auto& num_icu                = vnum_icu[region_idx];
-
-        for (size_t i = 0; i < ConfirmedCasesDataEntry::age_group_names.size(); i++) {
-            // R(t0) = ΣC(t0) − I(t0) − H(t0) − U(t0) − D(t0)
-            // subtract currently infectious/hospitalized/ICU/dead
-            // Note: We divide I/H/U/D by scaling_factor_inf to "unscale" these contributions back to the
-            // reported level before subtracting from recovered. If we also applied the scaling factor to
-            // recovered, we would implicitly assume that the same underreporting applies to the entire
-            // history up to t0, which would be wrong. The scaling factor should reflect underreporting
-            // around t0 only.
-            num_rec[i] -=
-                (num_InfectedSymptoms[i] / scaling_factor_inf[i] + num_InfectedSevere[i] / scaling_factor_inf[i] +
-                 num_icu[i] / scaling_factor_inf[i] + num_death[i] / scaling_factor_inf[i]);
-
-            auto try_fix_constraints = [region, i](double& value, double error, auto str) {
-                if (value < error) {
-                    //this should probably return a failure
-                    //but the algorithm is not robust enough to avoid large negative values and there are tests that rely on it
-                    log_error("{:s} for age group {:s} is {:.4f} for region {:d}, exceeds expected negative value.",
-                              str, ConfirmedCasesDataEntry::age_group_names[i], value, region);
-                    value = 0.0;
-                }
-                else if (value < 0) {
-                    log_info("{:s} for age group {:s} is {:.4f} for region {:d}, automatically corrected", str,
-                             ConfirmedCasesDataEntry::age_group_names[i], value, region);
-                    value = 0.0;
-                }
-            };
-
-            try_fix_constraints(num_InfectedSymptoms[i], -5, "InfectedSymptoms");
-            try_fix_constraints(num_InfectedNoSymptoms[i], -5, "InfectedNoSymptoms");
-            try_fix_constraints(num_Exposed[i], -5, "Exposed");
-            try_fix_constraints(num_InfectedSevere[i], -5, "InfectedSevere");
-            try_fix_constraints(num_death[i], -5, "Dead");
-            try_fix_constraints(num_icu[i], -5, "InfectedCritical");
-            try_fix_constraints(num_rec[i], -20, "Recovered");
+#ifdef MEMILIO_HAS_HDF5
+
+IOResult<void> export_input_data_timeseries(const mio::VectorRange<Node<Model<ScalarType>>> model,
+                                            const std::string& results_dir, Date date,
+                                            const std::vector<ScalarType>& scaling_factor_inf,
+                                            ScalarType scaling_factor_icu, int num_days,
+                                            const mio::regions::de::EpidataFilenames& epidata_filenames)
+{
+    const auto num_age_groups = (size_t)model[0].property.parameters.get_num_groups();
+    // allow scalar scaling factor as convenience for 1-group models
+    assert(scaling_factor_inf.size() == 1 || scaling_factor_inf.size() == num_age_groups);
+
+    std::vector<TimeSeries<ScalarType>> extrapolated_data(
+        model.size(), TimeSeries<ScalarType>::zero(num_days + 1, (size_t)InfectionState::Count * num_age_groups));
+
+    for (int t = 0; t <= num_days; ++t) {
+        auto offset_day = offset_date_by_days(date, t);
+
+        BOOST_OUTCOME_TRY(
+            read_input_data(model, offset_day, scaling_factor_inf, scaling_factor_icu, epidata_filenames));
+
+        for (size_t r = 0; r < model.size(); r++) {
+            extrapolated_data[r][t] = model[r].property.get_initial_values();
         }
     }
 
+    std::vector<int> vregion;
+    std::transform(model.begin(), model.end(), std::back_inserter(vregion), [](const auto& m) {
+        return m.id;
+    });
+    BOOST_OUTCOME_TRY(save_result(extrapolated_data, vregion, static_cast<int>(num_age_groups),
+                                  path_join(results_dir, "Results_rki.h5")));
+
+    auto extrapolated_rki_data_sum = sum_nodes(std::vector<std::vector<TimeSeries<ScalarType>>>{extrapolated_data});
+    BOOST_OUTCOME_TRY(save_result({extrapolated_rki_data_sum[0][0]}, {0}, static_cast<int>(num_age_groups),
+                                  path_join(results_dir, "Results_rki_sum.h5")));
+
+    return success();
+}
+#else
+IOResult<void> export_input_data_county_timeseries(const mio::VectorRange<Node<Model<ScalarType>>>, const std::string&,
+                                                   Date, const std::vector<int>&, const std::vector<ScalarType>&,
+                                                   const ScalarType, const int,
+                                                   const mio::regions::de::EpidataFilenames&)
+{
+    mio::log_warning("HDF5 not available. Cannot export time series of extrapolated real data.");
     return success();
 }
+#endif // MEMILIO_HAS_HDF5
 
-} // namespace details
 } // namespace osecir
+
 } // namespace mio
 
 #endif // MEMILIO_HAS_JSONCPP
diff --git a/cpp/models/ode_secir/parameters_io.h b/cpp/models/ode_secir/parameters_io.h
index 0505d45563..11c7b5e8d1 100644
--- a/cpp/models/ode_secir/parameters_io.h
+++ b/cpp/models/ode_secir/parameters_io.h
@@ -26,6 +26,8 @@
 #ifdef MEMILIO_HAS_JSONCPP
 
 #include "ode_secir/model.h"
+#include "memilio/geography/regions.h"
+#include "memilio/mobility/graph.h"
 #include "memilio/io/epi_data.h"
 #include "memilio/io/parameters_io.h"
 #include "memilio/io/result_io.h"
@@ -39,467 +41,142 @@ namespace osecir
 
 namespace details
 {
-/**
-     * @brief reads populations data from RKI.
-     * @param[in] rki_data Vector of ConfirmedCasesDataEntry%s.
-     * @param[in] vregion Vector of keys of the region of interest.
-     * @param[in] date Date at which the data is read.
-     * @param[in, out] vnum_* Output vector for number of people in the corresponding compartement.
-     * @param[in] vt_* vector Average time it takes to get from one compartement to another for each age group.
-     * @param[in] vmu_* vector Probabilities to get from one compartement to another for each age group.
-     * @param[in] scaling_factor_inf Factors by which to scale the confirmed cases of rki data.
-     */
-IOResult<void> read_confirmed_cases_data(
-    std::vector<ConfirmedCasesDataEntry>& rki_data, std::vector<int> const& vregion, Date date,
-    std::vector<std::vector<double>>& vnum_Exposed, std::vector<std::vector<double>>& vnum_InfectedNoSymptoms,
-    std::vector<std::vector<double>>& vnum_InfectedSymptoms, std::vector<std::vector<double>>& vnum_InfectedSevere,
-    std::vector<std::vector<double>>& vnum_icu, std::vector<std::vector<double>>& vnum_death,
-    std::vector<std::vector<double>>& vnum_rec, const std::vector<std::vector<int>>& vt_Exposed,
-    const std::vector<std::vector<int>>& vt_InfectedNoSymptoms,
-    const std::vector<std::vector<int>>& vt_InfectedSymptoms, const std::vector<std::vector<int>>& vt_InfectedSevere,
-    const std::vector<std::vector<int>>& vt_InfectedCritical, const std::vector<std::vector<double>>& vmu_C_R,
-    const std::vector<std::vector<double>>& vmu_I_H, const std::vector<std::vector<double>>& vmu_H_U,
-    const std::vector<double>& scaling_factor_inf);
 
 /**
- * @brief Sets populations data from already read case data with multiple age groups into a Model with one age group.
- * @tparam FP Floating point data type, e.g., double.
- * @param[in, out] model Vector of models in which the data is set.
- * @param[in] case_data List of confirmed cases data entries.
- * @param[in] region Vector of keys of the region of interest.
+ * @brief Reads populations data from rki data.
+ * @param[in] case_data Vector of ConfirmedCasesDataEntry%s.
+ * @param[in] region Key of the region of interest.
  * @param[in] date Date at which the data is read.
+ * @param[in, out] num_* Output vector for number of people in the corresponding compartement.
+ * @param[in] t_* vector Average time it takes to get from one compartement to another for each age group.
+ * @param[in] mu_* vector Probabilities to get from one compartement to another for each age group.
  * @param[in] scaling_factor_inf Factors by which to scale the confirmed cases of rki data.
  */
-template <typename FP>
-IOResult<void> set_confirmed_cases_data(std::vector<Model<FP>>& model, std::vector<ConfirmedCasesDataEntry>& case_data,
-                                        const std::vector<int>& region, Date date,
-                                        const std::vector<double>& scaling_factor_inf)
-{
-    const size_t num_age_groups = ConfirmedCasesDataEntry::age_group_names.size();
-    // allow single scalar scaling that is broadcast to all age groups
-    assert(scaling_factor_inf.size() == 1 || scaling_factor_inf.size() == num_age_groups);
-
-    // Set scaling factors to match num age groups
-    std::vector<double> scaling_factor_inf_full;
-    if (scaling_factor_inf.size() == 1) {
-        scaling_factor_inf_full.assign(num_age_groups, scaling_factor_inf[0]);
-    }
-    else {
-        scaling_factor_inf_full = scaling_factor_inf;
-    }
-
-    std::vector<std::vector<int>> t_InfectedNoSymptoms{model.size()};
-    std::vector<std::vector<int>> t_Exposed{model.size()};
-    std::vector<std::vector<int>> t_InfectedSymptoms{model.size()};
-    std::vector<std::vector<int>> t_InfectedSevere{model.size()};
-    std::vector<std::vector<int>> t_InfectedCritical{model.size()};
-
-    std::vector<std::vector<double>> mu_C_R{model.size()};
-    std::vector<std::vector<double>> mu_I_H{model.size()};
-    std::vector<std::vector<double>> mu_H_U{model.size()};
-    std::vector<std::vector<double>> mu_U_D{model.size()};
-
-    for (size_t node = 0; node < model.size(); ++node) {
-        const size_t model_groups = (size_t)model[node].parameters.get_num_groups();
-        assert(model_groups == 1 || model_groups == num_age_groups);
-        for (size_t ag = 0; ag < num_age_groups; ag++) {
-            // If the model has fewer groups than casedata entries available,
-            // reuse group 0 parameters for all RKI age groups
-            const size_t group = (model_groups == num_age_groups) ? ag : 0;
-
-            t_Exposed[node].push_back(
-                static_cast<int>(std::round(model[node].parameters.template get<TimeExposed<FP>>()[(AgeGroup)group])));
-            t_InfectedNoSymptoms[node].push_back(static_cast<int>(
-                std::round(model[node].parameters.template get<TimeInfectedNoSymptoms<FP>>()[(AgeGroup)group])));
-            t_InfectedSymptoms[node].push_back(static_cast<int>(
-                std::round(model[node].parameters.template get<TimeInfectedSymptoms<FP>>()[(AgeGroup)group])));
-            t_InfectedSevere[node].push_back(static_cast<int>(
-                std::round(model[node].parameters.template get<TimeInfectedSevere<FP>>()[(AgeGroup)group])));
-            t_InfectedCritical[node].push_back(static_cast<int>(
-                std::round(model[node].parameters.template get<TimeInfectedCritical<FP>>()[(AgeGroup)group])));
-
-            mu_C_R[node].push_back(
-                model[node].parameters.template get<RecoveredPerInfectedNoSymptoms<FP>>()[(AgeGroup)group]);
-            mu_I_H[node].push_back(
-                model[node].parameters.template get<SeverePerInfectedSymptoms<FP>>()[(AgeGroup)group]);
-            mu_H_U[node].push_back(model[node].parameters.template get<CriticalPerSevere<FP>>()[(AgeGroup)group]);
-            mu_U_D[node].push_back(model[node].parameters.template get<DeathsPerCritical<FP>>()[(AgeGroup)group]);
-        }
-    }
-    std::vector<std::vector<double>> num_InfectedSymptoms(model.size(), std::vector<double>(num_age_groups, 0.0));
-    std::vector<std::vector<double>> num_death(model.size(), std::vector<double>(num_age_groups, 0.0));
-    std::vector<std::vector<double>> num_rec(model.size(), std::vector<double>(num_age_groups, 0.0));
-    std::vector<std::vector<double>> num_Exposed(model.size(), std::vector<double>(num_age_groups, 0.0));
-    std::vector<std::vector<double>> num_InfectedNoSymptoms(model.size(), std::vector<double>(num_age_groups, 0.0));
-    std::vector<std::vector<double>> num_InfectedSevere(model.size(), std::vector<double>(num_age_groups, 0.0));
-    std::vector<std::vector<double>> num_icu(model.size(), std::vector<double>(num_age_groups, 0.0));
-
-    BOOST_OUTCOME_TRY(read_confirmed_cases_data(case_data, region, date, num_Exposed, num_InfectedNoSymptoms,
-                                                num_InfectedSymptoms, num_InfectedSevere, num_icu, num_death, num_rec,
-                                                t_Exposed, t_InfectedNoSymptoms, t_InfectedSymptoms, t_InfectedSevere,
-                                                t_InfectedCritical, mu_C_R, mu_I_H, mu_H_U, scaling_factor_inf_full));
-
-    for (size_t node = 0; node < model.size(); node++) {
-
-        if (std::accumulate(num_InfectedSymptoms[node].begin(), num_InfectedSymptoms[node].end(), FP(0.0),
-                            [](const FP& a, const FP& b) {
-                                return evaluate_intermediate<FP>(a + b);
-                            }) > 0.0) {
-            size_t num_groups = (size_t)model[node].parameters.get_num_groups();
-            if (num_groups == num_age_groups) {
-                for (size_t i = 0; i < num_groups; i++) {
-                    model[node].populations[{AgeGroup(i), InfectionState::Exposed}] = num_Exposed[node][i];
-                    model[node].populations[{AgeGroup(i), InfectionState::InfectedNoSymptoms}] =
-                        num_InfectedNoSymptoms[node][i];
-                    model[node].populations[{AgeGroup(i), InfectionState::InfectedNoSymptomsConfirmed}] = 0;
-                    model[node].populations[{AgeGroup(i), InfectionState::InfectedSymptoms}] =
-                        num_InfectedSymptoms[node][i];
-                    model[node].populations[{AgeGroup(i), InfectionState::InfectedSymptomsConfirmed}] = 0;
-                    model[node].populations[{AgeGroup(i), InfectionState::InfectedSevere}] =
-                        num_InfectedSevere[node][i];
-                    // Only set the number of ICU patients here, if the date is not available in the data.
-                    if (!is_divi_data_available(date)) {
-                        model[node].populations[{AgeGroup(i), InfectionState::InfectedCritical}] = num_icu[node][i];
-                    }
-                    model[node].populations[{AgeGroup(i), InfectionState::Dead}]      = num_death[node][i];
-                    model[node].populations[{AgeGroup(i), InfectionState::Recovered}] = num_rec[node][i];
-                }
-            }
-            else {
-                const auto sum_vec = [](const std::vector<FP>& v) {
-                    return std::accumulate(v.begin(), v.end(), FP(0.0), [](const FP& a, const FP& b) {
-                        return evaluate_intermediate<FP>(a + b);
-                    });
-                };
-                const size_t i0                                                  = 0;
-                model[node].populations[{AgeGroup(i0), InfectionState::Exposed}] = sum_vec(num_Exposed[node]);
-                model[node].populations[{AgeGroup(i0), InfectionState::InfectedNoSymptoms}] =
-                    sum_vec(num_InfectedNoSymptoms[node]);
-                model[node].populations[{AgeGroup(i0), InfectionState::InfectedNoSymptomsConfirmed}] = 0;
-                model[node].populations[{AgeGroup(i0), InfectionState::InfectedSymptoms}] =
-                    sum_vec(num_InfectedSymptoms[node]);
-                model[node].populations[{AgeGroup(i0), InfectionState::InfectedSymptomsConfirmed}] = 0;
-                model[node].populations[{AgeGroup(i0), InfectionState::InfectedSevere}] =
-                    sum_vec(num_InfectedSevere[node]);
-                if (!is_divi_data_available(date)) {
-                    model[node].populations[{AgeGroup(i0), InfectionState::InfectedCritical}] = sum_vec(num_icu[node]);
-                }
-                model[node].populations[{AgeGroup(i0), InfectionState::Dead}]      = sum_vec(num_death[node]);
-                model[node].populations[{AgeGroup(i0), InfectionState::Recovered}] = sum_vec(num_rec[node]);
-            }
-        }
-        else {
-            log_warning("No infections reported on date {} for region {}. Population data has not been set.", date,
-                        region[node]);
-        }
-    }
-    return success();
-}
+IOResult<void>
+compute_confirmed_cases_data(std::vector<ConfirmedCasesDataEntry>& case_data, const int region, Date date,
+                             std::vector<ScalarType>& num_Exposed, std::vector<ScalarType>& num_InfectedNoSymptoms,
+                             std::vector<ScalarType>& num_InfectedSymptoms, std::vector<ScalarType>& num_InfectedSevere,
+                             std::vector<ScalarType>& num_icu, std::vector<ScalarType>& num_death,
+                             std::vector<ScalarType>& num_rec, const std::vector<int>& t_Exposed,
+                             const std::vector<int>& t_InfectedNoSymptoms, const std::vector<int>& t_InfectedSymptoms,
+                             const std::vector<int>& t_InfectedSevere, const std::vector<int>& t_InfectedCritical,
+                             const std::vector<ScalarType>& mu_C_R, const std::vector<ScalarType>& mu_I_H,
+                             const std::vector<ScalarType>& mu_H_U, const std::vector<ScalarType>& scaling_factor_inf);
 
 /**
- * @brief Sets the infected population for a given model based on confirmed cases data. Here, we
- * read the case data from a file.
- * @tparam FP Floating point data type, e.g., double.
- * @param[in, out] Vector of models for which the confirmed cases data will be set.
- * @param[in] path Path to the confirmed cases data file.
- * @param[in] region Vector of keys of the region of interest.
+ * @brief Sets populations data from already read case data with multiple age groups into a Model.
+ * @param[in, out] model Model in which the data is set.
+ * @param[in] case_data Vector of ConfirmedCasesDataEntry%s.
+ * @param[in] region Key of the region of interest.
  * @param[in] date Date at which the data is read.
  * @param[in] scaling_factor_inf Factors by which to scale the confirmed cases of rki data.
  */
-template <typename FP>
-IOResult<void> set_confirmed_cases_data(std::vector<Model<FP>>& model, const std::string& path,
-                                        std::vector<int> const& region, Date date,
-                                        const std::vector<double>& scaling_factor_inf)
-{
-    BOOST_OUTCOME_TRY(auto&& case_data, mio::read_confirmed_cases_data(path));
-    BOOST_OUTCOME_TRY(set_confirmed_cases_data(model, case_data, region, date, scaling_factor_inf));
-    return success();
-}
+IOResult<void> set_confirmed_cases_data(Model<ScalarType>& model, std::vector<ConfirmedCasesDataEntry>& case_data,
+                                        const int region, Date date, const std::vector<ScalarType>& scaling_factor_inf);
 
 /**
-     * @brief Sets populations data from DIVI register into Model.
-     * @tparam FP floating point data type, e.g., double.
-     * @param[in, out] model Vector of models in which the data is set.
-     * @param[in] path Path to DIVI file.
-     * @param[in] vregion Vector of keys of the regions of interest.
-     * @param[in] date Date for which the arrays are initialized.
-     * @param[in] scaling_factor_icu factor by which to scale the icu cases of divi data.
-     */
-template <typename FP>
-IOResult<void> set_divi_data(std::vector<Model<FP>>& model, const std::string& path, const std::vector<int>& vregion,
-                             Date date, double scaling_factor_icu)
-{
-    // DIVI dataset will no longer be updated from CW29 2024 on.
-    if (!is_divi_data_available(date)) {
-        log_warning("No DIVI data available for date: {}. "
-                    "ICU compartment will be set based on Case data.",
-                    date);
-        return success();
-    }
-    std::vector<double> sum_mu_I_U(vregion.size(), 0);
-    std::vector<std::vector<double>> mu_I_U{model.size()};
-    for (size_t region = 0; region < vregion.size(); region++) {
-        auto num_groups = model[region].parameters.get_num_groups();
-        for (auto i = AgeGroup(0); i < num_groups; i++) {
-            sum_mu_I_U[region] += model[region].parameters.template get<CriticalPerSevere<FP>>()[i] *
-                                  model[region].parameters.template get<SeverePerInfectedSymptoms<FP>>()[i];
-            mu_I_U[region].push_back(model[region].parameters.template get<CriticalPerSevere<FP>>()[i] *
-                                     model[region].parameters.template get<SeverePerInfectedSymptoms<FP>>()[i]);
-        }
-    }
-    std::vector<double> num_icu(model.size(), 0.0);
-    BOOST_OUTCOME_TRY(read_divi_data(path, vregion, date, num_icu));
-
-    for (size_t region = 0; region < vregion.size(); region++) {
-        auto num_groups = model[region].parameters.get_num_groups();
-        for (auto i = AgeGroup(0); i < num_groups; i++) {
-            model[region].populations[{i, InfectionState::InfectedCritical}] =
-                scaling_factor_icu * num_icu[region] * mu_I_U[region][(size_t)i] / sum_mu_I_U[region];
-        }
-    }
-
-    return success();
-}
-
-/**
-* @brief Sets population data from census data which has been read into num_population.
-* @tparam FP floating point data type, e.g., double.
-* @param[in, out] model Vector of models in which the data is set. There should be one model per region.
-* @param[in] num_population Vector of population data. The size should be the same as vregion and model.
-* @param[in] vregion Vector of keys of the regions of interest.
-*/
-template <typename FP>
-IOResult<void> set_population_data(std::vector<Model<FP>>& model, const std::vector<std::vector<FP>>& num_population,
-                                   const std::vector<int>& vregion)
-{
-    assert(num_population.size() == vregion.size());
-    assert(model.size() == vregion.size());
-    for (size_t region = 0; region < vregion.size(); region++) {
-        const auto model_groups = (size_t)model[region].parameters.get_num_groups();
-        const auto data_groups  = num_population[region].size();
-        assert(data_groups == model_groups || (model_groups == 1 && data_groups >= 1));
-
-        if (data_groups == model_groups) {
-            for (auto i = AgeGroup(0); i < model[region].parameters.get_num_groups(); i++) {
-                model[region].populations.template set_difference_from_group_total<AgeGroup>(
-                    {i, InfectionState::Susceptible}, num_population[region][(size_t)i]);
-            }
-        }
-        else if (model_groups == 1 && data_groups >= 1) {
-            const FP total = std::accumulate(num_population[region].begin(), num_population[region].end(), FP(0.0),
-                                             [](const FP& a, const FP& b) {
-                                                 return evaluate_intermediate<FP>(a + b);
-                                             });
-            model[region].populations.template set_difference_from_group_total<AgeGroup>(
-                {AgeGroup(0), InfectionState::Susceptible}, total);
-        }
-    }
-    return success();
-}
+ * @brief Sets the infected populations for the given models based on confirmed cases data. 
+ * Reads the case data from a file and then calls a subfunction that sets the infected population 
+ * for each model.
+ * @param[in, out] model VectorRange of Node%s each containing a Model for which the confirmed cases data will be set.
+ * @param[in] path Path to the confirmed cases data file.
+ * @param[in] date Date at which the data is read.
+ * @param[in] scaling_factor_inf Factors by which to scale the confirmed cases of rki data.
+ */
+IOResult<void> set_confirmed_cases_data(const mio::VectorRange<Node<Model<ScalarType>>>& model, const std::string& path,
+                                        Date date, const std::vector<ScalarType>& scaling_factor_inf);
 
 /**
-* @brief Sets population data from census data into a Model.
-* @tparam FP Floating point data type, e.g., double.
-* @param[in, out] model Vector of models in which the data is set.
-* @param[in] path Path to RKI file containing population data.
-* @param[in] vregion Vector of keys of the regions of interest.
-*/
-template <typename FP>
-IOResult<void> set_population_data(std::vector<Model<FP>>& model, const std::string& path,
-                                   const std::vector<int>& vregion)
-{
-    BOOST_OUTCOME_TRY(const auto&& num_population, read_population_data(path, vregion));
-    BOOST_OUTCOME_TRY(set_population_data(model, num_population, vregion));
-    return success();
-}
-
-} //namespace details
-
-#ifdef MEMILIO_HAS_HDF5
+ * @brief Sets population data from census data which has been read into num_population.
+ * @param[in, out] model Model in which the data is set.
+ * @param[in] num_population Vector of population data for the region of interest.
+ * @param[in] region Key of the region of interest.
+ */
+IOResult<void> set_population_data(Model<ScalarType>& model, const std::vector<ScalarType>& num_population,
+                                   const int region);
 
 /**
-* @brief Uses the initialisation method, which uses the reported data to set the initial conditions for the model for a given day. 
-* The initialisation is applied for a predefined number of days and finally saved in a timeseries for each region. In the end,
-* we save the files "Results_rki.h5" and "Results_rki_sum.h5" in the results_dir.
-* Results_rki.h5 contains a time series for each region and Results_rki_sum.h5 contains the sum of all regions.
-* @param[in] models Vector of models in which the data is set. Copy is made to avoid changing the original model.
-* @param[in] results_dir Path to result files.
-* @param[in] region Vector of keys of the region of interest.
-* @param[in] date Date for which the data should be read.
-* @param[in] scaling_factor_inf Factors by which to scale the confirmed cases of rki data.
-* @param[in] scaling_factor_icu Factor by which to scale the icu cases of divi data.
-* @param[in] num_days Number of days to be simulated/initialized.
-* @param[in] divi_data_path Path to DIVI file.
-* @param[in] confirmed_cases_path Path to confirmed cases file.
-* @param[in] population_data_path Path to population data file.
-*/
-template <class Model>
-IOResult<void> export_input_data_county_timeseries(
-    std::vector<Model> models, const std::string& results_dir, std::vector<int> const& region, Date date,
-    const std::vector<double>& scaling_factor_inf, double scaling_factor_icu, int num_days,
-    const std::string& divi_data_path, const std::string& confirmed_cases_path, const std::string& population_data_path)
-{
-    const auto num_age_groups = (size_t)models[0].parameters.get_num_groups();
-    // allow scalar scaling factor as convenience for 1-group models
-    assert(scaling_factor_inf.size() == 1 || scaling_factor_inf.size() == num_age_groups);
-    assert(models.size() == region.size());
-    std::vector<TimeSeries<double>> extrapolated_data(
-        region.size(), TimeSeries<double>::zero(num_days + 1, (size_t)InfectionState::Count * num_age_groups));
-
-    BOOST_OUTCOME_TRY(auto&& num_population, mio::read_population_data(population_data_path, region));
-    BOOST_OUTCOME_TRY(auto&& case_data, mio::read_confirmed_cases_data(confirmed_cases_path));
-
-    for (int t = 0; t <= num_days; ++t) {
-        auto offset_day = offset_date_by_days(date, t);
-
-        BOOST_OUTCOME_TRY(details::set_divi_data(models, divi_data_path, region, offset_day, scaling_factor_icu));
-        BOOST_OUTCOME_TRY(details::set_confirmed_cases_data(models, case_data, region, offset_day, scaling_factor_inf));
-        BOOST_OUTCOME_TRY(details::set_population_data(models, num_population, region));
-        for (size_t r = 0; r < region.size(); r++) {
-            extrapolated_data[r][t] = models[r].get_initial_values();
-        }
-    }
-
-    BOOST_OUTCOME_TRY(
-        save_result(extrapolated_data, region, (int)num_age_groups, path_join(results_dir, "Results_rki.h5")));
-
-    auto rki_data_sum = sum_nodes(std::vector<std::vector<TimeSeries<double>>>{extrapolated_data});
-    BOOST_OUTCOME_TRY(
-        save_result({rki_data_sum[0][0]}, {0}, (int)num_age_groups, path_join(results_dir, "Results_rki_sum.h5")));
-
-    return success();
-}
-#else
-template <class Model>
-IOResult<void> export_input_data_county_timeseries(
-    std::vector<Model> models, const std::string& results_dir, std::vector<int> const& region, Date date,
-    const std::vector<double>& scaling_factor_inf, double scaling_factor_icu, int num_days,
-    const std::string& divi_data_path, const std::string& confirmed_cases_path, const std::string& population_data_path)
-{
-    mio::log_warning("HDF5 not available. Cannot export time series of extrapolated real data.");
-    return success();
-}
-#endif // MEMILIO_HAS_HDF5
+ * @brief Reads population data from a file and sets it for the each given model.
+ * @param[in, out] model VectorRange of Node%s containing a Model for which the confirmed cases data will be set.
+ * @param[in] path Path to population data file.
+ */
+IOResult<void> set_population_data(const mio::VectorRange<Node<Model<ScalarType>>>& model, const std::string& path);
 
 /**
- * @brief Reads population data from population files for the whole country.
- * @param[in, out] model Vector of model in which the data is set.
- * @param[in] date Date for which the data should be read.
- * @param[in] scaling_factor_inf Factors by which to scale the confirmed cases of rki data.
- * @param[in] scaling_factor_icu Factor by which to scale the icu cases of divi data.
- * @param[in] pydata_dir Directory of files.
+ * @brief Sets ICU data into a Model, distributed across age groups.
+ * @param[in, out] model Model in which the data is set.
+ * @param[in] num_icu ICU data for the region of interest.
+ * @param[in] scaling_factor_icu Scaling factor for reported ICU cases.
  */
-template <class Model>
-IOResult<void> read_input_data_germany(std::vector<Model>& model, Date date,
-                                       const std::vector<double>& scaling_factor_inf, double scaling_factor_icu,
-                                       const std::string& pydata_dir)
-{
-    BOOST_OUTCOME_TRY(
-        details::set_divi_data(model, path_join(pydata_dir, "germany_divi.json"), {0}, date, scaling_factor_icu));
-    BOOST_OUTCOME_TRY(details::set_confirmed_cases_data(model, path_join(pydata_dir, "cases_all_age_ma7.json"), {0},
-                                                        date, scaling_factor_inf));
-    BOOST_OUTCOME_TRY(
-        details::set_population_data(model, path_join(pydata_dir, "county_current_population.json"), {0}));
-    return success();
-}
+IOResult<void> set_divi_data(Model<ScalarType>& model, const ScalarType num_icu, ScalarType scaling_factor_icu);
 
 /**
- * @brief Reads population data from population files for the specefied state.
- * @param[in, out] model Vector of model in which the data is set.
- * @param[in] date Date for which the data should be read.
- * @param[in] state Vector of region keys of states of interest.
- * @param[in] scaling_factor_inf Factors by which to scale the confirmed cases of rki data.
- * @param[in] scaling_factor_icu Factor by which to scale the icu cases of divi data.
- * @param[in] pydata_dir Directory of files.
+ * @brief Sets ICU data from DIVI data into the a vector of models, distributed across age groups.
+ * 
+ * This function reads DIVI data from a file, computes the number of individuals in critical condition (ICU)
+ * for each region, and sets these values in the model. The ICU cases are distributed across age groups
+ * using the transition probabilities from severe to critical.
+ * 
+ * @param[in, out] model VectorRange of Node%s each containing a Model.
+ * @param[in] path Path to transformed DIVI file.
+ * @param[in] date Date at which the data is read.
+ * @param[in] scaling_factor_icu Scaling factor for reported ICU cases.
  */
-template <class Model>
-IOResult<void> read_input_data_state(std::vector<Model>& model, Date date, std::vector<int>& state,
-                                     const std::vector<double>& scaling_factor_inf, double scaling_factor_icu,
-                                     const std::string& pydata_dir)
-{
+IOResult<void> set_divi_data(const mio::VectorRange<Node<Model<ScalarType>>>& model, const std::string& path, Date date,
+                             ScalarType scaling_factor_icu);
 
-    BOOST_OUTCOME_TRY(
-        details::set_divi_data(model, path_join(pydata_dir, "state_divi.json"), state, date, scaling_factor_icu));
-    BOOST_OUTCOME_TRY(details::set_confirmed_cases_data(model, path_join(pydata_dir, "cases_all_state_age_ma7.json"),
-                                                        state, date, scaling_factor_inf));
-    BOOST_OUTCOME_TRY(
-        details::set_population_data(model, path_join(pydata_dir, "county_current_population.json"), state));
-    return success();
-}
+} //namespace details
 
 /**
- * @brief Reads population data from population files for the specefied county.
- * @param[in, out] model Vector of model in which the data is set.
+ * @brief Reads compartments for geographic units at a specified date from data files.
+ *
+ * This function estimates all compartments from available data using the provided model parameters.
+ * 
+ * @param[in,out] model VectorRange of Node%s each containing a Model to be initialized with data.
  * @param[in] date Date for which the data should be read.
- * @param[in] county Vector of region keys of counties of interest.
- * @param[in] scaling_factor_inf Factors by which to scale the confirmed cases of rki data.
- * @param[in] scaling_factor_icu Factor by which to scale the icu cases of divi data.
- * @param[in] pydata_dir Directory of files.
- * @param[in] num_days [Default: 0] Number of days to be simulated; required to extrapolate real data.
- * @param[in] export_time_series [Default: false] If true, reads data for each day of simulation and writes it in the same directory as the input files.
+ * @param[in] scaling_factor_inf Vector of scaling factors for confirmed cases.
+ * @param[in] scaling_factor_icu Scaling factor for ICU cases.
+ * @param[in] epidata_filenames Object containing the input data file paths.
+ * 
+ * @return An IOResult indicating success or failure.
  */
-template <class Model>
-IOResult<void> read_input_data_county(std::vector<Model>& model, Date date, const std::vector<int>& county,
-                                      const std::vector<double>& scaling_factor_inf, double scaling_factor_icu,
-                                      const std::string& pydata_dir, int num_days = 0, bool export_time_series = false)
-{
-    BOOST_OUTCOME_TRY(
-        details::set_divi_data(model, path_join(pydata_dir, "county_divi_ma7.json"), county, date, scaling_factor_icu));
-    BOOST_OUTCOME_TRY(details::set_confirmed_cases_data(model, path_join(pydata_dir, "cases_all_county_age_ma7.json"),
-                                                        county, date, scaling_factor_inf));
-    BOOST_OUTCOME_TRY(
-        details::set_population_data(model, path_join(pydata_dir, "county_current_population.json"), county));
+IOResult<void> read_input_data(const mio::VectorRange<Node<Model<ScalarType>>>& model, Date date,
+                               const std::vector<ScalarType>& scaling_factor_inf, ScalarType scaling_factor_icu,
+                               const mio::regions::de::EpidataFilenames& epidata_filenames);
 
-    if (export_time_series) {
-        // Use only if extrapolated real data is needed for comparison. EXPENSIVE !
-        // Run time equals run time of the previous functions times the num_days !
-        // (This only represents the vectorization of the previous function over all simulation days...)
-        log_warning("Exporting time series of extrapolated real data. This may take some minutes. "
-                    "For simulation runs over the same time period, deactivate it.");
-        BOOST_OUTCOME_TRY(export_input_data_county_timeseries(
-            model, pydata_dir, county, date, scaling_factor_inf, scaling_factor_icu, num_days,
-            path_join(pydata_dir, "county_divi_ma7.json"), path_join(pydata_dir, "cases_all_county_age_ma7.json"),
-            path_join(pydata_dir, "county_current_population.json")));
-    }
-    return success();
-}
+#ifdef MEMILIO_HAS_HDF5
 
 /**
- * @brief reads population data from population files for the specified nodes
- * @param[in, out] model vector of model in which the data is set
- * @param[in] date Date for which the data should be read
- * @param[in] county vector of region keys of interest
- * @param[in] scaling_factor_inf factors by which to scale the confirmed cases of rki data
- * @param[in] scaling_factor_icu factor by which to scale the icu cases of divi data
- * @param[in] pydata_dir directory of files
- * @param[in] age_group_names strings specifying age group names
+ * @brief Uses the initialisation method, which uses the reported data to set the initial conditions for the model for a given day. 
+ * 
+ * The initialisation is applied for a predefined number of days and finally saved in a timeseries for each region. In the end,
+ * we save the files "Results_rki.h5" and "Results_rki_sum.h5" in the results_dir.
+ * Results_rki.h5 contains a time series for each region and Results_rki_sum.h5 contains the sum of all regions.
+ * 
+ * @param[in] model VectorRange of Node%s each containing a Model in which the data is set.
+ * @param[in] results_dir Path to result files.
+ * @param[in] date Date for which the data should be read.
+ * @param[in] scaling_factor_inf Vector of scaling factors for confirmed cases.
+ * @param[in] scaling_factor_icu Scaling factor for ICU cases.
+ * @param[in] num_days Number of days to be simulated/initialized.
+ * @param[in] epidata_filenames Object containing the input data file paths.
+ * 
+ * @return An IOResult indicating success or failure.
  */
-template <class Model>
-IOResult<void> read_input_data(std::vector<Model>& model, Date date, const std::vector<int>& node_ids,
-                               const std::vector<double>& scaling_factor_inf, double scaling_factor_icu,
-                               const std::string& pydata_dir, int num_days = 0, bool export_time_series = false)
-{
-    BOOST_OUTCOME_TRY(details::set_divi_data(model, path_join(pydata_dir, "critical_cases.json"), node_ids, date,
-                                             scaling_factor_icu));
-    BOOST_OUTCOME_TRY(details::set_confirmed_cases_data(model, path_join(pydata_dir, "confirmed_cases.json"), node_ids,
-                                                        date, scaling_factor_inf));
-    BOOST_OUTCOME_TRY(details::set_population_data(model, path_join(pydata_dir, "population_data.json"), node_ids));
-
-    if (export_time_series) {
-        // Use only if extrapolated real data is needed for comparison. EXPENSIVE !
-        // Run time equals run time of the previous functions times the num_days !
-        // (This only represents the vectorization of the previous function over all simulation days...)
-        log_warning("Exporting time series of extrapolated real data. This may take some minutes. "
-                    "For simulation runs over the same time period, deactivate it.");
-        BOOST_OUTCOME_TRY(export_input_data_county_timeseries(
-            model, pydata_dir, node_ids, date, scaling_factor_inf, scaling_factor_icu, num_days,
-            path_join(pydata_dir, "critical_cases.json"), path_join(pydata_dir, "confirmed_cases.json"),
-            path_join(pydata_dir, "population_data.json")));
-    }
-    return success();
-}
+IOResult<void> export_input_data_timeseries(const mio::VectorRange<Node<Model<ScalarType>>> model,
+                                            const std::string& results_dir, Date date,
+                                            const std::vector<ScalarType>& scaling_factor_inf,
+                                            ScalarType scaling_factor_icu, int num_days,
+                                            const mio::regions::de::EpidataFilenames& epidata_filenames);
+#else
+IOResult<void> export_input_data_county_timeseries(const mio::VectorRange<Node<Model<ScalarType>>>, const std::string&,
+                                                   Date, const std::vector<int>&, const std::vector<ScalarType>&,
+                                                   const ScalarType, const int,
+                                                   const mio::regions::de::EpidataFilenames&);
+#endif // MEMILIO_HAS_HDF5
 
 } // namespace osecir
+
 } // namespace mio
 
 #endif // MEMILIO_HAS_JSONCPP
diff --git a/cpp/models/ode_secirts/parameters_io.cpp b/cpp/models/ode_secirts/parameters_io.cpp
index 0764859b02..949777822f 100644
--- a/cpp/models/ode_secirts/parameters_io.cpp
+++ b/cpp/models/ode_secirts/parameters_io.cpp
@@ -52,7 +52,642 @@ namespace osecirts
 {
 namespace details
 {
+
+IOResult<void> compute_confirmed_cases_data(
+    const std::vector<ConfirmedCasesDataEntry>& case_data, const int region, Date date,
+    std::vector<ScalarType>& num_Exposed, std::vector<ScalarType>& num_InfectedNoSymptoms,
+    std::vector<ScalarType>& num_InfectedSymptoms, std::vector<ScalarType>& num_InfectedSevere,
+    std::vector<ScalarType>& num_icu, std::vector<ScalarType>& num_death, std::vector<ScalarType>& num_imm,
+    const std::vector<int>& t_Exposed, const std::vector<int>& t_InfectedNoSymptoms,
+    const std::vector<int>& t_InfectedSymptoms, const std::vector<int>& t_InfectedSevere,
+    const std::vector<int>& t_InfectedCritical, const std::vector<int>& t_imm_interval_i,
+    const std::vector<ScalarType>& mu_C_R, const std::vector<ScalarType>& mu_I_H, const std::vector<ScalarType>& mu_H_U,
+    const std::vector<ScalarType>& reduc_t_Infected, const std::vector<ScalarType>& reduc_Exposed,
+    const std::vector<ScalarType>& reduc_InfectedSymptoms, const std::vector<ScalarType>& reduc_icu_death,
+    const std::vector<ScalarType>& scaling_factor_inf, const size_t layer)
+{
+    auto max_date_entry = std::max_element(case_data.begin(), case_data.end(), [](auto&& a, auto&& b) {
+        return a.date < b.date;
+    });
+    if (max_date_entry == case_data.end()) {
+        log_error("Case data file is empty.");
+        return failure(StatusCode::InvalidValue, "Case data is empty.");
+    }
+    auto max_date = max_date_entry->date;
+    if (max_date < date) {
+        log_error("Specified date does not exist in case data");
+        return failure(StatusCode::OutOfRange, "Case data does not contain specified date.");
+    }
+
+    // shifts the initilization to the recent past if simulation starts
+    // around current day and data of the future would be required.
+    // Only needed for preinfection compartments, exposed and InfectedNoSymptoms.
+    auto days_surplus = get_offset_in_days(max_date, date) - 6; // 6 > T_E + T_C
+    if (days_surplus > 0) {
+        days_surplus = 0;
+    }
+
+    for (auto&& entry : case_data) {
+        auto age = (size_t)entry.age_group;
+
+        // transition probabilities
+        ScalarType recoveredPerInfectedNoSymptoms = mu_C_R[age];
+        ScalarType severePerInfectedSymptoms      = mu_I_H[age];
+        ScalarType criticalPerSevere              = mu_H_U[age];
+
+        // transition times
+        int timeInfectedNoSymptoms = t_InfectedNoSymptoms[age];
+        int timeInfectedSymptoms   = t_InfectedSymptoms[age];
+
+        // if we select a layer with better immunity (layer > 0), we need to adjust the times and transition rates
+        if (layer > 0) {
+            timeInfectedNoSymptoms = static_cast<int>(std::round(timeInfectedNoSymptoms * reduc_t_Infected[age]));
+            timeInfectedSymptoms   = static_cast<int>(std::round(timeInfectedSymptoms * reduc_t_Infected[age]));
+
+            const ScalarType red_fact_exp = reduc_Exposed[age];
+
+            const ScalarType red_fact_inf = reduc_InfectedSymptoms[age];
+
+            const ScalarType red_fact_sev = reduc_icu_death[age];
+
+            recoveredPerInfectedNoSymptoms = 1 - red_fact_inf / red_fact_exp * (1 - recoveredPerInfectedNoSymptoms);
+            severePerInfectedSymptoms      = red_fact_sev / red_fact_inf * severePerInfectedSymptoms;
+        }
+
+        if (entry.date == offset_date_by_days(date, 0)) {
+            num_InfectedSymptoms[age] += scaling_factor_inf[age] * entry.num_confirmed;
+            num_imm[age] += entry.num_confirmed;
+        }
+        if (entry.date == offset_date_by_days(date, timeInfectedNoSymptoms + days_surplus)) {
+            num_InfectedNoSymptoms[age] +=
+                1 / (1 - recoveredPerInfectedNoSymptoms) * scaling_factor_inf[age] * entry.num_confirmed;
+            num_Exposed[age] -=
+                1 / (1 - recoveredPerInfectedNoSymptoms) * scaling_factor_inf[age] * entry.num_confirmed;
+        }
+        if (entry.date == offset_date_by_days(date, days_surplus)) {
+            num_InfectedNoSymptoms[age] -=
+                1 / (1 - recoveredPerInfectedNoSymptoms) * scaling_factor_inf[age] * entry.num_confirmed;
+        }
+        if (entry.date == offset_date_by_days(date, t_Exposed[age] + timeInfectedNoSymptoms + days_surplus)) {
+            num_Exposed[age] +=
+                1 / (1 - recoveredPerInfectedNoSymptoms) * scaling_factor_inf[age] * entry.num_confirmed;
+        }
+        if (entry.date == offset_date_by_days(date, -timeInfectedSymptoms)) {
+            num_InfectedSymptoms[age] -= scaling_factor_inf[age] * entry.num_confirmed;
+            num_InfectedSevere[age] += severePerInfectedSymptoms * scaling_factor_inf[age] * entry.num_confirmed;
+        }
+        if (entry.date == offset_date_by_days(date, -timeInfectedSymptoms - t_InfectedSevere[age])) {
+            num_InfectedSevere[age] -= severePerInfectedSymptoms * scaling_factor_inf[age] * entry.num_confirmed;
+            num_icu[age] +=
+                severePerInfectedSymptoms * criticalPerSevere * scaling_factor_inf[age] * entry.num_confirmed;
+        }
+        if (entry.date ==
+            offset_date_by_days(date, -timeInfectedSymptoms - t_InfectedSevere[age] - t_InfectedCritical[age])) {
+            num_death[age] += entry.num_deaths;
+            num_icu[age] -=
+                severePerInfectedSymptoms * criticalPerSevere * scaling_factor_inf[age] * entry.num_confirmed;
+        }
+        if (entry.date == offset_date_by_days(date, 0 - t_imm_interval_i[age])) {
+            num_imm[age] -= entry.num_confirmed;
+        }
+    }
+
+    size_t num_groups = ConfirmedCasesDataEntry::age_group_names.size();
+    for (size_t i = 0; i < num_groups; i++) {
+        auto try_fix_constraints = [region, i](ScalarType& value, ScalarType error, auto str) {
+            if (value < error) {
+                // this should probably return a failure
+                // but the algorithm is not robust enough to avoid large negative
+                // values and there are tests that rely on it
+                log_error("{:s} for age group {:s} is {:.4f} for region {:d}, "
+                          "exceeds expected negative value.",
+                          str, ConfirmedCasesDataEntry::age_group_names[i], value, region);
+                value = 0.0;
+            }
+            else if (value < 0) {
+                log_info("{:s} for age group {:s} is {:.4f} for region {:d}, "
+                         "automatically corrected",
+                         str, ConfirmedCasesDataEntry::age_group_names[i], value, region);
+                value = 0.0;
+            }
+        };
+
+        const ScalarType tol_error = -1e-8;
+        try_fix_constraints(num_InfectedSymptoms[i], tol_error, "InfectedSymptoms");
+        try_fix_constraints(num_InfectedNoSymptoms[i], tol_error, "InfectedNoSymptoms");
+        try_fix_constraints(num_Exposed[i], tol_error, "Exposed");
+        try_fix_constraints(num_InfectedSevere[i], tol_error, "InfectedSevere");
+        try_fix_constraints(num_death[i], tol_error, "Dead");
+        try_fix_constraints(num_icu[i], tol_error, "InfectedCritical");
+        try_fix_constraints(num_imm[i], tol_error, "Recently Recovered or Vaccinated");
+    }
+
+    return success();
+}
+
+IOResult<void> set_confirmed_cases_data(Model<ScalarType>& model, const std::vector<ConfirmedCasesDataEntry>& case_data,
+                                        const int region, Date date, const std::vector<ScalarType>& scaling_factor_inf,
+                                        const std::vector<std::vector<ScalarType>>& immunity_population)
+{
+    auto num_age_groups = (size_t)model.parameters.get_num_groups();
+    assert(scaling_factor_inf.size() == num_age_groups); //TODO: allow vector or scalar valued scaling factors
+    assert(ConfirmedCasesDataEntry::age_group_names.size() == num_age_groups);
+
+    std::vector<int> t_Exposed;
+    std::vector<int> t_InfectedNoSymptoms;
+    std::vector<int> t_InfectedSymptoms;
+    std::vector<int> t_InfectedSevere;
+    std::vector<int> t_InfectedCritical;
+    std::vector<int> t_imm_interval_i;
+
+    std::vector<ScalarType> mu_C_R;
+    std::vector<ScalarType> mu_I_H;
+    std::vector<ScalarType> mu_H_U;
+
+    std::vector<ScalarType> reduc_t_Infected;
+    std::vector<ScalarType> reduc_Exposed;
+    std::vector<ScalarType> reduc_InfectedSymptoms;
+    std::vector<ScalarType> reduc_icu_death;
+
+    std::vector<ScalarType> num_InfectedSymptoms(num_age_groups, 0.0);
+    std::vector<ScalarType> num_death(num_age_groups, 0.0);
+    std::vector<ScalarType> num_Exposed(num_age_groups, 0.0);
+    std::vector<ScalarType> num_InfectedNoSymptoms(num_age_groups, 0.0);
+    std::vector<ScalarType> num_InfectedSevere(num_age_groups, 0.0);
+    std::vector<ScalarType> num_icu(num_age_groups, 0.0);
+    std::vector<ScalarType> num_timm1(num_age_groups, 0.0);
+    std::vector<ScalarType> num_timm2(num_age_groups, 0.0);
+
+    std::vector<ScalarType> denom_E(num_age_groups, 0.0);
+    std::vector<ScalarType> denom_I_NS(num_age_groups, 0.0);
+    std::vector<ScalarType> denom_I_Sy(num_age_groups, 0.0);
+    std::vector<ScalarType> denom_I_Sev_Cr(num_age_groups, 0.0);
+
+    // calculate the denominators to split the reported case numbers to the different immunity layers.
+    for (size_t group = 0; group < num_age_groups; group++) {
+        denom_E[group] =
+            1 / (immunity_population[0][group] +
+                 immunity_population[1][group] *
+                     model.parameters.template get<ReducExposedPartialImmunity<ScalarType>>()[(AgeGroup)group] +
+                 immunity_population[2][group] *
+                     model.parameters.template get<ReducExposedImprovedImmunity<ScalarType>>()[(AgeGroup)group]);
+
+        denom_I_NS[group] =
+            1 / (immunity_population[0][group] +
+                 immunity_population[1][group] *
+                     model.parameters.template get<ReducExposedPartialImmunity<ScalarType>>()[(AgeGroup)group] +
+                 immunity_population[2][group] *
+                     model.parameters.template get<ReducExposedImprovedImmunity<ScalarType>>()[(AgeGroup)group]);
+
+        denom_I_Sy[group] =
+            1 /
+            (immunity_population[0][group] +
+             immunity_population[1][group] *
+                 model.parameters.template get<ReducInfectedSymptomsPartialImmunity<ScalarType>>()[(AgeGroup)group] +
+             immunity_population[2][group] *
+                 model.parameters.template get<ReducInfectedSymptomsImprovedImmunity<ScalarType>>()[(AgeGroup)group]);
+
+        denom_I_Sev_Cr[group] =
+            1 / (immunity_population[0][group] +
+                 immunity_population[1][group] *
+                     model.parameters
+                         .template get<ReducInfectedSevereCriticalDeadPartialImmunity<ScalarType>>()[(AgeGroup)group] +
+                 immunity_population[2][group] *
+                     model.parameters
+                         .template get<ReducInfectedSevereCriticalDeadImprovedImmunity<ScalarType>>()[(AgeGroup)group]);
+    }
+
+    /*----------- Naive immunity -----------*/
+    for (size_t group = 0; group < num_age_groups; group++) {
+        t_Exposed.push_back(
+            static_cast<int>(std::round(model.parameters.template get<TimeExposed<ScalarType>>()[(AgeGroup)group])));
+        t_InfectedNoSymptoms.push_back(static_cast<int>(
+            std::round(model.parameters.template get<TimeInfectedNoSymptoms<ScalarType>>()[(AgeGroup)group])));
+        t_InfectedSymptoms.push_back(static_cast<int>(
+            std::round(model.parameters.template get<TimeInfectedSymptoms<ScalarType>>()[(AgeGroup)group])));
+        t_InfectedSevere.push_back(static_cast<int>(
+            std::round(model.parameters.template get<TimeInfectedSevere<ScalarType>>()[(AgeGroup)group])));
+        t_InfectedCritical.push_back(static_cast<int>(
+            std::round(model.parameters.template get<TimeInfectedCritical<ScalarType>>()[(AgeGroup)group])));
+        t_imm_interval_i.push_back(static_cast<int>(
+            std::round(model.parameters.template get<TimeTemporaryImmunityPI<ScalarType>>()[(AgeGroup)group])));
+
+        mu_C_R.push_back(model.parameters.template get<RecoveredPerInfectedNoSymptoms<ScalarType>>()[(AgeGroup)group]);
+        mu_I_H.push_back(model.parameters.template get<SeverePerInfectedSymptoms<ScalarType>>()[(AgeGroup)group]);
+        mu_H_U.push_back(model.parameters.template get<CriticalPerSevere<ScalarType>>()[(AgeGroup)group]);
+
+        reduc_t_Infected.push_back(model.parameters.template get<ReducTimeInfectedMild<ScalarType>>()[(AgeGroup)group]);
+        reduc_Exposed.push_back(
+            model.parameters.template get<ReducExposedPartialImmunity<ScalarType>>()[(AgeGroup)group]);
+        reduc_InfectedSymptoms.push_back(
+            model.parameters.template get<ReducInfectedSymptomsPartialImmunity<ScalarType>>()[(AgeGroup)group]);
+        reduc_icu_death.push_back(
+            model.parameters
+                .template get<ReducInfectedSevereCriticalDeadPartialImmunity<ScalarType>>()[(AgeGroup)group]);
+    }
+
+    BOOST_OUTCOME_TRY(compute_confirmed_cases_data(
+        case_data, region, date, num_Exposed, num_InfectedNoSymptoms, num_InfectedSymptoms, num_InfectedSevere, num_icu,
+        num_death, num_timm1, t_Exposed, t_InfectedNoSymptoms, t_InfectedSymptoms, t_InfectedSevere, t_InfectedCritical,
+        t_imm_interval_i, mu_C_R, mu_I_H, mu_H_U, reduc_t_Infected, reduc_Exposed, reduc_InfectedSymptoms, 
+        reduc_icu_death, scaling_factor_inf, 0));
+
+    size_t num_groups = (size_t)model.parameters.get_num_groups();
+    for (size_t i = 0; i < num_groups; i++) {
+        model.populations[{AgeGroup(i), InfectionState::ExposedNaive}] =
+            immunity_population[0][i] * denom_E[i] * num_Exposed[i];
+        model.populations[{AgeGroup(i), InfectionState::InfectedNoSymptomsNaive}] =
+            immunity_population[0][i] * denom_I_NS[i] * num_InfectedNoSymptoms[i];
+        model.populations[{AgeGroup(i), InfectionState::InfectedNoSymptomsNaiveConfirmed}] = 0;
+        model.populations[{AgeGroup(i), InfectionState::InfectedSymptomsNaive}] =
+            immunity_population[0][i] * denom_I_Sy[i] * num_InfectedSymptoms[i];
+        model.populations[{AgeGroup(i), InfectionState::InfectedSymptomsNaiveConfirmed}] = 0;
+        model.populations[{AgeGroup(i), InfectionState::InfectedSevereNaive}] =
+            immunity_population[0][i] * denom_I_Sev_Cr[i] * num_InfectedSevere[i];
+        // Only set the number of ICU patients here, if the date is not available in the data.
+        if (!is_divi_data_available(date)) {
+            model.populations[{AgeGroup(i), InfectionState::InfectedCriticalNaive}] =
+                immunity_population[0][i] * denom_I_Sev_Cr[i] * num_icu[i];
+        }
+    }
+    if (std::accumulate(num_InfectedSymptoms.begin(), num_InfectedSymptoms.end(), ScalarType(0.0),
+                        [](const ScalarType& a, const ScalarType& b) {
+                            return evaluate_intermediate<ScalarType>(a + b);
+                        }) == 0) {
+        log_warning(
+            "No infections for unvaccinated reported on date {} for region {}. Population data has not been set.", date,
+            region);
+    }
+
+    /*----------- PARTIAL Immunity -----------*/
+    reduc_Exposed.clear();
+    reduc_InfectedSymptoms.clear();
+    reduc_icu_death.clear();
+
+    num_InfectedSymptoms   = std::vector<ScalarType>(num_age_groups, 0.0);
+    num_death              = std::vector<ScalarType>(num_age_groups, 0.0);
+    num_Exposed            = std::vector<ScalarType>(num_age_groups, 0.0);
+    num_InfectedNoSymptoms = std::vector<ScalarType>(num_age_groups, 0.0);
+    num_InfectedSevere     = std::vector<ScalarType>(num_age_groups, 0.0);
+    num_icu                = std::vector<ScalarType>(num_age_groups, 0.0);
+
+    for (size_t group = 0; group < num_age_groups; group++) {
+        reduc_Exposed.push_back(
+            model.parameters.template get<ReducExposedPartialImmunity<ScalarType>>()[(AgeGroup)group]);
+        reduc_InfectedSymptoms.push_back(
+            model.parameters.template get<ReducInfectedSymptomsPartialImmunity<ScalarType>>()[(AgeGroup)group]);
+        reduc_icu_death.push_back(
+            model.parameters
+                .template get<ReducInfectedSevereCriticalDeadPartialImmunity<ScalarType>>()[(AgeGroup)group]);
+    }
+
+    BOOST_OUTCOME_TRY(compute_confirmed_cases_data(
+        case_data, region, date, num_Exposed, num_InfectedNoSymptoms, num_InfectedSymptoms, num_InfectedSevere, num_icu,
+        num_death, num_timm1, t_Exposed, t_InfectedNoSymptoms, t_InfectedSymptoms, t_InfectedSevere, t_InfectedCritical,
+        t_imm_interval_i, mu_C_R, mu_I_H, mu_H_U, reduc_t_Infected, reduc_Exposed, reduc_InfectedSymptoms, 
+        reduc_icu_death, scaling_factor_inf, 1));
+
+    for (size_t i = 0; i < num_groups; i++) {
+        model.populations[{AgeGroup(i), InfectionState::ExposedPartialImmunity}] =
+            immunity_population[1][i] *
+            model.parameters.template get<ReducExposedPartialImmunity<ScalarType>>()[(AgeGroup)i] * denom_E[i] *
+            num_Exposed[i];
+        model.populations[{AgeGroup(i), InfectionState::InfectedNoSymptomsPartialImmunity}] =
+            immunity_population[1][i] *
+            model.parameters.template get<ReducExposedPartialImmunity<ScalarType>>()[(AgeGroup)i] * denom_I_NS[i] *
+            num_InfectedNoSymptoms[i];
+        model.populations[{AgeGroup(i), InfectionState::InfectedNoSymptomsPartialImmunityConfirmed}] = 0;
+        model.populations[{AgeGroup(i), InfectionState::InfectedSymptomsPartialImmunity}] =
+            immunity_population[1][i] *
+            model.parameters.template get<ReducInfectedSymptomsPartialImmunity<ScalarType>>()[(AgeGroup)i] *
+            denom_I_Sy[i] * num_InfectedSymptoms[i];
+        model.populations[{AgeGroup(i), InfectionState::InfectedSymptomsPartialImmunityConfirmed}] = 0;
+        model.populations[{AgeGroup(i), InfectionState::InfectedSeverePartialImmunity}] =
+            immunity_population[1][i] *
+            model.parameters.template get<ReducInfectedSevereCriticalDeadPartialImmunity<ScalarType>>()[(AgeGroup)i] *
+            denom_I_Sev_Cr[i] * num_InfectedSevere[i];
+        // Only set the number of ICU patients here, if the date is not available in the data.
+        if (!is_divi_data_available(date)) {
+            model.populations[{AgeGroup(i), InfectionState::InfectedCriticalPartialImmunity}] =
+                immunity_population[1][i] *
+                model.parameters
+                    .template get<ReducInfectedSevereCriticalDeadPartialImmunity<ScalarType>>()[(AgeGroup)i] *
+                denom_I_Sev_Cr[i] * num_icu[i];
+        }
+        // the += is necessary because we already set the previous vaccinated individuals
+        model.populations[{AgeGroup(i), InfectionState::TemporaryImmunePartialImmunity}] +=
+            immunity_population[1][i] *
+            model.parameters.template get<ReducExposedPartialImmunity<ScalarType>>()[(AgeGroup)i] * denom_E[i] *
+            num_timm1[i];
+    }
+    if (std::accumulate(num_InfectedSymptoms.begin(), num_InfectedSymptoms.end(), ScalarType(0.0),
+                        [](const ScalarType& a, const ScalarType& b) {
+                            return evaluate_intermediate<ScalarType>(a + b);
+                        }) == 0) {
+
+        log_warning("No infections for partially vaccinated reported on date {} for region {}. "
+                    "Population data has not been set.",
+                    date, region);
+    }
+
+    /*----------- Improved Immunity -----------*/
+    reduc_Exposed.clear();
+    reduc_InfectedSymptoms.clear();
+    reduc_icu_death.clear();
+
+    num_InfectedSymptoms   = std::vector<ScalarType>(num_age_groups, 0.0);
+    num_death              = std::vector<ScalarType>(num_age_groups, 0.0);
+    num_Exposed            = std::vector<ScalarType>(num_age_groups, 0.0);
+    num_InfectedNoSymptoms = std::vector<ScalarType>(num_age_groups, 0.0);
+    num_InfectedSevere     = std::vector<ScalarType>(num_age_groups, 0.0);
+    num_icu                = std::vector<ScalarType>(num_age_groups, 0.0);
+
+    for (size_t group = 0; group < num_age_groups; group++) {
+        reduc_Exposed.push_back(
+            model.parameters.template get<ReducExposedImprovedImmunity<ScalarType>>()[(AgeGroup)group]);
+        reduc_InfectedSymptoms.push_back(
+            model.parameters.template get<ReducInfectedSymptomsImprovedImmunity<ScalarType>>()[(AgeGroup)group]);
+        reduc_icu_death.push_back(
+            model.parameters
+                .template get<ReducInfectedSevereCriticalDeadImprovedImmunity<ScalarType>>()[(AgeGroup)group]);
+    }
+
+    BOOST_OUTCOME_TRY(compute_confirmed_cases_data(
+        case_data, region, date, num_Exposed, num_InfectedNoSymptoms, num_InfectedSymptoms, num_InfectedSevere, num_icu,
+        num_death, num_timm2, t_Exposed, t_InfectedNoSymptoms, t_InfectedSymptoms, t_InfectedSevere, t_InfectedCritical,
+        t_imm_interval_i, mu_C_R, mu_I_H, mu_H_U, reduc_t_Infected, reduc_Exposed, reduc_InfectedSymptoms, 
+        reduc_icu_death, scaling_factor_inf, 2));
+
+    for (size_t i = 0; i < num_groups; i++) {
+        model.populations[{AgeGroup(i), InfectionState::ExposedImprovedImmunity}] =
+            immunity_population[2][i] *
+            model.parameters.template get<ReducExposedImprovedImmunity<ScalarType>>()[(AgeGroup)i] * denom_E[i] *
+            num_Exposed[i];
+        model.populations[{AgeGroup(i), InfectionState::InfectedNoSymptomsImprovedImmunity}] =
+            immunity_population[2][i] *
+            model.parameters.template get<ReducExposedImprovedImmunity<ScalarType>>()[(AgeGroup)i] * denom_I_NS[i] *
+            num_InfectedNoSymptoms[i];
+        model.populations[{AgeGroup(i), InfectionState::InfectedNoSymptomsImprovedImmunityConfirmed}] = 0;
+        model.populations[{AgeGroup(i), InfectionState::InfectedSymptomsImprovedImmunity}] =
+            immunity_population[2][i] *
+            model.parameters.template get<ReducInfectedSymptomsImprovedImmunity<ScalarType>>()[(AgeGroup)i] *
+            denom_I_Sy[i] * num_InfectedSymptoms[i];
+        model.populations[{AgeGroup(i), InfectionState::InfectedSymptomsImprovedImmunityConfirmed}] = 0;
+        model.populations[{AgeGroup(i), InfectionState::InfectedSevereImprovedImmunity}] =
+            immunity_population[2][i] *
+            model.parameters.template get<ReducInfectedSevereCriticalDeadImprovedImmunity<ScalarType>>()[(AgeGroup)i] *
+            denom_I_Sev_Cr[i] * num_InfectedSevere[i];
+        // Only set the number of ICU patients here, if the date is not available in the data.
+        if (!is_divi_data_available(date)) {
+            model.populations[{AgeGroup(i), InfectionState::InfectedCriticalImprovedImmunity}] =
+                immunity_population[2][i] *
+                model.parameters
+                    .template get<ReducInfectedSevereCriticalDeadImprovedImmunity<ScalarType>>()[(AgeGroup)i] *
+                denom_I_Sev_Cr[i] * num_icu[i];
+        }
+
+        // the += is necessary because we already set the previous vaccinated individuals
+        model.populations[{AgeGroup(i), InfectionState::TemporaryImmuneImprovedImmunity}] +=
+            immunity_population[2][i] *
+            model.parameters.template get<ReducExposedImprovedImmunity<ScalarType>>()[(AgeGroup)i] * denom_E[i] *
+            num_timm2[i];
+    }
+    if (std::accumulate(num_InfectedSymptoms.begin(), num_InfectedSymptoms.end(), ScalarType(0.0),
+                        [](const ScalarType& a, const ScalarType& b) {
+                            return evaluate_intermediate<ScalarType>(a + b);
+                        }) == 0) {
+        log_warning("No infections for vaccinated reported on date {} for region {}. "
+                    "Population data has not been set.",
+                    date, region);
+    }
+    return success();
+}
+
+IOResult<void> set_confirmed_cases_data(const mio::VectorRange<Node<Model<ScalarType>>>& model, const std::string& path,
+                                        Date date, const std::vector<ScalarType>& scaling_factor_inf,
+                                        const std::vector<std::vector<ScalarType>>& immunity_population)
+{
+    BOOST_OUTCOME_TRY(auto&& case_data, mio::read_confirmed_cases_data(path));
+
+    // sort case_data into regions and ignore once with no region associated
+    std::vector<std::vector<ConfirmedCasesDataEntry>> vcase_data{model.size()};
+    for (auto&& entry : case_data) {
+        auto it = std::find_if(model.begin(), model.end(), [&entry](auto m) {
+            return m.id == 0 || get_region_id(entry) == m.id;
+        });
+        if (it != model.end()) {
+            auto region_idx = size_t(it - model.begin());
+            vcase_data[region_idx].push_back(entry);
+        }
+    }
+
+    for (size_t region_idx = 0; region_idx < model.size(); ++region_idx) {
+        BOOST_OUTCOME_TRY(set_confirmed_cases_data(model[region_idx].property, vcase_data[region_idx],
+                                                   model[region_idx].id, date, scaling_factor_inf,
+                                                   immunity_population));
+    }
+
+    return success();
+}
+
+IOResult<void> set_population_data(Model<ScalarType>& model, const std::vector<ScalarType>& num_population,
+                                   const int region, const std::vector<std::vector<ScalarType>>& immunity_population)
+{
+    if (std::accumulate(num_population.begin(), num_population.end(), ScalarType(0.0),
+                        [](const ScalarType& a, const ScalarType& b) {
+                            return evaluate_intermediate<ScalarType>(a + b);
+                        }) <= 0) {
+        log_warning("No population data available for region " + std::to_string(region) +
+                    ". Population data has not been set.");
+        return success();
+    }
+
+    auto num_groups = model.parameters.get_num_groups();
+    for (auto i = AgeGroup(0); i < num_groups; i++) {
+
+        ScalarType SN  = num_population[size_t(i)] * immunity_population[0][size_t(i)];
+        ScalarType SPI = num_population[size_t(i)] * immunity_population[1][size_t(i)];
+        ScalarType SII = num_population[size_t(i)] - SN - SPI;
+
+        model.populations[{i, InfectionState::SusceptibleImprovedImmunity}] =
+            std::max(0.0, SII - (model.populations[{i, InfectionState::ExposedImprovedImmunity}] +
+                                 model.populations[{i, InfectionState::InfectedNoSymptomsImprovedImmunity}] +
+                                 model.populations[{i, InfectionState::InfectedNoSymptomsImprovedImmunityConfirmed}] +
+                                 model.populations[{i, InfectionState::InfectedSymptomsImprovedImmunity}] +
+                                 model.populations[{i, InfectionState::InfectedSymptomsImprovedImmunityConfirmed}] +
+                                 model.populations[{i, InfectionState::InfectedSevereImprovedImmunity}] +
+                                 model.populations[{i, InfectionState::InfectedCriticalImprovedImmunity}] +
+                                 model.populations[{i, InfectionState::DeadImprovedImmunity}] +
+                                 model.populations[{i, InfectionState::TemporaryImmuneImprovedImmunity}]));
+
+        model.populations[{i, InfectionState::SusceptiblePartialImmunity}] =
+            std::max(0.0, SPI - model.populations[{i, InfectionState::ExposedPartialImmunity}] -
+                              model.populations[{i, InfectionState::InfectedNoSymptomsPartialImmunity}] -
+                              model.populations[{i, InfectionState::InfectedNoSymptomsPartialImmunityConfirmed}] -
+                              model.populations[{i, InfectionState::InfectedSymptomsPartialImmunity}] -
+                              model.populations[{i, InfectionState::InfectedSymptomsPartialImmunityConfirmed}] -
+                              model.populations[{i, InfectionState::InfectedSeverePartialImmunity}] -
+                              model.populations[{i, InfectionState::InfectedCriticalPartialImmunity}] -
+                              model.populations[{i, InfectionState::DeadPartialImmunity}] -
+                              model.populations[{i, InfectionState::TemporaryImmunePartialImmunity}]);
+
+        model.populations.template set_difference_from_group_total<AgeGroup>({i, InfectionState::SusceptibleNaive},
+                                                                             num_population[size_t(i)]);
+    }
+
+    for (auto i = AgeGroup(0); i < AgeGroup(6); i++) {
+        for (auto j = Index<InfectionState>(0); j < InfectionState::Count; ++j) {
+            if (model.populations[{i, j}] < 0) {
+                log_warning("Compartment at age group {}, infection state {}, is negative: {}", size_t(i), size_t(j),
+                            model.populations[{i, j}]);
+            }
+        }
+    }
+
+    return success();
+}
+
+IOResult<void> set_population_data(const mio::VectorRange<Node<Model<ScalarType>>>& model, const std::string& path,
+                                   const std::vector<std::vector<ScalarType>>& immunity_population)
+{
+    std::vector<int> vregion;
+    std::transform(model.begin(), model.end(), std::back_inserter(vregion), [](const auto& m) {
+        return m.id;
+    });
+    BOOST_OUTCOME_TRY(auto&& num_population, mio::read_population_data(path, vregion));
+
+    for (size_t region_idx = 0; region_idx < model.size(); ++region_idx) {
+        BOOST_OUTCOME_TRY(set_population_data(model[region_idx].property, num_population[region_idx],
+                                              model[region_idx].id, immunity_population));
+    }
+    return success();
+}
+
+IOResult<void> set_divi_data(Model<ScalarType>& model, const ScalarType num_icu, ScalarType scaling_factor_icu)
+{
+    ScalarType sum_mu_I_U = 0;
+    std::vector<ScalarType> mu_I_U;
+    auto num_groups = model.parameters.get_num_groups();
+    for (auto i = AgeGroup(0); i < num_groups; i++) {
+        sum_mu_I_U += model.parameters.template get<CriticalPerSevere<ScalarType>>()[i] *
+                      model.parameters.template get<SeverePerInfectedSymptoms<ScalarType>>()[i];
+        mu_I_U.push_back(model.parameters.template get<CriticalPerSevere<ScalarType>>()[i] *
+                         model.parameters.template get<SeverePerInfectedSymptoms<ScalarType>>()[i]);
+    }
+
+    for (auto i = AgeGroup(0); i < num_groups; i++) {
+        model.populations[{i, InfectionState::InfectedCriticalNaive}] =
+            scaling_factor_icu * num_icu * mu_I_U[(size_t)i] / sum_mu_I_U;
+    }
+
+    return success();
+}
+
+IOResult<void> set_divi_data(const mio::VectorRange<Node<Model<ScalarType>>>& model, const std::string& path, Date date,
+                             ScalarType scaling_factor_icu)
+{
+    // DIVI dataset will no longer be updated from CW29 2024 on.
+    if (!is_divi_data_available(date)) {
+        log_warning("No DIVI data available for date: {}. "
+                    "ICU compartment will be set based on Case data.",
+                    date);
+        return success();
+    }
+
+    std::vector<int> vregion;
+    std::transform(model.begin(), model.end(), std::back_inserter(vregion), [](const auto& m) {
+        return m.id;
+    });
+    BOOST_OUTCOME_TRY(auto&& num_icu, read_divi_data(path, vregion, date));
+
+    for (size_t region_idx = 0; region_idx < model.size(); ++region_idx) {
+        BOOST_OUTCOME_TRY(set_divi_data(model[region_idx].property, num_icu[region_idx], scaling_factor_icu));
+    }
+
+    return success();
+}
+
 } // namespace details
+
+IOResult<void> read_input_data(const mio::VectorRange<Node<Model<ScalarType>>>& model, Date date,
+                               const std::vector<ScalarType>& scaling_factor_inf, ScalarType scaling_factor_icu,
+                               int num_days, const std::vector<std::vector<ScalarType>>& immunity_population,
+                               const mio::regions::de::EpidataFilenames& epidata_filenames)
+{
+
+    BOOST_OUTCOME_TRY(
+        details::set_vaccination_data<ScalarType>(model, epidata_filenames.vaccination_data_path, date, num_days));
+
+    // TODO: Reuse more code, e.g., set_divi_data (in secir) and a set_divi_data (here) only need a different ModelType.
+    // TODO: add option to set ICU data from confirmed cases if DIVI or other data is not available.
+    BOOST_OUTCOME_TRY(details::set_divi_data(model, epidata_filenames.divi_data_path, date, scaling_factor_icu));
+
+    BOOST_OUTCOME_TRY(details::set_confirmed_cases_data(model, epidata_filenames.case_data_path, date,
+                                                        scaling_factor_inf, immunity_population));
+    BOOST_OUTCOME_TRY(details::set_population_data(model, epidata_filenames.population_data_path, immunity_population));
+    return success();
+}
+
+#ifdef MEMILIO_HAS_HDF5
+
+IOResult<void> export_input_data_timeseries(const mio::VectorRange<Node<Model<ScalarType>>> model,
+                                            const std::string& results_dir, Date date,
+                                            const std::vector<ScalarType>& scaling_factor_inf,
+                                            const ScalarType scaling_factor_icu, const int num_days,
+                                            const std::vector<std::vector<ScalarType>>& immunity_population,
+                                            const mio::regions::de::EpidataFilenames& epidata_filenames)
+{
+    const auto num_age_groups = (size_t)model[0].property.parameters.get_num_groups();
+    assert(scaling_factor_inf.size() == num_age_groups);
+    assert(num_age_groups == ConfirmedCasesDataEntry::age_group_names.size());
+    std::vector<TimeSeries<ScalarType>> extrapolated_data(
+        model.size(), TimeSeries<ScalarType>::zero(num_days + 1, (size_t)InfectionState::Count * num_age_groups));
+
+    for (int t = 0; t <= num_days; ++t) {
+        auto offset_day = offset_date_by_days(date, t);
+
+        // TODO: empty vaccination data path guard
+        BOOST_OUTCOME_TRY(read_input_data(model, offset_day, scaling_factor_inf, scaling_factor_icu, num_days,
+                                          immunity_population, epidata_filenames));
+
+        for (size_t r = 0; r < model.size(); r++) {
+            extrapolated_data[r][t] = model[r].property.get_initial_values();
+            // in set_population_data the number of death individuals is subtracted from the SusceptibleImprovedImmunity compartment.
+            // Since we should be independent whether we consider them or not, we add them back here before we save the data.
+            for (size_t age = 0; age < num_age_groups; age++) {
+                extrapolated_data[r][t][(size_t)InfectionState::SusceptibleImprovedImmunity +
+                                        age * (size_t)InfectionState::Count] +=
+                    extrapolated_data[r][t][(size_t)InfectionState::DeadNaive + age * (size_t)InfectionState::Count];
+            }
+        }
+    }
+
+    std::vector<int> vregion;
+    std::transform(model.begin(), model.end(), std::back_inserter(vregion), [](const auto& m) {
+        return m.id;
+    });
+    BOOST_OUTCOME_TRY(save_result(extrapolated_data, vregion, static_cast<int>(num_age_groups),
+                                  path_join(results_dir, "Results_rki.h5")));
+
+    auto extrapolated_rki_data_sum = sum_nodes(std::vector<std::vector<TimeSeries<ScalarType>>>{extrapolated_data});
+    BOOST_OUTCOME_TRY(save_result({extrapolated_rki_data_sum[0][0]}, {0}, static_cast<int>(num_age_groups),
+                                  path_join(results_dir, "Results_rki_sum.h5")));
+
+    return success();
+}
+
+#else
+IOResult<void> export_input_data_timeseries(const mio::VectorRange<Node<Model<ScalarType>>>, const std::string&, Date,
+                                            const std::vector<int>&, const std::vector<ScalarType>&, const ScalarType,
+                                            const int, const std::vector<std::vector<ScalarType>>,
+                                            const mio::regions::de::EpidataFilenames&)
+{
+    mio::log_warning("HDF5 not available. Cannot export time series of extrapolated real data.");
+    return success();
+}
+
+#endif //MEMILIO_HAS_HDF5
+
 } // namespace osecirts
 } // namespace mio
 
diff --git a/cpp/models/ode_secirts/parameters_io.h b/cpp/models/ode_secirts/parameters_io.h
index c86263e717..e5f5510890 100644
--- a/cpp/models/ode_secirts/parameters_io.h
+++ b/cpp/models/ode_secirts/parameters_io.h
@@ -26,6 +26,7 @@
 
 #include "ode_secirts/model.h"
 #include "ode_secirts/analyze_result.h"
+#include "memilio/geography/regions.h"
 #include "memilio/math/eigen_util.h"
 #include "memilio/math/math_utils.h"
 #include "memilio/mobility/graph.h"
@@ -45,718 +46,200 @@ namespace osecirts
 
 namespace details
 {
+
 /**
- * @brief Computes the distribution of confirmed cases across infection states based on Case (RKI) data.
- *
- * This function processes case data for given regions and distributes the cases across different
- * infection states, considering the corresponding transition times and probabilities defined in the model.
- *
- * @tparam Model The type of the model used.
- * @tparam FP Floating point type (default: double).
- *
- * @param[in] case_data Vector of confirmed case data entries (defined in epi_data.h).
- * @param[out] vnum_Exposed Output vector for the number of exposed individuals per age group and region.
- * @param[out] vnum_InfectedNoSymptoms Output vector for the number of infected individuals without symptoms.
- * @param[out] vnum_InfectedSymptoms Output vector for the number of infected individuals with symptoms.
- * @param[out] vnum_InfectedSevere Output vector for the number of severely infected individuals.
- * @param[out] vnum_icu Output vector for the number of individuals in critical condition (ICU).
- * @param[out] vnum_death Output vector for the number of deaths.
- * @param[out] vnum_timm_i Output vector for the number of individuals in temporary immunity state.
- * @param[in] vregion Vector of region IDs representing the regions in the model vector.
- * @param[in] date Date for which the simulation starts.
- * @param[in] model Vector of models, each representing a region and containing the parameters.
- * @param[in] scaling_factor_inf Vector of scaling factors for confirmed cases for
+ * @brief Reads populations data from rki data.
+ * @param[in] case_data Vector of ConfirmedCasesDataEntry%s.
+ * @param[in] region Key of the region of interest.
+ * @param[in] date Date at which the data is read.
+ * @param[in, out] num_* Output vector for number of people in the corresponding compartement.
+ * @param[in] t_* vector Average time it takes to get from one compartement to another for each age group.
+ * @param[in] mu_* vector Probabilities to get from one compartement to another for each age group.
+ * @param[in] reduc_* vector Factors by which to adjust the times and transition rates based on immunity layer.
+ * @param[in] scaling_factor_inf Factors by which to scale the confirmed cases of rki data.
  * @param[in] layer Specifies the immunity layer: 0 (Naive), 1 (Partial Immunity), 2 (Improved Immunity).
- *
- * @return An IOResult showing success or failure.
  */
-template <typename FP, class Model>
 IOResult<void> compute_confirmed_cases_data(
-    const std::vector<ConfirmedCasesDataEntry>& case_data, std::vector<std::vector<FP>>& vnum_Exposed,
-    std::vector<std::vector<FP>>& vnum_InfectedNoSymptoms, std::vector<std::vector<FP>>& vnum_InfectedSymptoms,
-    std::vector<std::vector<FP>>& vnum_InfectedSevere, std::vector<std::vector<FP>>& vnum_icu,
-    std::vector<std::vector<FP>>& vnum_death, std::vector<std::vector<FP>>& vnum_timm_i,
-    std::vector<int> const& vregion, Date date, const std::vector<Model>& model,
-    const std::vector<FP>& scaling_factor_inf, const size_t layer)
-{
-    auto max_date_entry = std::max_element(case_data.begin(), case_data.end(), [](auto&& a, auto&& b) {
-        return a.date < b.date;
-    });
-    if (max_date_entry == case_data.end()) {
-        log_error("Case data file is empty.");
-        return failure(StatusCode::InvalidValue, "Case data is empty.");
-    }
-    auto max_date = max_date_entry->date;
-    if (max_date < date) {
-        log_error("Specified date does not exist in case data");
-        return failure(StatusCode::OutOfRange, "Case data does not contain specified date.");
-    }
-
-    // shifts the initilization to the recent past if simulation starts
-    // around current day and data of the future would be required.
-    // Only needed for preinfection compartments, exposed and InfectedNoSymptoms.
-    auto days_surplus = get_offset_in_days(max_date, date) - 6; // 6 > T_E + T_C
-    if (days_surplus > 0) {
-        days_surplus = 0;
-    }
-
-    for (auto&& entry : case_data) {
-        auto it = std::find_if(vregion.begin(), vregion.end(), [&entry](auto r) {
-            return r == 0 || get_region_id(entry) == r;
-        });
-        if (it != vregion.end()) {
-            auto region_idx = size_t(it - vregion.begin());
-
-            auto params_region           = model[region_idx].parameters;
-            auto& num_InfectedNoSymptoms = vnum_InfectedNoSymptoms[region_idx];
-            auto& num_InfectedSymptoms   = vnum_InfectedSymptoms[region_idx];
-            auto& num_Exposed            = vnum_Exposed[region_idx];
-            auto& num_InfectedSevere     = vnum_InfectedSevere[region_idx];
-            auto& num_death              = vnum_death[region_idx];
-            auto& num_icu                = vnum_icu[region_idx];
-            auto& num_imm                = vnum_timm_i[region_idx];
-
-            auto age = (size_t)entry.age_group;
-            // (rounded) transition times
-            const int t_exposed =
-                static_cast<int>(std::round(params_region.template get<TimeExposed<FP>>()[entry.age_group]));
-            int t_InfectedNoSymptoms =
-                static_cast<int>(std::round(params_region.template get<TimeInfectedNoSymptoms<FP>>()[entry.age_group]));
-            int t_InfectedSymptoms =
-                static_cast<int>(std::round(params_region.template get<TimeInfectedSymptoms<FP>>()[entry.age_group]));
-            const int t_InfectedSevere =
-                static_cast<int>(std::round(params_region.template get<TimeInfectedSevere<FP>>()[entry.age_group]));
-            const int t_InfectedCritical =
-                static_cast<int>(std::round(params_region.template get<TimeInfectedCritical<FP>>()[entry.age_group]));
-            const int t_imm_interval_i = static_cast<int>(
-                std::round(params_region.template get<TimeTemporaryImmunityPI<FP>>()[entry.age_group]));
-
-            // transition probabilities
-            FP recoveredPerInfectedNoSymptoms =
-                params_region.template get<RecoveredPerInfectedNoSymptoms<FP>>()[entry.age_group];
-            FP severePerInfectedSymptoms = params_region.template get<SeverePerInfectedSymptoms<FP>>()[entry.age_group];
-            FP criticalPerSevere         = params_region.template get<CriticalPerSevere<FP>>()[entry.age_group];
-
-            // if we select a layer with better immunity (layer > 0), we need to adjust the times and transition rates
-            if (layer > 0) {
-                t_InfectedNoSymptoms = static_cast<int>(std::round(
-                    t_InfectedNoSymptoms * params_region.template get<ReducTimeInfectedMild<FP>>()[entry.age_group]));
-                t_InfectedSymptoms   = static_cast<int>(std::round(
-                    t_InfectedSymptoms * params_region.template get<ReducTimeInfectedMild<FP>>()[entry.age_group]));
-
-                const FP red_fact_exp =
-                    layer == 1 ? params_region.template get<ReducExposedPartialImmunity<FP>>()[entry.age_group]
-                               : params_region.template get<ReducExposedImprovedImmunity<FP>>()[entry.age_group];
-
-                const FP red_fact_inf =
-                    layer == 1
-                        ? params_region.template get<ReducInfectedSymptomsPartialImmunity<FP>>()[entry.age_group]
-                        : params_region.template get<ReducInfectedSymptomsImprovedImmunity<FP>>()[entry.age_group];
-
-                const FP red_fact_sev =
-                    layer == 1
-                        ? params_region
-                              .template get<ReducInfectedSevereCriticalDeadPartialImmunity<FP>>()[entry.age_group]
-                        : params_region
-                              .template get<ReducInfectedSevereCriticalDeadImprovedImmunity<FP>>()[entry.age_group];
-
-                recoveredPerInfectedNoSymptoms = 1 - red_fact_inf / red_fact_exp * (1 - recoveredPerInfectedNoSymptoms);
-                severePerInfectedSymptoms      = red_fact_sev / red_fact_inf * severePerInfectedSymptoms;
-            }
-
-            if (entry.date == offset_date_by_days(date, 0)) {
-                num_InfectedSymptoms[age] += scaling_factor_inf[age] * entry.num_confirmed;
-                num_imm[age] += entry.num_confirmed;
-            }
-            if (entry.date == offset_date_by_days(date, t_InfectedNoSymptoms + days_surplus)) {
-                num_InfectedNoSymptoms[age] +=
-                    1 / (1 - recoveredPerInfectedNoSymptoms) * scaling_factor_inf[age] * entry.num_confirmed;
-                num_Exposed[age] -=
-                    1 / (1 - recoveredPerInfectedNoSymptoms) * scaling_factor_inf[age] * entry.num_confirmed;
-            }
-            if (entry.date == offset_date_by_days(date, days_surplus)) {
-                num_InfectedNoSymptoms[age] -=
-                    1 / (1 - recoveredPerInfectedNoSymptoms) * scaling_factor_inf[age] * entry.num_confirmed;
-            }
-            if (entry.date == offset_date_by_days(date, t_exposed + t_InfectedNoSymptoms + days_surplus)) {
-                num_Exposed[age] +=
-                    1 / (1 - recoveredPerInfectedNoSymptoms) * scaling_factor_inf[age] * entry.num_confirmed;
-            }
-            if (entry.date == offset_date_by_days(date, -t_InfectedSymptoms)) {
-                num_InfectedSymptoms[age] -= scaling_factor_inf[age] * entry.num_confirmed;
-                num_InfectedSevere[age] += severePerInfectedSymptoms * scaling_factor_inf[age] * entry.num_confirmed;
-            }
-            if (entry.date == offset_date_by_days(date, -t_InfectedSymptoms - t_InfectedSevere)) {
-                num_InfectedSevere[age] -= severePerInfectedSymptoms * scaling_factor_inf[age] * entry.num_confirmed;
-                num_icu[age] +=
-                    severePerInfectedSymptoms * criticalPerSevere * scaling_factor_inf[age] * entry.num_confirmed;
-            }
-            if (entry.date == offset_date_by_days(date, -t_InfectedSymptoms - t_InfectedSevere - t_InfectedCritical)) {
-                num_death[age] += entry.num_deaths;
-                num_icu[age] -=
-                    severePerInfectedSymptoms * criticalPerSevere * scaling_factor_inf[age] * entry.num_confirmed;
-            }
-            if (entry.date == offset_date_by_days(date, 0 - t_imm_interval_i)) {
-                num_imm[age] -= entry.num_confirmed;
-            }
-        }
-    }
-
-    for (size_t region_idx = 0; region_idx < vregion.size(); ++region_idx) {
-        auto region = vregion[region_idx];
-
-        auto& num_InfectedNoSymptoms = vnum_InfectedNoSymptoms[region_idx];
-        auto& num_InfectedSymptoms   = vnum_InfectedSymptoms[region_idx];
-        auto& num_Exposed            = vnum_Exposed[region_idx];
-        auto& num_InfectedSevere     = vnum_InfectedSevere[region_idx];
-        auto& num_death              = vnum_death[region_idx];
-        auto& num_icu                = vnum_icu[region_idx];
-        auto& num_timm_i             = vnum_timm_i[region_idx];
-
-        size_t num_groups = ConfirmedCasesDataEntry::age_group_names.size();
-        for (size_t i = 0; i < num_groups; i++) {
-            auto try_fix_constraints = [region, i](FP& value, FP error, auto str) {
-                if (value < error) {
-                    // this should probably return a failure
-                    // but the algorithm is not robust enough to avoid large negative
-                    // values and there are tests that rely on it
-                    log_error("{:s} for age group {:s} is {:.4f} for region {:d}, "
-                              "exceeds expected negative value.",
-                              str, ConfirmedCasesDataEntry::age_group_names[i], value, region);
-                    value = 0.0;
-                }
-                else if (value < 0) {
-                    log_info("{:s} for age group {:s} is {:.4f} for region {:d}, "
-                             "automatically corrected",
-                             str, ConfirmedCasesDataEntry::age_group_names[i], value, region);
-                    value = 0.0;
-                }
-            };
-
-            const FP tol_error = -1e-8;
-            try_fix_constraints(num_InfectedSymptoms[i], tol_error, "InfectedSymptoms");
-            try_fix_constraints(num_InfectedNoSymptoms[i], tol_error, "InfectedNoSymptoms");
-            try_fix_constraints(num_Exposed[i], tol_error, "Exposed");
-            try_fix_constraints(num_InfectedSevere[i], tol_error, "InfectedSevere");
-            try_fix_constraints(num_death[i], tol_error, "Dead");
-            try_fix_constraints(num_icu[i], tol_error, "InfectedCritical");
-            try_fix_constraints(num_timm_i[i], tol_error, "Recently Recovered or Vaccinated");
-        }
-    }
-
-    return success();
-}
+    const std::vector<ConfirmedCasesDataEntry>& case_data, const int region, Date date,
+    std::vector<ScalarType>& num_Exposed, std::vector<ScalarType>& num_InfectedNoSymptoms,
+    std::vector<ScalarType>& num_InfectedSymptoms, std::vector<ScalarType>& num_InfectedSevere,
+    std::vector<ScalarType>& num_icu, std::vector<ScalarType>& num_death, std::vector<ScalarType>& num_imm,
+    const std::vector<int>& t_Exposed, const std::vector<int>& t_InfectedNoSymptoms,
+    const std::vector<int>& t_InfectedSymptoms, const std::vector<int>& t_InfectedSevere,
+    const std::vector<int>& t_InfectedCritical, const std::vector<int>& t_imm_interval_i,
+    const std::vector<ScalarType>& mu_C_R, const std::vector<ScalarType>& mu_I_H, const std::vector<ScalarType>& mu_H_U,
+    const std::vector<ScalarType>& reduc_t_Infected, const std::vector<ScalarType>& reduc_Exposed,
+    const std::vector<ScalarType>& reduc_InfectedSymptoms, const std::vector<ScalarType>& reduc_icu_death,
+    const std::vector<ScalarType>& scaling_factor_inf, const size_t layer);
 
 /**
- * @brief Reads confirmed case data from a file and computes the distribution of cases across infection states.
- *
- * This function reads transformed RKI data from a specified file and processes the confirmed cases
- * to distribute them across different infection states and age groups.
+ * @brief Sets the confirmed cases data in the model considering different immunity layers.
  *
- * @tparam Model The type of the model used.
- * @tparam FP Floating point type (default: double).
+ * This function distributes confirmed case data across infection states for regions and age groups
+ * in the model. It considers different levels of immunity (naive, partial, and improved).
  *
- * @param[in] path Path to the file containing transformed case (rki) data.
- * @param[in] vregion Vector of region IDs to process.
- * @param[in] date Date for which the simulation starts.
- * @param[out] vnum_Exposed Output vector for the number of exposed individuals per age group and region.
- * @param[out] vnum_InfectedNoSymptoms Output vector for the number of infected individuals without symptoms.
- * @param[out] vnum_InfectedSymptoms Output vector for the number of infected individuals with symptoms.
- * @param[out] vnum_InfectedSevere Output vector for the number of severely infected individuals (Hospitalized).
- * @param[out] vnum_icu Output vector for the number of individuals in critical condition (ICU).
- * @param[out] vnum_death Output vector for the number of deaths.
- * @param[out] vnum_timm_i Output vector for the number of individuals in a temporary immunity state.
- * @param[in] model Vector of models, each representing a region and containing the parameters.
+ * @param[in, out] model Model in which the data is set.
+ * @param[in] case_data Vector of ConfirmedCasesDataEntry%s.
+ * @param[in] region Key of the region of interest.
+ * @param[in] date Date at which the data is read.
  * @param[in] scaling_factor_inf Vector of scaling factors for confirmed cases.
- * @param[in] layer Specifies the immunity layer: 0 (Naive), 1 (Partial Immunity), 2 (Improved Immunity).
+ * @param[in] immunity_population Vector containing the immunity distribution for naive, partial, and improved immunity layers.
  *
  * @return An IOResult indicating success or failure.
  */
-template <typename FP, class Model>
-IOResult<void> read_confirmed_cases_data(
-    std::string const& path, std::vector<int> const& vregion, Date date, std::vector<std::vector<FP>>& vnum_Exposed,
-    std::vector<std::vector<FP>>& vnum_InfectedNoSymptoms, std::vector<std::vector<FP>>& vnum_InfectedSymptoms,
-    std::vector<std::vector<FP>>& vnum_InfectedSevere, std::vector<std::vector<FP>>& vnum_icu,
-    std::vector<std::vector<FP>>& vnum_death, std::vector<std::vector<FP>>& vnum_timm_i,
-    const std::vector<Model>& model, const std::vector<FP>& scaling_factor_inf, const size_t layer)
-{
-    BOOST_OUTCOME_TRY(auto&& case_data, mio::read_confirmed_cases_data(path));
-    return compute_confirmed_cases_data(case_data, vnum_Exposed, vnum_InfectedNoSymptoms, vnum_InfectedSymptoms,
-                                        vnum_InfectedSevere, vnum_icu, vnum_death, vnum_timm_i, vregion, date, model,
-                                        scaling_factor_inf, layer);
-}
+IOResult<void> set_confirmed_cases_data(Model<ScalarType>& model, const std::vector<ConfirmedCasesDataEntry>& case_data,
+                                        const int region, Date date, const std::vector<ScalarType>& scaling_factor_inf,
+                                        const std::vector<std::vector<ScalarType>>& immunity_population);
 
 /**
- * @brief Sets the confirmed cases data in the model considering different immunity layers.
- *
- * This function distributes confirmed case data across infection states for regions and age groups
- * in the model. It considers different levels of immunity (naive, partial, and improved).
+ * @brief Sets confirmed case data from a file into the given models.
  *
- * @tparam Model The type of the model used.
- * @tparam FP Floating point type (default: double).
+ * This function reads transformed rki data from the specified file and calls a subfunction that distributes the 
+ * confirmed case data across different infection states for age groups in each model. 
+ * It considers naive, partial, and improved immunity layers.
  *
- * @param[in,out] model Vector of models, each representing a region, where the compartments are updated.
- * @param[in] case_data Vector of confirmed case data entries.
- * @param[in] region Vector of region IDs for which the data is processed.
- * @param[in] date Date for which the confirmed cases are set in the model.
+ * @param[in, out] model VectorRange of Node%s each containing a Model for which the confirmed cases data will be set.
+ * @param[in] path Path to the confirmed cases data file.
+ * @param[in] date Date at which the data is read.
  * @param[in] scaling_factor_inf Vector of scaling factors for confirmed cases.
  * @param[in] immunity_population Vector containing the immunity distribution for naive, partial, and improved immunity layers.
  *
  * @return An IOResult indicating success or failure.
  */
-template <typename FP, class Model>
-IOResult<void>
-set_confirmed_cases_data(std::vector<Model>& model, const std::vector<ConfirmedCasesDataEntry>& case_data,
-                         std::vector<int> const& region, Date date, const std::vector<FP>& scaling_factor_inf,
-                         const std::vector<std::vector<FP>> immunity_population)
-{
-    auto num_age_groups = (size_t)model[0].parameters.get_num_groups();
-    assert(scaling_factor_inf.size() == num_age_groups); //TODO: allow vector or scalar valued scaling factors
-    assert(ConfirmedCasesDataEntry::age_group_names.size() == num_age_groups);
-
-    std::vector<std::vector<FP>> num_InfectedSymptoms(model.size());
-    std::vector<std::vector<FP>> num_death(model.size());
-    std::vector<std::vector<FP>> num_Exposed(model.size());
-    std::vector<std::vector<FP>> num_InfectedNoSymptoms(model.size());
-    std::vector<std::vector<FP>> num_InfectedSevere(model.size());
-    std::vector<std::vector<FP>> num_icu(model.size());
-    std::vector<std::vector<FP>> num_timm1(model.size());
-    std::vector<std::vector<FP>> num_timm2(model.size());
-
-    std::vector<FP> denom_E(num_age_groups, 0.0);
-    std::vector<FP> denom_I_NS(num_age_groups, 0.0);
-    std::vector<FP> denom_I_Sy(num_age_groups, 0.0);
-    std::vector<FP> denom_I_Sev_Cr(num_age_groups, 0.0);
-
-    /*----------- Naive immunity -----------*/
-    for (size_t county = 0; county < model.size(); county++) {
-        num_InfectedSymptoms[county]   = std::vector<FP>(num_age_groups, 0.0);
-        num_death[county]              = std::vector<FP>(num_age_groups, 0.0);
-        num_Exposed[county]            = std::vector<FP>(num_age_groups, 0.0);
-        num_InfectedNoSymptoms[county] = std::vector<FP>(num_age_groups, 0.0);
-        num_InfectedSevere[county]     = std::vector<FP>(num_age_groups, 0.0);
-        num_icu[county]                = std::vector<FP>(num_age_groups, 0.0);
-        num_timm1[county]              = std::vector<FP>(num_age_groups, 0.0);
-        num_timm2[county]              = std::vector<FP>(num_age_groups, 0.0);
-        for (size_t group = 0; group < num_age_groups; group++) {
-            // calculate the denominators to split the reported case numbers to the different immunity layers.
-            denom_E[group] =
-                1 / (immunity_population[0][group] +
-                     immunity_population[1][group] *
-                         model[county].parameters.template get<ReducExposedPartialImmunity<FP>>()[(AgeGroup)group] +
-                     immunity_population[2][group] *
-                         model[county].parameters.template get<ReducExposedImprovedImmunity<FP>>()[(AgeGroup)group]);
-
-            denom_I_NS[group] =
-                1 / (immunity_population[0][group] +
-                     immunity_population[1][group] *
-                         model[county].parameters.template get<ReducExposedPartialImmunity<FP>>()[(AgeGroup)group] +
-                     immunity_population[2][group] *
-                         model[county].parameters.template get<ReducExposedImprovedImmunity<FP>>()[(AgeGroup)group]);
-
-            denom_I_Sy[group] =
-                1 / (immunity_population[0][group] +
-                     immunity_population[1][group] *
-                         model[county]
-                             .parameters.template get<ReducInfectedSymptomsPartialImmunity<FP>>()[(AgeGroup)group] +
-                     immunity_population[2][group] *
-                         model[county]
-                             .parameters.template get<ReducInfectedSymptomsImprovedImmunity<FP>>()[(AgeGroup)group]);
-
-            denom_I_Sev_Cr[group] =
-                1 / (immunity_population[0][group] +
-                     immunity_population[1][group] *
-                         model[county].parameters.template get<ReducInfectedSevereCriticalDeadPartialImmunity<FP>>()[(
-                             AgeGroup)group] +
-                     immunity_population[2][group] *
-                         model[county].parameters.template get<ReducInfectedSevereCriticalDeadImprovedImmunity<FP>>()[(
-                             AgeGroup)group]);
-        }
-    }
-
-    BOOST_OUTCOME_TRY(compute_confirmed_cases_data(case_data, num_Exposed, num_InfectedNoSymptoms, num_InfectedSymptoms,
-                                                   num_InfectedSevere, num_icu, num_death, num_timm1, region, date,
-                                                   model, scaling_factor_inf, 0));
-
-    for (size_t county = 0; county < model.size(); county++) {
-        size_t num_groups = (size_t)model[county].parameters.get_num_groups();
-        for (size_t i = 0; i < num_groups; i++) {
-            model[county].populations[{AgeGroup(i), InfectionState::ExposedNaive}] =
-                immunity_population[0][i] * denom_E[i] * num_Exposed[county][i];
-            model[county].populations[{AgeGroup(i), InfectionState::InfectedNoSymptomsNaive}] =
-                immunity_population[0][i] * denom_I_NS[i] * num_InfectedNoSymptoms[county][i];
-            model[county].populations[{AgeGroup(i), InfectionState::InfectedNoSymptomsNaiveConfirmed}] = 0;
-            model[county].populations[{AgeGroup(i), InfectionState::InfectedSymptomsNaive}] =
-                immunity_population[0][i] * denom_I_Sy[i] * num_InfectedSymptoms[county][i];
-            model[county].populations[{AgeGroup(i), InfectionState::InfectedSymptomsNaiveConfirmed}] = 0;
-            model[county].populations[{AgeGroup(i), InfectionState::InfectedSevereNaive}] =
-                immunity_population[0][i] * denom_I_Sev_Cr[i] * num_InfectedSevere[county][i];
-            // Only set the number of ICU patients here, if the date is not available in the data.
-            if (!is_divi_data_available(date)) {
-                model[county].populations[{AgeGroup(i), InfectionState::InfectedCriticalNaive}] =
-                    immunity_population[0][i] * denom_I_Sev_Cr[i] * num_icu[county][i];
-            }
-        }
-        if (std::accumulate(num_InfectedSymptoms[county].begin(), num_InfectedSymptoms[county].end(), FP(0.0),
-                            [](const FP& a, const FP& b) {
-                                return evaluate_intermediate<FP>(a + b);
-                            }) == 0) {
-            log_warning(
-                "No infections for unvaccinated reported on date {} for region {}. Population data has not been set.",
-                date, region[county]);
-        }
-    }
-
-    /*----------- PARTIAL Immunity -----------*/
-    for (size_t county = 0; county < model.size(); county++) {
-        num_InfectedSymptoms[county]   = std::vector<FP>(num_age_groups, 0.0);
-        num_death[county]              = std::vector<FP>(num_age_groups, 0.0);
-        num_Exposed[county]            = std::vector<FP>(num_age_groups, 0.0);
-        num_InfectedNoSymptoms[county] = std::vector<FP>(num_age_groups, 0.0);
-        num_InfectedSevere[county]     = std::vector<FP>(num_age_groups, 0.0);
-        num_icu[county]                = std::vector<FP>(num_age_groups, 0.0);
-    }
-
-    BOOST_OUTCOME_TRY(compute_confirmed_cases_data(case_data, num_Exposed, num_InfectedNoSymptoms, num_InfectedSymptoms,
-                                                   num_InfectedSevere, num_icu, num_death, num_timm1, region, date,
-                                                   model, scaling_factor_inf, 1));
-
-    for (size_t county = 0; county < model.size(); county++) {
-        size_t num_groups = (size_t)model[county].parameters.get_num_groups();
-        for (size_t i = 0; i < num_groups; i++) {
-            model[county].populations[{AgeGroup(i), InfectionState::ExposedPartialImmunity}] =
-                immunity_population[1][i] *
-                model[county].parameters.template get<ReducExposedPartialImmunity<FP>>()[(AgeGroup)i] * denom_E[i] *
-                num_Exposed[county][i];
-            model[county].populations[{AgeGroup(i), InfectionState::InfectedNoSymptomsPartialImmunity}] =
-                immunity_population[1][i] *
-                model[county].parameters.template get<ReducExposedPartialImmunity<FP>>()[(AgeGroup)i] * denom_I_NS[i] *
-                num_InfectedNoSymptoms[county][i];
-            model[county].populations[{AgeGroup(i), InfectionState::InfectedNoSymptomsPartialImmunityConfirmed}] = 0;
-            model[county].populations[{AgeGroup(i), InfectionState::InfectedSymptomsPartialImmunity}] =
-                immunity_population[1][i] *
-                model[county].parameters.template get<ReducInfectedSymptomsPartialImmunity<FP>>()[(AgeGroup)i] *
-                denom_I_Sy[i] * num_InfectedSymptoms[county][i];
-            model[county].populations[{AgeGroup(i), InfectionState::InfectedSymptomsPartialImmunityConfirmed}] = 0;
-            model[county].populations[{AgeGroup(i), InfectionState::InfectedSeverePartialImmunity}] =
-                immunity_population[1][i] *
-                model[county]
-                    .parameters.template get<ReducInfectedSevereCriticalDeadPartialImmunity<FP>>()[(AgeGroup)i] *
-                denom_I_Sev_Cr[i] * num_InfectedSevere[county][i];
-            // Only set the number of ICU patients here, if the date is not available in the data.
-            if (!is_divi_data_available(date)) {
-                model[county].populations[{AgeGroup(i), InfectionState::InfectedCriticalPartialImmunity}] =
-                    immunity_population[1][i] *
-                    model[county]
-                        .parameters.template get<ReducInfectedSevereCriticalDeadPartialImmunity<FP>>()[(AgeGroup)i] *
-                    denom_I_Sev_Cr[i] * num_icu[county][i];
-            }
-            // the += is necessary because we already set the previous vaccinated individuals
-            model[county].populations[{AgeGroup(i), InfectionState::TemporaryImmunePartialImmunity}] +=
-                immunity_population[1][i] *
-                model[county].parameters.template get<ReducExposedPartialImmunity<FP>>()[(AgeGroup)i] * denom_E[i] *
-                num_timm1[county][i];
-        }
-        if (std::accumulate(num_InfectedSymptoms[county].begin(), num_InfectedSymptoms[county].end(), FP(0.0),
-                            [](const FP& a, const FP& b) {
-                                return evaluate_intermediate<FP>(a + b);
-                            }) == 0) {
-
-            log_warning("No infections for partially vaccinated reported on date {} for region {}. "
-                        "Population data has not been set.",
-                        date, region[county]);
-        }
-    }
-
-    /*----------- Improved Immunity -----------*/
-    for (size_t county = 0; county < model.size(); county++) {
-        num_InfectedSymptoms[county]   = std::vector<FP>(num_age_groups, 0.0);
-        num_death[county]              = std::vector<FP>(num_age_groups, 0.0);
-        num_Exposed[county]            = std::vector<FP>(num_age_groups, 0.0);
-        num_InfectedNoSymptoms[county] = std::vector<FP>(num_age_groups, 0.0);
-        num_InfectedSevere[county]     = std::vector<FP>(num_age_groups, 0.0);
-        num_icu[county]                = std::vector<FP>(num_age_groups, 0.0);
-    }
-
-    BOOST_OUTCOME_TRY(compute_confirmed_cases_data(case_data, num_Exposed, num_InfectedNoSymptoms, num_InfectedSymptoms,
-                                                   num_InfectedSevere, num_icu, num_death, num_timm2, region, date,
-                                                   model, scaling_factor_inf, 2));
-
-    for (size_t county = 0; county < model.size(); county++) {
-        size_t num_groups = (size_t)model[county].parameters.get_num_groups();
-        for (size_t i = 0; i < num_groups; i++) {
-            model[county].populations[{AgeGroup(i), InfectionState::ExposedImprovedImmunity}] =
-                immunity_population[2][i] *
-                model[county].parameters.template get<ReducExposedImprovedImmunity<FP>>()[(AgeGroup)i] * denom_E[i] *
-                num_Exposed[county][i];
-            model[county].populations[{AgeGroup(i), InfectionState::InfectedNoSymptomsImprovedImmunity}] =
-                immunity_population[2][i] *
-                model[county].parameters.template get<ReducExposedImprovedImmunity<FP>>()[(AgeGroup)i] * denom_I_NS[i] *
-                num_InfectedNoSymptoms[county][i];
-            model[county].populations[{AgeGroup(i), InfectionState::InfectedNoSymptomsImprovedImmunityConfirmed}] = 0;
-            model[county].populations[{AgeGroup(i), InfectionState::InfectedSymptomsImprovedImmunity}] =
-                immunity_population[2][i] *
-                model[county].parameters.template get<ReducInfectedSymptomsImprovedImmunity<FP>>()[(AgeGroup)i] *
-                denom_I_Sy[i] * num_InfectedSymptoms[county][i];
-            model[county].populations[{AgeGroup(i), InfectionState::InfectedSymptomsImprovedImmunityConfirmed}] = 0;
-            model[county].populations[{AgeGroup(i), InfectionState::InfectedSevereImprovedImmunity}] =
-                immunity_population[2][i] *
-                model[county]
-                    .parameters.template get<ReducInfectedSevereCriticalDeadImprovedImmunity<FP>>()[(AgeGroup)i] *
-                denom_I_Sev_Cr[i] * num_InfectedSevere[county][i];
-            // Only set the number of ICU patients here, if the date is not available in the data.
-            if (!is_divi_data_available(date)) {
-                model[county].populations[{AgeGroup(i), InfectionState::InfectedCriticalImprovedImmunity}] =
-                    immunity_population[2][i] *
-                    model[county]
-                        .parameters.template get<ReducInfectedSevereCriticalDeadImprovedImmunity<FP>>()[(AgeGroup)i] *
-                    denom_I_Sev_Cr[i] * num_icu[county][i];
-            }
-
-            // the += is necessary because we already set the previous vaccinated individuals
-            model[county].populations[{AgeGroup(i), InfectionState::TemporaryImmuneImprovedImmunity}] +=
-                immunity_population[2][i] *
-                model[county].parameters.template get<ReducExposedImprovedImmunity<FP>>()[(AgeGroup)i] * denom_E[i] *
-                num_timm2[county][i];
-        }
-        if (std::accumulate(num_InfectedSymptoms[county].begin(), num_InfectedSymptoms[county].end(), FP(0.0),
-                            [](const FP& a, const FP& b) {
-                                return evaluate_intermediate<FP>(a + b);
-                            }) == 0) {
-            log_warning("No infections for vaccinated reported on date {} for region {}. "
-                        "Population data has not been set.",
-                        date, region[county]);
-        }
-    }
-    return success();
-}
+IOResult<void> set_confirmed_cases_data(const mio::VectorRange<Node<Model<ScalarType>>>& model, const std::string& path,
+                                        Date date, const std::vector<ScalarType>& scaling_factor_inf,
+                                        const std::vector<std::vector<ScalarType>>& immunity_population);
 
 /**
- * @brief Reads confirmed case data from a file and sets it in the model.
- *
- * This function reads transformed RKI data from the specified file and distributes the confirmed case data
- * across different infection states for regions and age groups in the model. It considers naive, partial,
- * and improved immunity layers.
- *
- * @tparam Model The type of the model used.
- * @tparam FP Floating point type (default: double).
+ * @brief Sets the population data for the given model based on the provided population distribution and immunity levels.
+ * @param[in, out] model Model in which the data is set.
+ * @param[in] num_population Vector of population data for the region of interest.
+ * @param[in] region Key of the region of interest.
+ * @param[in] immunity_population A 2D vector where each row represents the immunity distribution for a specific region
+ *                                 across different levels of immunity (e.g., naive, partial, improved immunity).
  *
- * @param[in,out] model Vector of models, each representing a region, where the compartments are updated.
- * @param[in] path Path to the file containing case (RKI) data.
- * @param[in] region Vector of region IDs for which the data is processed.
- * @param[in] date Date for which the confirmed cases are set in the model.
- * @param[in] scaling_factor_inf Vector of scaling factors for confirmed cases.
- * @param[in] immunity_population Vector containing the immunity distribution for naive, partial, and improved immunity layers.
+ * @return An IOResult indicating success or failure.
+ */
+IOResult<void> set_population_data(Model<ScalarType>& model, const std::vector<ScalarType>& num_population,
+                                   const int region, const std::vector<std::vector<ScalarType>>& immunity_population);
+
+/**
+ * @brief Reads population data from a file and sets it for the each given model.
+ * @param[in, out] model VectorRange of Node%s containing a Model for which the confirmed cases data will be set.
+ * @param[in] path Path to population data file.
+ * @param[in] immunity_population A 2D vector specifying immunity for each age group and immunity layer.
  *
  * @return An IOResult indicating success or failure.
  */
-template <typename FP, class Model>
-IOResult<void> set_confirmed_cases_data(std::vector<Model>& model, const std::string& path,
-                                        std::vector<int> const& region, Date date,
-                                        const std::vector<FP>& scaling_factor_inf,
-                                        const std::vector<std::vector<FP>> immunity_population)
-{
-    BOOST_OUTCOME_TRY(auto&& case_data, mio::read_confirmed_cases_data(path));
-    BOOST_OUTCOME_TRY(
-        set_confirmed_cases_data(model, case_data, region, date, scaling_factor_inf, immunity_population));
-    return success();
-}
+IOResult<void> set_population_data(const mio::VectorRange<Node<Model<ScalarType>>>& model, const std::string& path,
+                                   const std::vector<std::vector<ScalarType>>& immunity_population);
+
+/**
+ * @brief Sets ICU data into a Model, distributed across age groups.
+ * @param[in, out] model Model in which the data is set.
+ * @param[in] num_icu ICU data for the region of interest.
+ * @param[in] scaling_factor_icu Scaling factor for reported ICU cases.
+ */
+IOResult<void> set_divi_data(Model<ScalarType>& model, const ScalarType num_icu, ScalarType scaling_factor_icu);
 
 /**
  * @brief Sets ICU data from DIVI data into the a vector of models, distributed across age groups.
- *
+ * 
  * This function reads DIVI data from a file, computes the number of individuals in critical condition (ICU)
  * for each region, and sets these values in the model. The ICU cases are distributed across age groups
  * using the transition probabilities from severe to critical.
- *
- * @tparam Model The type of the model used.
- * @tparam FP Floating point type (default: double).
- *
- * @param[in,out] model Vector of models, each representing a region, where the ICU population is updated.
- * @param[in] path Path to the file containing DIVI data.
- * @param[in] vregion Vector of region IDs for which the data is computed.
- * @param[in] date Date for which the ICU data is computed.
+ * 
+ * @param[in, out] model VectorRange of Node%s each containing a Model for which the confirmed cases data will be set.
+ * @param[in] path Path to transformed DIVI file.
+ * @param[in] date Date at which the data is read.
  * @param[in] scaling_factor_icu Scaling factor for reported ICU cases.
- *
- * @return An IOResult indicating success or failure.
  */
-template <typename FP, class Model>
-IOResult<void> set_divi_data(std::vector<Model>& model, const std::string& path, const std::vector<int>& vregion,
-                             Date date, FP scaling_factor_icu)
-{
-    // DIVI dataset will no longer be updated from CW29 2024 on.
-    if (!is_divi_data_available(date)) {
-        log_warning("No DIVI data available for date: {}. "
-                    "ICU compartment will be set based on Case data.",
-                    date);
-        return success();
-    }
-    std::vector<FP> sum_mu_I_U(vregion.size(), 0);
-    std::vector<std::vector<FP>> mu_I_U{model.size()};
-    for (size_t region = 0; region < vregion.size(); region++) {
-        auto num_groups = model[region].parameters.get_num_groups();
-        for (auto i = AgeGroup(0); i < num_groups; i++) {
-            sum_mu_I_U[region] += model[region].parameters.template get<CriticalPerSevere<FP>>()[i] *
-                                  model[region].parameters.template get<SeverePerInfectedSymptoms<FP>>()[i];
-            mu_I_U[region].push_back(model[region].parameters.template get<CriticalPerSevere<FP>>()[i] *
-                                     model[region].parameters.template get<SeverePerInfectedSymptoms<FP>>()[i]);
-        }
-    }
-    std::vector<FP> num_icu(model.size(), 0.0);
-    BOOST_OUTCOME_TRY(read_divi_data(path, vregion, date, num_icu));
-
-    for (size_t region = 0; region < vregion.size(); region++) {
-        auto num_groups = model[region].parameters.get_num_groups();
-        for (auto i = AgeGroup(0); i < num_groups; i++) {
-            model[region].populations[{i, InfectionState::InfectedCriticalNaive}] =
-                scaling_factor_icu * num_icu[region] * mu_I_U[region][(size_t)i] / sum_mu_I_U[region];
-        }
-    }
-
-    return success();
-}
+IOResult<void> set_divi_data(const mio::VectorRange<Node<Model<ScalarType>>>& model, const std::string& path, Date date,
+                             ScalarType scaling_factor_icu);
 
 /**
- * @brief Sets the population data for the given models based on the provided population distribution and immunity levels.
- *
- * @tparam Model The type of the model used.
+ * @brief Sets vaccination data into a Model using provided vaccination (partial, full, and booster) data.
  * @tparam FP Floating point type (default: double).
- *
- * @param[in,out] model A vector of models for which population data will be set.
- * @param[in] num_population A 2D vector where each row represents the age group population distribution for a specific region.
- * @param[in] vregion A vector of region identifiers corresponding to the population data.
- * @param[in] immunity_population A 2D vector where each row represents the immunity distribution for a specific region
- *                                 across different levels of immunity (e.g., naive, partial, improved immunity).
- *
+ * @param[in, out] model Model in which the data is set.
+ * @param[in] vacc_data Vector of VaccinationDataEntry objects containing the vaccination data.
+ * @param[in] date Start date for the simulation.
+ * @param[in] num_days Number of days for which the simulation is run.
+ * @param[in] max_date
+ * @param[in] days_until_effective_n
+ * @param[in] days_until_effective_pi
+ * @param[in] days_until_effective_ii
+ * 
  * @return An IOResult indicating success or failure.
  */
-template <typename FP, class Model>
-IOResult<void> set_population_data(std::vector<Model>& model, const std::vector<std::vector<FP>>& num_population,
-                                   const std::vector<int>& vregion,
-                                   const std::vector<std::vector<FP>> immunity_population)
+template <typename FP>
+IOResult<void> set_vaccination_data(Model<FP>& model, const VaccinationDataEntry& vacc_data_entry, Date date,
+                                    int num_days, Date max_date, const int days_until_effective_n,
+                                    const int days_until_effective_pi, const int days_until_effective_ii)
 {
-    for (size_t region = 0; region < vregion.size(); region++) {
-        if (std::accumulate(num_population[region].begin(), num_population[region].end(), FP(0.0),
-                            [](const FP& a, const FP& b) {
-                                return evaluate_intermediate<FP>(a + b);
-                            }) > 0.0) {
-            auto num_groups = model[region].parameters.get_num_groups();
-            for (auto i = AgeGroup(0); i < num_groups; i++) {
-
-                FP SN  = num_population[region][size_t(i)] * immunity_population[0][size_t(i)];
-                FP SPI = num_population[region][size_t(i)] * immunity_population[1][size_t(i)];
-                FP SII = num_population[region][size_t(i)] - SN - SPI;
-
-                model[region].populations[{i, InfectionState::SusceptibleImprovedImmunity}] = std::max(
-                    0.0,
-                    FP(SII -
-                       (model[region].populations[{i, InfectionState::ExposedImprovedImmunity}] +
-                        model[region].populations[{i, InfectionState::InfectedNoSymptomsImprovedImmunity}] +
-                        model[region].populations[{i, InfectionState::InfectedNoSymptomsImprovedImmunityConfirmed}] +
-                        model[region].populations[{i, InfectionState::InfectedSymptomsImprovedImmunity}] +
-                        model[region].populations[{i, InfectionState::InfectedSymptomsImprovedImmunityConfirmed}] +
-                        model[region].populations[{i, InfectionState::InfectedSevereImprovedImmunity}] +
-                        model[region].populations[{i, InfectionState::InfectedCriticalImprovedImmunity}] +
-                        model[region].populations[{i, InfectionState::DeadImprovedImmunity}] +
-                        model[region].populations[{i, InfectionState::TemporaryImmuneImprovedImmunity}])));
-
-                model[region].populations[{i, InfectionState::SusceptiblePartialImmunity}] = std::max(
-                    0.0,
-                    SPI - model[region].populations[{i, InfectionState::ExposedPartialImmunity}] -
-                        model[region].populations[{i, InfectionState::InfectedNoSymptomsPartialImmunity}] -
-                        model[region].populations[{i, InfectionState::InfectedNoSymptomsPartialImmunityConfirmed}] -
-                        model[region].populations[{i, InfectionState::InfectedSymptomsPartialImmunity}] -
-                        model[region].populations[{i, InfectionState::InfectedSymptomsPartialImmunityConfirmed}] -
-                        model[region].populations[{i, InfectionState::InfectedSeverePartialImmunity}] -
-                        model[region].populations[{i, InfectionState::InfectedCriticalPartialImmunity}] -
-                        model[region].populations[{i, InfectionState::DeadPartialImmunity}] -
-                        model[region].populations[{i, InfectionState::TemporaryImmunePartialImmunity}]);
+    auto date_df = vacc_data_entry.date;
+    AgeGroup age = vacc_data_entry.age_group;
+
+    // get daily vaccinations for each layer
+    for (size_t d = 0; d < (size_t)num_days + 1; ++d) {
+        auto offset_first_date = offset_date_by_days(date, (int)d - days_until_effective_n);
+        if (max_date >= offset_first_date) {
+            if (date_df == offset_first_date) {
+                model.parameters.template get<DailyPartialVaccinations<FP>>()[{age, SimulationDay(d)}] =
+                    vacc_data_entry.num_vaccinations_partial;
+            }
+        }
+        else {
+            if (date_df == offset_first_date) {
+                model.parameters.template get<DailyPartialVaccinations<FP>>()[{age, SimulationDay(d)}] = 0;
+            }
+        }
 
-                model[region].populations.template set_difference_from_group_total<AgeGroup>(
-                    {i, InfectionState::SusceptibleNaive}, num_population[region][size_t(i)]);
+        auto offset_full_date = offset_date_by_days(date, (int)d - days_until_effective_pi);
+        if (max_date >= offset_full_date) {
+            if (date_df == offset_full_date) {
+                model.parameters.template get<DailyFullVaccinations<FP>>()[{age, SimulationDay(d)}] =
+                    vacc_data_entry.num_vaccinations_completed;
+            }
+        }
+        else {
+            if (date_df == offset_first_date) {
+                model.parameters.template get<DailyFullVaccinations<FP>>()[{age, SimulationDay(d)}] = 0;
             }
+        }
 
-            for (auto i = AgeGroup(0); i < AgeGroup(6); i++) {
-                for (auto j = Index<InfectionState>(0); j < InfectionState::Count; ++j) {
-                    if (model[region].populations[{i, j}] < 0) {
-                        log_warning("Compartment at age group {}, infection state {}, is negative: {}", size_t(i),
-                                    size_t(j), model[region].populations[{i, j}]);
-                    }
-                }
+        auto offset_booster_date = offset_date_by_days(date, (int)d - days_until_effective_ii);
+        if (max_date >= offset_booster_date) {
+            if (date_df == offset_booster_date) {
+                model.parameters.template get<DailyBoosterVaccinations<FP>>()[{age, SimulationDay(d)}] =
+                    vacc_data_entry.num_vaccinations_refreshed_first +
+                    vacc_data_entry.num_vaccinations_refreshed_additional;
             }
         }
         else {
-            log_warning("No population data available for region " + std::to_string(region) +
-                        ". Population data has not been set.");
+            if (date_df == offset_first_date) {
+                model.parameters.template get<DailyBoosterVaccinations<FP>>()[{age, SimulationDay(d)}] = 0;
+            }
         }
     }
-
-    return success();
-}
-
-/**
- * @brief Reads population data from a file and sets it for the each given model.
- *
- * @tparam Model The type of the model used.
- *
- * @param[in,out] model A vector of models for which population data will be set.
- * @param[in] path The file path to the population data.
- * @param[in] vregion A vector of region identifiers corresponding to the population data.
- * @param[in] immunity_population A 2D vector where each row represents the immunity distribution for a specific region
- *                                 across different levels of immunity (e.g., naive, partial, improved).
- *
- * @return An IOResult indicating success or failure.
- */
-template <class Model>
-IOResult<void> set_population_data(std::vector<Model>& model, const std::string& path, const std::vector<int>& vregion,
-                                   const std::vector<std::vector<double>> immunity_population)
-{
-    BOOST_OUTCOME_TRY(auto&& num_population, mio::read_population_data(path, vregion));
-    BOOST_OUTCOME_TRY(set_population_data(model, num_population, vregion, immunity_population));
     return success();
 }
 
 /**
  * @brief Sets vaccination data for the given models using provided vaccination (partial, full, and booster) data.
- *
- *
+ * 
  * @tparam FP Floating point type (default: double).
- *
  * @param[in,out] model A vector of models for which vaccination data will be set.
  * @param[in] vacc_data A vector of VaccinationDataEntry objects containing the vaccination data.
  * @param[in] date The starting date for the simulation.
- * @param[in] vregion A vector of region identifiers corresponding to the vaccination data.
  * @param[in] num_days The number of days for which the simulation runs.
  *
  * @return An IOResult indicating success or failure.
  */
 template <typename FP>
-IOResult<void> set_vaccination_data(std::vector<Model<FP>>& model, const std::vector<VaccinationDataEntry>& vacc_data,
-                                    Date date, const std::vector<int>& vregion, int num_days)
+IOResult<void> set_vaccination_data(Model<FP>& model, const std::vector<VaccinationDataEntry>& vacc_data, Date date,
+                                    int num_days)
 {
-    auto num_groups = model[0].parameters.get_num_groups();
-
-    auto days_until_effective_n =
-        (int)(double)model[0].parameters.template get<DaysUntilEffectivePartialVaccination<FP>>()[AgeGroup(0)];
-    auto days_until_effective_pi =
-        (int)(double)model[0].parameters.template get<DaysUntilEffectiveImprovedVaccination<FP>>()[AgeGroup(0)];
-    auto days_until_effective_ii =
-        (int)(double)model[0].parameters.template get<DaysUntilEffectiveBoosterImmunity<FP>>()[AgeGroup(0)];
-    // iterate over regions (e.g., counties)
-    for (size_t i = 0; i < model.size(); ++i) {
-        // iterate over age groups in region
-        for (auto g = AgeGroup(0); g < num_groups; ++g) {
-
-            model[i].parameters.template get<DailyPartialVaccinations<FP>>().resize(SimulationDay(num_days + 1));
-            model[i].parameters.template get<DailyFullVaccinations<FP>>().resize(SimulationDay(num_days + 1));
-            model[i].parameters.template get<DailyBoosterVaccinations<FP>>().resize(SimulationDay(num_days + 1));
-            for (auto d = SimulationDay(0); d < SimulationDay(num_days + 1); ++d) {
-                model[i].parameters.template get<DailyPartialVaccinations<FP>>()[{g, d}] = 0.0;
-                model[i].parameters.template get<DailyFullVaccinations<FP>>()[{g, d}]    = 0.0;
-                model[i].parameters.template get<DailyBoosterVaccinations<FP>>()[{g, d}] = 0.0;
-            }
-        }
-    }
+    using std::floor;
 
     auto max_date_entry = std::max_element(vacc_data.begin(), vacc_data.end(), [](auto&& a, auto&& b) {
         return a.date < b.date;
@@ -775,66 +258,17 @@ IOResult<void> set_vaccination_data(std::vector<Model<FP>>& model, const std::ve
                     min_date, max_date);
     }
 
-    for (auto&& vacc_data_entry : vacc_data) {
-        auto it      = std::find_if(vregion.begin(), vregion.end(), [&vacc_data_entry](auto&& r) {
-            return r == 0 || (vacc_data_entry.county_id && vacc_data_entry.county_id == regions::CountyId(r)) ||
-                   (vacc_data_entry.state_id && vacc_data_entry.state_id == regions::StateId(r)) ||
-                   (vacc_data_entry.district_id && vacc_data_entry.district_id == regions::DistrictId(r));
-        });
-        auto date_df = vacc_data_entry.date;
-        if (it != vregion.end()) {
-            auto region_idx = size_t(it - vregion.begin());
-            AgeGroup age    = vacc_data_entry.age_group;
-
-            // get daily vaccinations for each layer
-            for (size_t d = 0; d < (size_t)num_days + 1; ++d) {
-                auto offset_first_date = offset_date_by_days(date, (int)d - days_until_effective_n);
-                if (max_date >= offset_first_date) {
-                    if (date_df == offset_first_date) {
-                        model[region_idx]
-                            .parameters.template get<DailyPartialVaccinations<FP>>()[{age, SimulationDay(d)}] =
-                            vacc_data_entry.num_vaccinations_partial;
-                    }
-                }
-                else {
-                    if (date_df == offset_first_date) {
-                        model[region_idx]
-                            .parameters.template get<DailyPartialVaccinations<FP>>()[{age, SimulationDay(d)}] = 0;
-                    }
-                }
-
-                auto offset_full_date = offset_date_by_days(date, (int)d - days_until_effective_pi);
-                if (max_date >= offset_full_date) {
-                    if (date_df == offset_full_date) {
-                        model[region_idx]
-                            .parameters.template get<DailyFullVaccinations<FP>>()[{age, SimulationDay(d)}] =
-                            vacc_data_entry.num_vaccinations_completed;
-                    }
-                }
-                else {
-                    if (date_df == offset_first_date) {
-                        model[region_idx]
-                            .parameters.template get<DailyFullVaccinations<FP>>()[{age, SimulationDay(d)}] = 0;
-                    }
-                }
+    auto days_until_effective_n = static_cast<int>(
+        floor(static_cast<FP>(model.parameters.template get<DaysUntilEffectivePartialVaccination<FP>>()[AgeGroup(0)])));
+    auto days_until_effective_pi = static_cast<int>(floor(
+        static_cast<FP>(model.parameters.template get<DaysUntilEffectiveImprovedVaccination<FP>>()[AgeGroup(0)])));
+    auto days_until_effective_ii = static_cast<int>(
+        floor(static_cast<FP>(model.parameters.template get<DaysUntilEffectiveBoosterImmunity<FP>>()[AgeGroup(0)])));
 
-                auto offset_booster_date = offset_date_by_days(date, (int)d - days_until_effective_ii);
-                if (max_date >= offset_booster_date) {
-                    if (date_df == offset_booster_date) {
-                        model[region_idx]
-                            .parameters.template get<DailyBoosterVaccinations<FP>>()[{age, SimulationDay(d)}] =
-                            vacc_data_entry.num_vaccinations_refreshed_first +
-                            vacc_data_entry.num_vaccinations_refreshed_additional;
-                    }
-                }
-                else {
-                    if (date_df == offset_first_date) {
-                        model[region_idx]
-                            .parameters.template get<DailyBoosterVaccinations<FP>>()[{age, SimulationDay(d)}] = 0;
-                    }
-                }
-            }
-        }
+    for (auto&& vacc_data_entry : vacc_data) {
+        BOOST_OUTCOME_TRY(set_vaccination_data<FP>(model, vacc_data_entry, date, num_days, max_date,
+                                                   days_until_effective_n, days_until_effective_pi,
+                                                   days_until_effective_ii));
     }
     return success();
 }
@@ -846,260 +280,121 @@ IOResult<void> set_vaccination_data(std::vector<Model<FP>>& model, const std::ve
  * (partial, full, and booster) to each region and age group in the models.
  *
  * @tparam FP Floating point type (default: double).
- *
- * @param[in,out] model A vector of models for which vaccination data will be set.
- * @param[in] path The file path to the vaccination data.
+ * @param[in,out] model VectorRange of Node%s each containing a Model for which vaccination data will be set.
+ * @param[in] path Path to vaccination data file.
  * @param[in] date The starting date for the simulation.
- * @param[in] vregion A vector of region identifiers corresponding to the vaccination data.
  * @param[in] num_days The number of days for which the simulation runs.
- *
+ * 
  * @return An IOResult indicating success or failure.
  */
 template <typename FP>
-IOResult<void> set_vaccination_data(std::vector<Model<FP>>& model, const std::string& path, Date date,
-                                    const std::vector<int>& vregion, int num_days)
+IOResult<void> set_vaccination_data(const mio::VectorRange<Node<Model<FP>>>& model, const std::string& path, Date date,
+                                    int num_days)
 {
+    // Set vaccination data to 0 for all models
+    for (auto& m : model) {
+        m.property.parameters.template get<DailyPartialVaccinations<FP>>().resize(SimulationDay(num_days + 1));
+        m.property.parameters.template get<DailyFullVaccinations<FP>>().resize(SimulationDay(num_days + 1));
+        m.property.parameters.template get<DailyBoosterVaccinations<FP>>().resize(SimulationDay(num_days + 1));
+        for (auto d = SimulationDay(0); d < SimulationDay(num_days + 1); ++d) {
+            for (auto a = AgeGroup(0); a < m.property.parameters.get_num_groups(); ++a) {
+                m.property.parameters.template get<DailyPartialVaccinations<FP>>()[{a, d}] = 0.0;
+                m.property.parameters.template get<DailyFullVaccinations<FP>>()[{a, d}]    = 0.0;
+                m.property.parameters.template get<DailyBoosterVaccinations<FP>>()[{a, d}] = 0.0;
+            }
+        }
+    }
+
     // Check if vaccination data is available for the given date range
     auto end_date = offset_date_by_days(date, num_days);
     if (!is_vaccination_data_available(date, end_date)) {
         log_warning("No vaccination data available in range from {} to {}. "
                     "Vaccination data will be set to 0.",
                     date, end_date);
-        // Set vaccination data to 0 for all models
-        for (auto& m : model) {
-            m.parameters.template get<DailyPartialVaccinations<FP>>().resize(SimulationDay(num_days + 1));
-            m.parameters.template get<DailyFullVaccinations<FP>>().resize(SimulationDay(num_days + 1));
-            m.parameters.template get<DailyBoosterVaccinations<FP>>().resize(SimulationDay(num_days + 1));
-            for (auto d = SimulationDay(0); d < SimulationDay(num_days + 1); ++d) {
-                for (auto a = AgeGroup(0); a < m.parameters.get_num_groups(); ++a) {
-                    m.parameters.template get<DailyPartialVaccinations<FP>>()[{a, d}] = 0.0;
-                    m.parameters.template get<DailyFullVaccinations<FP>>()[{a, d}]    = 0.0;
-                    m.parameters.template get<DailyBoosterVaccinations<FP>>()[{a, d}] = 0.0;
-                }
-            }
-        }
         return success();
     }
-    BOOST_OUTCOME_TRY(auto&& vacc_data, read_vaccination_data(path));
-    BOOST_OUTCOME_TRY(set_vaccination_data(model, vacc_data, date, vregion, num_days));
-    return success();
-}
-
-} // namespace details
-
-#ifdef MEMILIO_HAS_HDF5
-
-/**
- * @brief Exports extrapolated real-world data time series for specified regions.
- *
- * This function generates and exports time series data based on the extrapolation and approximation methods
- * used to initialize the model with real-world data. The resulting data represents the initialized states of
- * the model over the specified time range.
- *
- * @tparam Model Type of the model used.
- *
- * @param[in] models A vector of models for which the extrapolated data is set.
- * @param[in] results_dir Path to the directory where the extrapolated results will be saved in a h5 file.
- * @param[in] counties A vector of region identifiers for which the time series will be exported.
- * @param[in] date The starting date of the time series.
- * @param[in] scaling_factor_inf A vector of scaling factors applied to confirmed cases.
- * @param[in] scaling_factor_icu A scaling factor applied to ICU cases.
- * @param[in] num_days The number of days for which will be extrapolated.
- * @param[in] divi_data_path Path to the DIVI ICU data file.
- * @param[in] confirmed_cases_path Path to the confirmed cases data file.
- * @param[in] population_data_path Path to the population data file.
- * @param[in] immunity_population A vector of vectors specifying immunity for each age group and immunity layer.
- * @param[in] vaccination_data_path Path to the vaccination data file (optional).
- *
- * @return An IOResult indicating success or failure.
- */
-template <class Model>
-IOResult<void> export_input_data_county_timeseries(
-    std::vector<Model> models, const std::string& results_dir, const std::vector<int>& counties, Date date,
-    const std::vector<double>& scaling_factor_inf, const double scaling_factor_icu, const int num_days,
-    const std::string& divi_data_path, const std::string& confirmed_cases_path, const std::string& population_data_path,
-    const std::vector<std::vector<double>> immunity_population, const std::string& vaccination_data_path = "")
-{
-    const auto num_groups = (size_t)models[0].parameters.get_num_groups();
-    assert(scaling_factor_inf.size() == num_groups);
-    assert(num_groups == ConfirmedCasesDataEntry::age_group_names.size());
-    assert(models.size() == counties.size());
-    std::vector<TimeSeries<double>> extrapolated_data(
-        models.size(), TimeSeries<double>::zero(num_days + 1, (size_t)InfectionState::Count * num_groups));
-
-    BOOST_OUTCOME_TRY(auto&& case_data, read_confirmed_cases_data(confirmed_cases_path));
-    BOOST_OUTCOME_TRY(auto&& population_data, read_population_data(population_data_path, counties));
-
-    // empty vector if set_vaccination_data is not set
-    std::vector<VaccinationDataEntry> vacc_data;
-    if (!vaccination_data_path.empty()) {
-        BOOST_OUTCOME_TRY(vacc_data, read_vaccination_data(vaccination_data_path));
-    }
-
-    for (int t = 0; t <= num_days; ++t) {
-        auto offset_day = offset_date_by_days(date, t);
-
-        if (!vaccination_data_path.empty()) {
-            BOOST_OUTCOME_TRY(details::set_vaccination_data(models, vacc_data, offset_day, counties, num_days));
-        }
-
-        // TODO: Reuse more code, e.g., set_divi_data (in secir) and a set_divi_data (here) only need a different ModelType.
-        // TODO: add option to set ICU data from confirmed cases if DIVI or other data is not available.
-        BOOST_OUTCOME_TRY(details::set_divi_data(models, divi_data_path, counties, offset_day, scaling_factor_icu));
-
-        BOOST_OUTCOME_TRY(details::set_confirmed_cases_data(models, case_data, counties, offset_day, scaling_factor_inf,
-                                                            immunity_population));
 
-        BOOST_OUTCOME_TRY(details::set_population_data(models, population_data, counties, immunity_population));
+    BOOST_OUTCOME_TRY(auto&& vacc_data, read_vaccination_data(path));
 
-        for (size_t r = 0; r < counties.size(); r++) {
-            extrapolated_data[r][t] = models[r].get_initial_values();
-            // in set_population_data the number of death individuals is subtracted from the SusceptibleImprovedImmunity compartment.
-            // Since we should be independent whether we consider them or not, we add them back here before we save the data.
-            for (size_t age = 0; age < num_groups; age++) {
-                extrapolated_data[r][t][(size_t)InfectionState::SusceptibleImprovedImmunity +
-                                        age * (size_t)InfectionState::Count] +=
-                    extrapolated_data[r][t][(size_t)InfectionState::DeadNaive + age * (size_t)InfectionState::Count];
-            }
+    // Sort vacc_data into regions and ignore once with no region associated
+    std::vector<std::vector<VaccinationDataEntry>> vvacc_data{model.size()};
+    for (auto&& vacc_data_entry : vacc_data) {
+        auto it = std::find_if(model.begin(), model.end(), [&vacc_data_entry](auto&& m) {
+            return m.id == 0 || (vacc_data_entry.county_id && vacc_data_entry.county_id == regions::CountyId(m.id)) ||
+                   (vacc_data_entry.state_id && vacc_data_entry.state_id == regions::StateId(m.id)) ||
+                   (vacc_data_entry.district_id && vacc_data_entry.district_id == regions::DistrictId(m.id));
+        });
+        if (it != model.end()) {
+            auto region_idx = size_t(it - model.begin());
+            vvacc_data[region_idx].push_back(vacc_data_entry);
         }
     }
-    BOOST_OUTCOME_TRY(save_result(extrapolated_data, counties, static_cast<int>(num_groups),
-                                  path_join(results_dir, "Results_rki.h5")));
 
-    auto extrapolated_rki_data_sum = sum_nodes(std::vector<std::vector<TimeSeries<double>>>{extrapolated_data});
-    BOOST_OUTCOME_TRY(save_result({extrapolated_rki_data_sum[0][0]}, {0}, static_cast<int>(num_groups),
-                                  path_join(results_dir, "Results_rki_sum.h5")));
-
-    return success();
-}
+    for (size_t region_idx = 0; region_idx < model.size(); ++region_idx) {
+        BOOST_OUTCOME_TRY(set_vaccination_data<FP>(model[region_idx].property, vvacc_data[region_idx], date, num_days));
+    }
 
-#else
-template <class Model>
-IOResult<void> export_input_data_county_timeseries(std::vector<Model>, const std::string&, const std::vector<int>&,
-                                                   Date, const std::vector<double>&, const double, const int,
-                                                   const std::string&, const std::string&, const std::string&,
-                                                   const std::vector<std::vector<double>>, const std::string&)
-{
-    mio::log_warning("HDF5 not available. Cannot export time series of extrapolated real data.");
     return success();
 }
 
-#endif //MEMILIO_HAS_HDF5
+} // namespace details
 
 /**
- * @brief Reads input data for specified counties and initializes the model accordingly.
- *
- * This function loads real-world data for specified counties and initializes
- * the corresponding model compartments. Optionally, it can extrapolate real-world data
- * using the export_input_data_county_timeseries function.
+ * @brief Reads compartments for geographic units at a specified date from data files.
  *
- * @tparam Model The model type used.
+ * This function estimates all compartments from available data using the provided model parameters.
  *
- * @param[in,out] model Vector of model objects that will be initialized with the input data.
- * @param[in] date The starting date for the simulation.
- * @param[in] county Vector of county IDs for which the data is read.
+ * @param[in,out] model VectorRange of Node%s each containing a Model to be initialized with data.
+ * @param[in] date Date for which the data should be read.
  * @param[in] scaling_factor_inf Vector of scaling factors for confirmed cases.
- * @param[in] scaling_factor_icu Scaling factor for ICU data.
- * @param[in] pydata_dir Path to the directory containing input data files.
- * @param[in] num_days The number of days for which the simulation runs.
- * @param[in] immunity_population Vector of vectors representing immunity proportions for each age group and immunity layer.
- * @param[in] export_time_series Boolean flag indicating whether to export time series of extrapolated data (default: false).
+ * @param[in] scaling_factor_icu Scaling factor for ICU cases.
+ * @param[in] num_days Number of days to simulate.
+ * @param[in] immunity_population Matrix containing immunity proportions for each age group and immunity layer.
+ * @param[in] epidata_filenames Object containing the input data file paths.
  *
  * @return An IOResult indicating success or failure.
  */
-template <class Model>
-IOResult<void> read_input_data_county(std::vector<Model>& model, Date date, const std::vector<int>& county,
-                                      const std::vector<double>& scaling_factor_inf, double scaling_factor_icu,
-                                      const std::string& pydata_dir, int num_days,
-                                      const std::vector<std::vector<double>> immunity_population,
-                                      bool export_time_series = false)
-{
-    BOOST_OUTCOME_TRY(details::set_vaccination_data(model, path_join(pydata_dir, "vacc_county_ageinf_ma7.json"), date,
-                                                    county, num_days));
-
-    // TODO: Reuse more code, e.g., set_divi_data (in secir) and a set_divi_data (here) only need a different ModelType.
-    // TODO: add option to set ICU data from confirmed cases if DIVI or other data is not available.
-    BOOST_OUTCOME_TRY(
-        details::set_divi_data(model, path_join(pydata_dir, "county_divi_ma7.json"), county, date, scaling_factor_icu));
-
-    BOOST_OUTCOME_TRY(details::set_confirmed_cases_data(model, path_join(pydata_dir, "cases_all_county_age_ma7.json"),
-                                                        county, date, scaling_factor_inf, immunity_population));
-    BOOST_OUTCOME_TRY(details::set_population_data(model, path_join(pydata_dir, "county_current_population.json"),
-                                                   county, immunity_population));
+IOResult<void> read_input_data(const mio::VectorRange<Node<Model<ScalarType>>>& model, Date date,
+                               const std::vector<ScalarType>& scaling_factor_inf, ScalarType scaling_factor_icu,
+                               int num_days, const std::vector<std::vector<ScalarType>>& immunity_population,
+                               const mio::regions::de::EpidataFilenames& epidata_filenames);
 
-    if (export_time_series) {
-        // Use only if extrapolated real data is needed for comparison. EXPENSIVE !
-        // Run time equals run time of the previous functions times the num_days !
-        // (This only represents the vectorization of the previous function over all simulation days...)
-        log_info("Exporting time series of extrapolated real data. This may take some minutes. "
-                 "For simulation runs over the same time period, deactivate it.");
-        BOOST_OUTCOME_TRY(export_input_data_county_timeseries(
-            model, pydata_dir, county, date, scaling_factor_inf, scaling_factor_icu, num_days,
-            path_join(pydata_dir, "county_divi_ma7.json"), path_join(pydata_dir, "cases_all_county_age_ma7.json"),
-            path_join(pydata_dir, "county_current_population.json"), immunity_population,
-            path_join(pydata_dir, "vacc_county_ageinf_ma7.json")));
-    }
-
-    return success();
-}
+#ifdef MEMILIO_HAS_HDF5
 
 /**
- * @brief Reads compartments for geographic units at a specified date from data files.
- *
- * This function estimates all compartments from available data using the provided model parameters.
- *
- * @tparam Model The type of tmodel used.
- *
- * @param[in,out] model Vector of models, one per county, to be initialized with data.
+ * @brief Uses the initialisation method, which uses the reported data to set the initial conditions for the model for a given day. 
+ * 
+ * The initialisation is applied for a predefined number of days and finally saved in a timeseries for each region. In the end,
+ * we save the files "Results_rki.h5" and "Results_rki_sum.h5" in the results_dir.
+ * Results_rki.h5 contains a time series for each region and Results_rki_sum.h5 contains the sum of all regions.
+ * 
+ * @param[in] model VectorRange of Node%s each containing a Model in which the data is set.
+ * @param[in] results_dir Path to result files.
  * @param[in] date Date for which the data should be read.
- * @param[in] node_ids Vector of IDs of the units for which data is read.
  * @param[in] scaling_factor_inf Vector of scaling factors for confirmed cases.
  * @param[in] scaling_factor_icu Scaling factor for ICU cases.
- * @param[in] pydata_dir Directory containing the input data files.
- * @param[in] num_days Number of days to simulate.
+ * @param[in] num_days Number of days to be simulated/initialized.
  * @param[in] immunity_population Matrix containing immunity proportions for each age group and immunity layer.
- * @param[in] export_time_series Boolean flag indicating whether to export time series of extrapolated data (default: false).
- *
+ * @param[in] epidata_filenames Object containing the input data file paths.
+ * 
  * @return An IOResult indicating success or failure.
-    */
-template <class Model>
-IOResult<void> read_input_data(std::vector<Model>& model, Date date, const std::vector<int>& node_ids,
-                               const std::vector<double>& scaling_factor_inf, double scaling_factor_icu,
-                               const std::string& pydata_dir, int num_days,
-                               const std::vector<std::vector<double>> immunity_population,
-                               bool export_time_series = false)
-{
-
-    BOOST_OUTCOME_TRY(
-        details::set_vaccination_data(model, path_join(pydata_dir, "vaccination_data.json"), date, node_ids, num_days));
-
-    // TODO: Reuse more code, e.g., set_divi_data (in secir) and a set_divi_data (here) only need a different ModelType.
-    // TODO: add option to set ICU data from confirmed cases if DIVI or other data is not available.
-    BOOST_OUTCOME_TRY(details::set_divi_data(model, path_join(pydata_dir, "critical_cases.json"), node_ids, date,
-                                             scaling_factor_icu));
-
-    BOOST_OUTCOME_TRY(details::set_confirmed_cases_data(model, path_join(pydata_dir, "confirmed_cases.json"), node_ids,
-                                                        date, scaling_factor_inf, immunity_population));
-    BOOST_OUTCOME_TRY(details::set_population_data(model, path_join(pydata_dir, "population_data.json"), node_ids,
-                                                   immunity_population));
-
-    if (export_time_series) {
-        // Use only if extrapolated real data is needed for comparison. EXPENSIVE !
-        // Run time equals run time of the previous functions times the num_days !
-        // (This only represents the vectorization of the previous function over all simulation days...)
-        log_info("Exporting time series of extrapolated real data. This may take some minutes. "
-                 "For simulation runs over the same time period, deactivate it.");
-        BOOST_OUTCOME_TRY(export_input_data_county_timeseries(
-            model, pydata_dir, node_ids, date, scaling_factor_inf, scaling_factor_icu, num_days,
-            path_join(pydata_dir, "critical_cases.json"), path_join(pydata_dir, "confirmed_cases.json"),
-            path_join(pydata_dir, "population_data.json"), immunity_population,
-            path_join(pydata_dir, "vaccination_data.json")));
-    }
+ */
+IOResult<void> export_input_data_timeseries(const mio::VectorRange<Node<Model<ScalarType>>> model,
+                                            const std::string& results_dir, Date date,
+                                            const std::vector<ScalarType>& scaling_factor_inf,
+                                            const ScalarType scaling_factor_icu, const int num_days,
+                                            const std::vector<std::vector<ScalarType>>& immunity_population,
+                                            const mio::regions::de::EpidataFilenames& epidata_filenames);
+#else
+IOResult<void> export_input_data_timeseries(const mio::VectorRange<Node<Model<ScalarType>>>, const std::string&, Date,
+                                            const std::vector<int>&, const std::vector<ScalarType>&, const ScalarType,
+                                            const int, const std::vector<std::vector<ScalarType>>,
+                                            const mio::regions::de::EpidataFilenames&);
 
-    return success();
-}
+#endif //MEMILIO_HAS_HDF5
 
 } // namespace osecirts
+
 } // namespace mio
 
 #endif // MEMILIO_HAS_JSONCPP
diff --git a/cpp/models/ode_secirvvs/parameters_io.cpp b/cpp/models/ode_secirvvs/parameters_io.cpp
index b1f0dcddb2..626f8f1dd2 100644
--- a/cpp/models/ode_secirvvs/parameters_io.cpp
+++ b/cpp/models/ode_secirvvs/parameters_io.cpp
@@ -29,40 +29,22 @@ namespace osecirvvs
 {
 namespace details
 {
-IOResult<void> read_confirmed_cases_data(
-    std::string const& path, std::vector<int> const& vregion, Date date, std::vector<std::vector<double>>& vnum_Exposed,
-    std::vector<std::vector<double>>& vnum_InfectedNoSymptoms, std::vector<std::vector<double>>& vnum_InfectedSymptoms,
-    std::vector<std::vector<double>>& vnum_InfectedSevere, std::vector<std::vector<double>>& vnum_icu,
-    std::vector<std::vector<double>>& vnum_death, std::vector<std::vector<double>>& vnum_rec,
-    const std::vector<std::vector<int>>& vt_Exposed, const std::vector<std::vector<int>>& vt_InfectedNoSymptoms,
-    const std::vector<std::vector<int>>& vt_InfectedSymptoms, const std::vector<std::vector<int>>& vt_InfectedSevere,
-    const std::vector<std::vector<int>>& vt_InfectedCritical, const std::vector<std::vector<double>>& vmu_C_R,
-    const std::vector<std::vector<double>>& vmu_I_H, const std::vector<std::vector<double>>& vmu_H_U,
-    const std::vector<double>& scaling_factor_inf)
-{
-    BOOST_OUTCOME_TRY(auto&& rki_data, mio::read_confirmed_cases_data(path));
-    return read_confirmed_cases_data(rki_data, vregion, date, vnum_Exposed, vnum_InfectedNoSymptoms,
-                                     vnum_InfectedSymptoms, vnum_InfectedSevere, vnum_icu, vnum_death, vnum_rec,
-                                     vt_Exposed, vt_InfectedNoSymptoms, vt_InfectedSymptoms, vt_InfectedSevere,
-                                     vt_InfectedCritical, vmu_C_R, vmu_I_H, vmu_H_U, scaling_factor_inf);
-}
 
-IOResult<void> read_confirmed_cases_data(
-    const std::vector<ConfirmedCasesDataEntry>& rki_data, std::vector<int> const& vregion, Date date,
-    std::vector<std::vector<double>>& vnum_Exposed, std::vector<std::vector<double>>& vnum_InfectedNoSymptoms,
-    std::vector<std::vector<double>>& vnum_InfectedSymptoms, std::vector<std::vector<double>>& vnum_InfectedSevere,
-    std::vector<std::vector<double>>& vnum_icu, std::vector<std::vector<double>>& vnum_death,
-    std::vector<std::vector<double>>& vnum_rec, const std::vector<std::vector<int>>& vt_Exposed,
-    const std::vector<std::vector<int>>& vt_InfectedNoSymptoms,
-    const std::vector<std::vector<int>>& vt_InfectedSymptoms, const std::vector<std::vector<int>>& vt_InfectedSevere,
-    const std::vector<std::vector<int>>& vt_InfectedCritical, const std::vector<std::vector<double>>& vmu_C_R,
-    const std::vector<std::vector<double>>& vmu_I_H, const std::vector<std::vector<double>>& vmu_H_U,
-    const std::vector<double>& scaling_factor_inf)
+IOResult<void>
+compute_confirmed_cases_data(const std::vector<ConfirmedCasesDataEntry>& case_data, const int region, Date date,
+                             std::vector<ScalarType>& num_Exposed, std::vector<ScalarType>& num_InfectedNoSymptoms,
+                             std::vector<ScalarType>& num_InfectedSymptoms, std::vector<ScalarType>& num_InfectedSevere,
+                             std::vector<ScalarType>& num_icu, std::vector<ScalarType>& num_death,
+                             std::vector<ScalarType>& num_rec, const std::vector<int>& t_Exposed,
+                             const std::vector<int>& t_InfectedNoSymptoms, const std::vector<int>& t_InfectedSymptoms,
+                             const std::vector<int>& t_InfectedSevere, const std::vector<int>& t_InfectedCritical,
+                             const std::vector<ScalarType>& mu_C_R, const std::vector<ScalarType>& mu_I_H,
+                             const std::vector<ScalarType>& mu_H_U, const std::vector<ScalarType>& scaling_factor_inf)
 {
-    auto max_date_entry = std::max_element(rki_data.begin(), rki_data.end(), [](auto&& a, auto&& b) {
+    auto max_date_entry = std::max_element(case_data.begin(), case_data.end(), [](auto&& a, auto&& b) {
         return a.date < b.date;
     });
-    if (max_date_entry == rki_data.end()) {
+    if (max_date_entry == case_data.end()) {
         log_error("RKI data file is empty.");
         return failure(StatusCode::InvalidValue, "RKI data is empty.");
     }
@@ -80,130 +62,84 @@ IOResult<void> read_confirmed_cases_data(
         days_surplus = 0;
     }
 
-    for (auto&& entry : rki_data) {
-        auto it = std::find_if(vregion.begin(), vregion.end(), [&entry](auto r) {
-            return r == 0 || get_region_id(entry) == r;
-        });
-        if (it != vregion.end()) {
-            auto region_idx = size_t(it - vregion.begin());
-
-            auto& t_Exposed            = vt_Exposed[region_idx];
-            auto& t_InfectedNoSymptoms = vt_InfectedNoSymptoms[region_idx];
-            auto& t_InfectedSymptoms   = vt_InfectedSymptoms[region_idx];
-            auto& t_InfectedSevere     = vt_InfectedSevere[region_idx];
-            auto& t_InfectedCritical   = vt_InfectedCritical[region_idx];
-
-            auto& num_InfectedNoSymptoms = vnum_InfectedNoSymptoms[region_idx];
-            auto& num_InfectedSymptoms   = vnum_InfectedSymptoms[region_idx];
-            auto& num_rec                = vnum_rec[region_idx];
-            auto& num_Exposed            = vnum_Exposed[region_idx];
-            auto& num_InfectedSevere     = vnum_InfectedSevere[region_idx];
-            auto& num_death              = vnum_death[region_idx];
-            auto& num_icu                = vnum_icu[region_idx];
-
-            auto& mu_C_R = vmu_C_R[region_idx];
-            auto& mu_I_H = vmu_I_H[region_idx];
-            auto& mu_H_U = vmu_H_U[region_idx];
-
-            auto age = (size_t)entry.age_group;
-            if (entry.date == offset_date_by_days(date, 0)) {
-                num_InfectedSymptoms[age] += scaling_factor_inf[age] * entry.num_confirmed;
-                num_rec[age] += entry.num_confirmed;
-            }
-            if (entry.date == offset_date_by_days(date, t_InfectedNoSymptoms[age] + days_surplus)) {
-                num_InfectedNoSymptoms[age] += 1 / (1 - mu_C_R[age]) * scaling_factor_inf[age] * entry.num_confirmed;
-                num_Exposed[age] -= 1 / (1 - mu_C_R[age]) * scaling_factor_inf[age] * entry.num_confirmed;
-            }
-            if (entry.date == offset_date_by_days(date, days_surplus)) {
-                num_InfectedNoSymptoms[age] -= 1 / (1 - mu_C_R[age]) * scaling_factor_inf[age] * entry.num_confirmed;
-            }
-            if (entry.date == offset_date_by_days(date, t_Exposed[age] + t_InfectedNoSymptoms[age] + days_surplus)) {
-                num_Exposed[age] += 1 / (1 - mu_C_R[age]) * scaling_factor_inf[age] * entry.num_confirmed;
-            }
-            if (entry.date == offset_date_by_days(date, -t_InfectedSymptoms[age])) {
-                num_InfectedSymptoms[age] -= scaling_factor_inf[age] * entry.num_confirmed;
-                num_InfectedSevere[age] += mu_I_H[age] * scaling_factor_inf[age] * entry.num_confirmed;
-            }
-            if (entry.date == offset_date_by_days(date, -t_InfectedSymptoms[age] - t_InfectedSevere[age])) {
-                num_InfectedSevere[age] -= mu_I_H[age] * scaling_factor_inf[age] * entry.num_confirmed;
-                num_icu[age] += mu_I_H[age] * mu_H_U[age] * scaling_factor_inf[age] * entry.num_confirmed;
-            }
-            if (entry.date ==
-                offset_date_by_days(date, -t_InfectedSymptoms[age] - t_InfectedSevere[age] - t_InfectedCritical[age])) {
-                num_death[age] += entry.num_deaths;
-                num_icu[age] -= mu_I_H[age] * mu_H_U[age] * scaling_factor_inf[age] * entry.num_confirmed;
-            }
+    for (auto&& entry : case_data) {
+
+        auto age = (size_t)entry.age_group;
+        if (entry.date == offset_date_by_days(date, 0)) {
+            num_InfectedSymptoms[age] += scaling_factor_inf[age] * entry.num_confirmed;
+            num_rec[age] += entry.num_confirmed;
+        }
+        if (entry.date == offset_date_by_days(date, t_InfectedNoSymptoms[age] + days_surplus)) {
+            num_InfectedNoSymptoms[age] += 1 / (1 - mu_C_R[age]) * scaling_factor_inf[age] * entry.num_confirmed;
+            num_Exposed[age] -= 1 / (1 - mu_C_R[age]) * scaling_factor_inf[age] * entry.num_confirmed;
+        }
+        if (entry.date == offset_date_by_days(date, days_surplus)) {
+            num_InfectedNoSymptoms[age] -= 1 / (1 - mu_C_R[age]) * scaling_factor_inf[age] * entry.num_confirmed;
+        }
+        if (entry.date == offset_date_by_days(date, t_Exposed[age] + t_InfectedNoSymptoms[age] + days_surplus)) {
+            num_Exposed[age] += 1 / (1 - mu_C_R[age]) * scaling_factor_inf[age] * entry.num_confirmed;
+        }
+        if (entry.date == offset_date_by_days(date, -t_InfectedSymptoms[age])) {
+            num_InfectedSymptoms[age] -= scaling_factor_inf[age] * entry.num_confirmed;
+            num_InfectedSevere[age] += mu_I_H[age] * scaling_factor_inf[age] * entry.num_confirmed;
+        }
+        if (entry.date == offset_date_by_days(date, -t_InfectedSymptoms[age] - t_InfectedSevere[age])) {
+            num_InfectedSevere[age] -= mu_I_H[age] * scaling_factor_inf[age] * entry.num_confirmed;
+            num_icu[age] += mu_I_H[age] * mu_H_U[age] * scaling_factor_inf[age] * entry.num_confirmed;
+        }
+        if (entry.date ==
+            offset_date_by_days(date, -t_InfectedSymptoms[age] - t_InfectedSevere[age] - t_InfectedCritical[age])) {
+            num_death[age] += entry.num_deaths;
+            num_icu[age] -= mu_I_H[age] * mu_H_U[age] * scaling_factor_inf[age] * entry.num_confirmed;
         }
     }
 
-    for (size_t region_idx = 0; region_idx < vregion.size(); ++region_idx) {
-        auto region = vregion[region_idx];
-
-        auto& num_InfectedNoSymptoms = vnum_InfectedNoSymptoms[region_idx];
-        auto& num_InfectedSymptoms   = vnum_InfectedSymptoms[region_idx];
-        auto& num_rec                = vnum_rec[region_idx];
-        auto& num_Exposed            = vnum_Exposed[region_idx];
-        auto& num_InfectedSevere     = vnum_InfectedSevere[region_idx];
-        auto& num_death              = vnum_death[region_idx];
-        auto& num_icu                = vnum_icu[region_idx];
-
-        size_t num_groups = ConfirmedCasesDataEntry::age_group_names.size();
-        for (size_t i = 0; i < num_groups; i++) {
-            // subtract infected confirmed cases which are not yet recovered
-            // and remove dark figure scaling factor
-            num_rec[i] -= num_InfectedSymptoms[i] / scaling_factor_inf[i];
-            num_rec[i] -= num_InfectedSevere[i] / scaling_factor_inf[i];
-            num_rec[i] -=
-                num_icu[i] /
-                scaling_factor_inf[i]; // TODO: this has to be adapted for scaling_factor_inf != 1 or != ***_icu
-            num_rec[i] -= num_death[i] / scaling_factor_inf[i];
-            auto try_fix_constraints = [region, i](double& value, double error, auto str) {
-                if (value < error) {
-                    // this should probably return a failure
-                    // but the algorithm is not robust enough to avoid large negative
-                    // values and there are tests that rely on it
-                    log_error("{:s} for age group {:s} is {:.4f} for region {:d}, "
-                              "exceeds expected negative value.",
-                              str, ConfirmedCasesDataEntry::age_group_names[i], value, region);
-                    value = 0.0;
-                }
-                else if (value < 0) {
-                    log_info("{:s} for age group {:s} is {:.4f} for region {:d}, "
-                             "automatically corrected",
-                             str, ConfirmedCasesDataEntry::age_group_names[i], value, region);
-                    value = 0.0;
-                }
-            };
-
-            try_fix_constraints(num_InfectedSymptoms[i], -5, "InfectedSymptoms");
-            try_fix_constraints(num_InfectedNoSymptoms[i], -5, "InfectedNoSymptoms");
-            try_fix_constraints(num_Exposed[i], -5, "Exposed");
-            try_fix_constraints(num_InfectedSevere[i], -5, "InfectedSevere");
-            try_fix_constraints(num_death[i], -5, "Dead");
-            try_fix_constraints(num_icu[i], -5, "InfectedCritical");
-            try_fix_constraints(num_rec[i], -20, "Recovered or vaccinated");
-        }
+    for (size_t i = 0; i < ConfirmedCasesDataEntry::age_group_names.size(); i++) {
+        // subtract infected confirmed cases which are not yet recovered
+        // and remove dark figure scaling factor
+        num_rec[i] -= num_InfectedSymptoms[i] / scaling_factor_inf[i];
+        num_rec[i] -= num_InfectedSevere[i] / scaling_factor_inf[i];
+        num_rec[i] -= num_icu[i] /
+                      scaling_factor_inf[i]; // TODO: this has to be adapted for scaling_factor_inf != 1 or != ***_icu
+        num_rec[i] -= num_death[i] / scaling_factor_inf[i];
+        auto try_fix_constraints = [region, i](ScalarType& value, ScalarType error, auto str) {
+            if (value < error) {
+                // this should probably return a failure
+                // but the algorithm is not robust enough to avoid large negative
+                // values and there are tests that rely on it
+                log_error("{:s} for age group {:s} is {:.4f} for region {:d}, "
+                          "exceeds expected negative value.",
+                          str, ConfirmedCasesDataEntry::age_group_names[i], value, region);
+                value = 0.0;
+            }
+            else if (value < 0) {
+                log_info("{:s} for age group {:s} is {:.4f} for region {:d}, "
+                         "automatically corrected",
+                         str, ConfirmedCasesDataEntry::age_group_names[i], value, region);
+                value = 0.0;
+            }
+        };
+
+        try_fix_constraints(num_InfectedSymptoms[i], -5, "InfectedSymptoms");
+        try_fix_constraints(num_InfectedNoSymptoms[i], -5, "InfectedNoSymptoms");
+        try_fix_constraints(num_Exposed[i], -5, "Exposed");
+        try_fix_constraints(num_InfectedSevere[i], -5, "InfectedSevere");
+        try_fix_constraints(num_death[i], -5, "Dead");
+        try_fix_constraints(num_icu[i], -5, "InfectedCritical");
+        try_fix_constraints(num_rec[i], -20, "Recovered or vaccinated");
     }
 
     return success();
 }
 
-IOResult<void> read_confirmed_cases_data_fix_recovered(std::string const& path, std::vector<int> const& vregion,
-                                                       Date date, std::vector<std::vector<double>>& vnum_rec,
-                                                       double delay)
+IOResult<std::vector<ScalarType>>
+compute_confirmed_cases_data_fix_recovered(const std::vector<ConfirmedCasesDataEntry>& case_data, const int region,
+                                           Date date, ScalarType delay)
 {
-    BOOST_OUTCOME_TRY(auto&& rki_data, mio::read_confirmed_cases_data(path));
-    return read_confirmed_cases_data_fix_recovered(rki_data, vregion, date, vnum_rec, delay);
-}
-
-IOResult<void> read_confirmed_cases_data_fix_recovered(const std::vector<ConfirmedCasesDataEntry>& rki_data,
-                                                       std::vector<int> const& vregion, Date date,
-                                                       std::vector<std::vector<double>>& vnum_rec, double delay)
-{
-    auto max_date_entry = std::max_element(rki_data.begin(), rki_data.end(), [](auto&& a, auto&& b) {
+    auto max_date_entry = std::max_element(case_data.begin(), case_data.end(), [](auto&& a, auto&& b) {
         return a.date < b.date;
     });
-    if (max_date_entry == rki_data.end()) {
+    if (max_date_entry == case_data.end()) {
         log_error("RKI data is empty.");
         return failure(StatusCode::InvalidValue, "RKI data is empty.");
     }
@@ -221,49 +157,627 @@ IOResult<void> read_confirmed_cases_data_fix_recovered(const std::vector<Confirm
         days_surplus = 0;
     }
 
-    for (auto&& rki_entry : rki_data) {
-        auto it = std::find_if(vregion.begin(), vregion.end(), [&rki_entry](auto r) {
-            return r == 0 || get_region_id(rki_entry) == r;
+    auto num_groups = ConfirmedCasesDataEntry::age_group_names.size();
+    std::vector<ScalarType> num_rec(num_groups, 0.0);
+
+    for (auto&& rki_entry : case_data) {
+        if (rki_entry.date == offset_date_by_days(date, int(-delay))) {
+            num_rec[size_t(rki_entry.age_group)] = rki_entry.num_confirmed;
+        }
+    }
+
+    for (size_t i = 0; i < num_groups; i++) {
+        auto try_fix_constraints = [region, i](ScalarType& value, ScalarType error, auto str) {
+            if (value < error) {
+                // this should probably return a failure
+                // but the algorithm is not robust enough to avoid large negative
+                // values and there are tests that rely on it
+                log_error("{:s} for age group {:s} is {:.4f} for region {:d}, "
+                          "exceeds expected negative value.",
+                          str, ConfirmedCasesDataEntry::age_group_names[i], value, region);
+                value = 0.0;
+            }
+            else if (value < 0) {
+                log_info("{:s} for age group {:s} is {:.4f} for region {:d}, "
+                         "automatically corrected",
+                         str, ConfirmedCasesDataEntry::age_group_names[i], value, region);
+                value = 0.0;
+            }
+        };
+        try_fix_constraints(num_rec[i], 0, "Recovered");
+    }
+
+    return success(num_rec);
+}
+
+IOResult<void> set_confirmed_cases_data(Model<ScalarType>& model, const std::vector<ConfirmedCasesDataEntry>& case_data,
+                                        const int region, Date date, const std::vector<ScalarType>& scaling_factor_inf,
+                                        bool set_death)
+{
+    auto num_age_groups = (size_t)model.parameters.get_num_groups();
+    assert(scaling_factor_inf.size() == num_age_groups); //TODO: allow vector or scalar valued scaling factors
+    assert(ConfirmedCasesDataEntry::age_group_names.size() == num_age_groups);
+
+    std::vector<int> t_Exposed;
+    std::vector<int> t_InfectedNoSymptoms;
+    std::vector<int> t_InfectedSymptoms;
+    std::vector<int> t_InfectedSevere;
+    std::vector<int> t_InfectedCritical;
+
+    std::vector<ScalarType> mu_C_R;
+    std::vector<ScalarType> mu_I_H;
+    std::vector<ScalarType> mu_H_U;
+
+    std::vector<ScalarType> num_InfectedSymptoms(num_age_groups, 0.0);
+    std::vector<ScalarType> num_death(num_age_groups, 0.0);
+    std::vector<ScalarType> num_rec(num_age_groups, 0.0);
+    std::vector<ScalarType> num_Exposed(num_age_groups, 0.0);
+    std::vector<ScalarType> num_InfectedNoSymptoms(num_age_groups, 0.0);
+    std::vector<ScalarType> num_InfectedSevere(num_age_groups, 0.0);
+    std::vector<ScalarType> num_icu(num_age_groups, 0.0);
+
+    /*----------- UNVACCINATED -----------*/
+    for (size_t group = 0; group < num_age_groups; group++) {
+
+        t_Exposed.push_back(
+            static_cast<int>(std::round(model.parameters.template get<TimeExposed<ScalarType>>()[(AgeGroup)group])));
+        t_InfectedNoSymptoms.push_back(static_cast<int>(
+            std::round(model.parameters.template get<TimeInfectedNoSymptoms<ScalarType>>()[(AgeGroup)group])));
+        t_InfectedSymptoms.push_back(static_cast<int>(
+            std::round(model.parameters.template get<TimeInfectedSymptoms<ScalarType>>()[(AgeGroup)group])));
+        t_InfectedSevere.push_back(static_cast<int>(
+            std::round(model.parameters.template get<TimeInfectedSevere<ScalarType>>()[(AgeGroup)group])));
+        t_InfectedCritical.push_back(static_cast<int>(
+            std::round(model.parameters.template get<TimeInfectedCritical<ScalarType>>()[(AgeGroup)group])));
+
+        mu_C_R.push_back(model.parameters.template get<RecoveredPerInfectedNoSymptoms<ScalarType>>()[(AgeGroup)group]);
+        mu_I_H.push_back(model.parameters.template get<SeverePerInfectedSymptoms<ScalarType>>()[(AgeGroup)group]);
+        mu_H_U.push_back(model.parameters.template get<CriticalPerSevere<ScalarType>>()[(AgeGroup)group]);
+    }
+
+    BOOST_OUTCOME_TRY(compute_confirmed_cases_data(
+        case_data, region, date, num_Exposed, num_InfectedNoSymptoms, num_InfectedSymptoms, num_InfectedSevere, num_icu,
+        num_death, num_rec, t_Exposed, t_InfectedNoSymptoms, t_InfectedSymptoms, t_InfectedSevere, t_InfectedCritical,
+        mu_C_R, mu_I_H, mu_H_U, scaling_factor_inf));
+
+    size_t num_groups = (size_t)model.parameters.get_num_groups();
+    for (size_t i = 0; i < num_groups; i++) {
+        model.populations[{AgeGroup(i), InfectionState::ExposedNaive}]                     = num_Exposed[i];
+        model.populations[{AgeGroup(i), InfectionState::InfectedNoSymptomsNaive}]          = num_InfectedNoSymptoms[i];
+        model.populations[{AgeGroup(i), InfectionState::InfectedNoSymptomsNaiveConfirmed}] = 0;
+        model.populations[{AgeGroup(i), InfectionState::InfectedSymptomsNaive}]            = num_InfectedSymptoms[i];
+        model.populations[{AgeGroup(i), InfectionState::InfectedSymptomsNaiveConfirmed}]   = 0;
+        model.populations[{AgeGroup(i), InfectionState::InfectedSevereNaive}]              = num_InfectedSevere[i];
+        // Only set the number of ICU patients here, if the date is not available in the data.
+        if (!is_divi_data_available(date)) {
+            model.populations[{AgeGroup(i), InfectionState::InfectedCriticalNaive}] = num_icu[i];
+        }
+        model.populations[{AgeGroup(i), InfectionState::SusceptibleImprovedImmunity}] = num_rec[i];
+        if (set_death) {
+            // in set_confirmed_cases_data initilization, deaths are now set to 0. In order to visualize
+            // the extrapolated real number of deaths, they have to be set here. In the comparison of data
+            // it has to be paid attention to the fact, the the simulation starts with deaths=0
+            // while this method starts with deaths=number of reported deaths so far...
+            // Additionally, we set the number of reported deaths to DeadNaive since no information on that is
+            // available here.
+            // Do only add deaths after substraction.
+            model.populations[{AgeGroup(i), InfectionState::DeadNaive}] = num_death[i];
+        }
+    }
+    if (std::accumulate(num_InfectedSymptoms.begin(), num_InfectedSymptoms.end(), ScalarType(0.0),
+                        [](const ScalarType& a, const ScalarType& b) {
+                            return evaluate_intermediate<ScalarType>(a + b);
+                        }) == 0.0) {
+        log_warning(
+            "No infections for unvaccinated reported on date {} for region {}. Population data has not been set.", date,
+            region);
+    }
+
+    /*----------- PARTIALLY VACCINATED -----------*/
+    t_InfectedNoSymptoms.clear();
+    t_Exposed.clear();
+    t_InfectedSymptoms.clear();
+    t_InfectedSevere.clear();
+    t_InfectedCritical.clear();
+
+    mu_C_R.clear();
+    mu_I_H.clear();
+    mu_H_U.clear();
+
+    num_InfectedSymptoms   = std::vector<ScalarType>(num_age_groups, 0.0);
+    num_death              = std::vector<ScalarType>(num_age_groups, 0.0);
+    num_rec                = std::vector<ScalarType>(num_age_groups, 0.0);
+    num_Exposed            = std::vector<ScalarType>(num_age_groups, 0.0);
+    num_InfectedNoSymptoms = std::vector<ScalarType>(num_age_groups, 0.0);
+    num_InfectedSevere     = std::vector<ScalarType>(num_age_groups, 0.0);
+    num_icu                = std::vector<ScalarType>(num_age_groups, 0.0);
+
+    for (size_t group = 0; group < num_age_groups; group++) {
+        ScalarType reduc_t = model.parameters.template get<ReducTimeInfectedMild<ScalarType>>()[(AgeGroup)group];
+        t_Exposed.push_back(
+            static_cast<int>(std::round(model.parameters.template get<TimeExposed<ScalarType>>()[(AgeGroup)group])));
+        t_InfectedNoSymptoms.push_back(static_cast<int>(std::round(
+            model.parameters.template get<TimeInfectedNoSymptoms<ScalarType>>()[(AgeGroup)group] * reduc_t)));
+        t_InfectedSymptoms.push_back(static_cast<int>(
+            std::round(model.parameters.template get<TimeInfectedSymptoms<ScalarType>>()[(AgeGroup)group] * reduc_t)));
+        t_InfectedSevere.push_back(static_cast<int>(
+            std::round(model.parameters.template get<TimeInfectedSevere<ScalarType>>()[(AgeGroup)group])));
+        t_InfectedCritical.push_back(static_cast<int>(
+            std::round(model.parameters.template get<TimeInfectedCritical<ScalarType>>()[(AgeGroup)group])));
+
+        ScalarType exp_fac_part_immune =
+            model.parameters.template get<ReducExposedPartialImmunity<ScalarType>>()[(AgeGroup)group];
+        ScalarType inf_fac_part_immune =
+            model.parameters.template get<ReducInfectedSymptomsPartialImmunity<ScalarType>>()[(AgeGroup)group];
+        ScalarType hosp_fac_part_immune =
+            model.parameters
+                .template get<ReducInfectedSevereCriticalDeadPartialImmunity<ScalarType>>()[(AgeGroup)group];
+        ScalarType icu_fac_part_immune =
+            model.parameters
+                .template get<ReducInfectedSevereCriticalDeadPartialImmunity<ScalarType>>()[(AgeGroup)group];
+        mu_C_R.push_back(
+            (1 -
+             inf_fac_part_immune / exp_fac_part_immune *
+                 (1 - model.parameters.template get<RecoveredPerInfectedNoSymptoms<ScalarType>>()[(AgeGroup)group])));
+        mu_I_H.push_back(hosp_fac_part_immune / inf_fac_part_immune *
+                         model.parameters.template get<SeverePerInfectedSymptoms<ScalarType>>()[(AgeGroup)group]);
+        // transfer from H to U, D unchanged.
+        mu_H_U.push_back(icu_fac_part_immune / hosp_fac_part_immune *
+                         model.parameters.template get<CriticalPerSevere<ScalarType>>()[(AgeGroup)group]);
+    }
+
+    BOOST_OUTCOME_TRY(compute_confirmed_cases_data(
+        case_data, region, date, num_Exposed, num_InfectedNoSymptoms, num_InfectedSymptoms, num_InfectedSevere, num_icu,
+        num_death, num_rec, t_Exposed, t_InfectedNoSymptoms, t_InfectedSymptoms, t_InfectedSevere, t_InfectedCritical,
+        mu_C_R, mu_I_H, mu_H_U, scaling_factor_inf));
+
+    for (size_t i = 0; i < num_groups; i++) {
+        model.populations[{AgeGroup(i), InfectionState::ExposedPartialImmunity}]            = num_Exposed[i];
+        model.populations[{AgeGroup(i), InfectionState::InfectedNoSymptomsPartialImmunity}] = num_InfectedNoSymptoms[i];
+        model.populations[{AgeGroup(i), InfectionState::InfectedNoSymptomsPartialImmunityConfirmed}] = 0;
+        model.populations[{AgeGroup(i), InfectionState::InfectedSymptomsPartialImmunity}] = num_InfectedSymptoms[i];
+        model.populations[{AgeGroup(i), InfectionState::InfectedSymptomsPartialImmunityConfirmed}] = 0;
+        model.populations[{AgeGroup(i), InfectionState::InfectedSeverePartialImmunity}] = num_InfectedSevere[i];
+        // Only set the number of ICU patients here, if the date is not available in the data.
+        if (!is_divi_data_available(date)) {
+            model.populations[{AgeGroup(i), InfectionState::InfectedCriticalPartialImmunity}] = num_icu[i];
+        }
+    }
+    // }
+    if (std::accumulate(num_InfectedSymptoms.begin(), num_InfectedSymptoms.end(), ScalarType(0.0),
+                        [](const ScalarType& a, const ScalarType& b) {
+                            return evaluate_intermediate<ScalarType>(a + b);
+                        }) == 0.0) {
+        log_warning("No infections for partially vaccinated reported on date {} for region {}. "
+                    "Population data has not been set.",
+                    date, region);
+    }
+
+    /*----------- FULLY VACCINATED -----------*/
+    t_InfectedNoSymptoms.clear();
+    t_Exposed.clear();
+    t_InfectedSymptoms.clear();
+    t_InfectedSevere.clear();
+    t_InfectedCritical.clear();
+
+    mu_C_R.clear();
+    mu_I_H.clear();
+    mu_H_U.clear();
+
+    num_InfectedSymptoms   = std::vector<ScalarType>(num_age_groups, 0.0);
+    num_death              = std::vector<ScalarType>(num_age_groups, 0.0);
+    num_rec                = std::vector<ScalarType>(num_age_groups, 0.0);
+    num_Exposed            = std::vector<ScalarType>(num_age_groups, 0.0);
+    num_InfectedNoSymptoms = std::vector<ScalarType>(num_age_groups, 0.0);
+    num_InfectedSevere     = std::vector<ScalarType>(num_age_groups, 0.0);
+    num_icu                = std::vector<ScalarType>(num_age_groups, 0.0);
+
+    for (size_t group = 0; group < num_age_groups; group++) {
+        ScalarType reduc_t = model.parameters.template get<ReducTimeInfectedMild<ScalarType>>()[(AgeGroup)group];
+        t_Exposed.push_back(
+            static_cast<int>(std::round(model.parameters.template get<TimeExposed<ScalarType>>()[(AgeGroup)group])));
+        t_InfectedNoSymptoms.push_back(static_cast<int>(std::round(
+            model.parameters.template get<TimeInfectedNoSymptoms<ScalarType>>()[(AgeGroup)group] * reduc_t)));
+        t_InfectedSymptoms.push_back(static_cast<int>(
+            std::round(model.parameters.template get<TimeInfectedSymptoms<ScalarType>>()[(AgeGroup)group] * reduc_t)));
+        t_InfectedSevere.push_back(static_cast<int>(
+            std::round(model.parameters.template get<TimeInfectedSevere<ScalarType>>()[(AgeGroup)group])));
+        t_InfectedCritical.push_back(static_cast<int>(
+            std::round(model.parameters.template get<TimeInfectedCritical<ScalarType>>()[(AgeGroup)group])));
+
+        ScalarType reduc_immune_exp =
+            model.parameters.template get<ReducExposedImprovedImmunity<ScalarType>>()[(AgeGroup)group];
+        ScalarType reduc_immune_inf =
+            model.parameters.template get<ReducInfectedSymptomsImprovedImmunity<ScalarType>>()[(AgeGroup)group];
+        ScalarType reduc_immune_hosp =
+            model.parameters
+                .template get<ReducInfectedSevereCriticalDeadImprovedImmunity<ScalarType>>()[(AgeGroup)group];
+        ScalarType reduc_immune_icu =
+            model.parameters
+                .template get<ReducInfectedSevereCriticalDeadImprovedImmunity<ScalarType>>()[(AgeGroup)group];
+        mu_C_R.push_back(
+            (1 -
+             reduc_immune_inf / reduc_immune_exp *
+                 (1 - model.parameters.template get<RecoveredPerInfectedNoSymptoms<ScalarType>>()[(AgeGroup)group])));
+        mu_I_H.push_back(reduc_immune_hosp / reduc_immune_inf *
+                         model.parameters.template get<SeverePerInfectedSymptoms<ScalarType>>()[(AgeGroup)group]);
+        // transfer from H to U, D unchanged.
+        mu_H_U.push_back(reduc_immune_icu / reduc_immune_hosp *
+                         model.parameters.template get<CriticalPerSevere<ScalarType>>()[(AgeGroup)group]);
+    }
+
+    BOOST_OUTCOME_TRY(compute_confirmed_cases_data(
+        case_data, region, date, num_Exposed, num_InfectedNoSymptoms, num_InfectedSymptoms, num_InfectedSevere, num_icu,
+        num_death, num_rec, t_Exposed, t_InfectedNoSymptoms, t_InfectedSymptoms, t_InfectedSevere, t_InfectedCritical,
+        mu_C_R, mu_I_H, mu_H_U, scaling_factor_inf));
+
+    for (size_t i = 0; i < num_groups; i++) {
+        model.populations[{AgeGroup(i), InfectionState::ExposedImprovedImmunity}] = num_Exposed[i];
+        model.populations[{AgeGroup(i), InfectionState::InfectedNoSymptomsImprovedImmunity}] =
+            num_InfectedNoSymptoms[i];
+        model.populations[{AgeGroup(i), InfectionState::InfectedNoSymptomsImprovedImmunityConfirmed}] = 0;
+        model.populations[{AgeGroup(i), InfectionState::InfectedSymptomsImprovedImmunity}] = num_InfectedSymptoms[i];
+        model.populations[{AgeGroup(i), InfectionState::InfectedSymptomsImprovedImmunityConfirmed}] = 0;
+        model.populations[{AgeGroup(i), InfectionState::InfectedSevereImprovedImmunity}] = num_InfectedSevere[i];
+        // Only set the number of ICU patients here, if the date is not available in the data.
+        if (!is_divi_data_available(date)) {
+            model.populations[{AgeGroup(i), InfectionState::InfectedCriticalImprovedImmunity}] = num_icu[i];
+        }
+    }
+
+    if (std::accumulate(num_InfectedSymptoms.begin(), num_InfectedSymptoms.end(), ScalarType(0.0),
+                        [](const ScalarType& a, const ScalarType& b) {
+                            return evaluate_intermediate<ScalarType>(a + b);
+                        }) == 0.0) {
+        log_warning("No infections for vaccinated reported on date {} for region {}. "
+                    "Population data has not been set.",
+                    date, region);
+    }
+
+    return success();
+}
+
+IOResult<void> set_confirmed_cases_data(const mio::VectorRange<Node<Model<ScalarType>>>& model, const std::string& path,
+                                        Date date, const std::vector<ScalarType>& scaling_factor_inf, bool set_death)
+{
+    BOOST_OUTCOME_TRY(auto&& case_data, mio::read_confirmed_cases_data(path));
+
+    // sort case_data into regions and ignore once with no region associated
+    std::vector<std::vector<ConfirmedCasesDataEntry>> vcase_data{model.size()};
+    for (auto&& entry : case_data) {
+        auto it = std::find_if(model.begin(), model.end(), [&entry](auto m) {
+            return m.id == 0 || get_region_id(entry) == m.id;
         });
-        if (it != vregion.end()) {
-            auto region_idx = size_t(it - vregion.begin());
-            if (rki_entry.date == offset_date_by_days(date, int(-delay))) {
-                vnum_rec[region_idx][size_t(rki_entry.age_group)] = rki_entry.num_confirmed;
+        if (it != model.end()) {
+            auto region_idx = size_t(it - model.begin());
+            vcase_data[region_idx].push_back(entry);
+        }
+    }
+
+    for (size_t region_idx = 0; region_idx < model.size(); ++region_idx) {
+        BOOST_OUTCOME_TRY(set_confirmed_cases_data(model[region_idx].property, vcase_data[region_idx],
+                                                   model[region_idx].id, date, scaling_factor_inf, set_death));
+    }
+    return success();
+}
+
+IOResult<void> set_population_data(Model<ScalarType>& model, const std::vector<ScalarType>& num_population,
+                                   const int region, const std::vector<ScalarType>& num_rec)
+{
+    if (std::accumulate(num_population.begin(), num_population.end(), ScalarType(0.0),
+                        [](const ScalarType& a, const ScalarType& b) {
+                            return evaluate_intermediate<ScalarType>(a + b);
+                        }) <= 0) {
+        log_warning("No population data available for region " + std::to_string(region) +
+                    ". Population data has not been set.");
+        return success();
+    }
+
+    auto num_groups = model.parameters.get_num_groups();
+    for (auto i = AgeGroup(0); i < num_groups; i++) {
+
+        ScalarType S_v =
+            std::min(model.parameters.template get<DailyFullVaccinations<ScalarType>>()[{i, SimulationDay(0)}] +
+                         num_rec[size_t(i)],
+                     num_population[size_t(i)]);
+        ScalarType S_pv =
+            std::max(model.parameters.template get<DailyPartialVaccinations<ScalarType>>()[{i, SimulationDay(0)}] -
+                         model.parameters.template get<DailyFullVaccinations<ScalarType>>()[{i, SimulationDay(0)}],
+                     0.0); // use std::max with 0
+        ScalarType S;
+        if (num_population[size_t(i)] - S_pv - S_v < 0.0) {
+            log_warning("Number of vaccinated persons greater than population in county {}, age group {}.", region,
+                        size_t(i));
+            S   = 0.0;
+            S_v = num_population[size_t(i)] - S_pv;
+        }
+        else {
+            S = num_population[size_t(i)] - S_pv - S_v;
+        }
+
+        ScalarType denom_E =
+            1 / (S + S_pv * model.parameters.template get<ReducExposedPartialImmunity<ScalarType>>()[i] +
+                 S_v * model.parameters.template get<ReducExposedImprovedImmunity<ScalarType>>()[i]);
+        ScalarType denom_C =
+            1 / (S + S_pv * model.parameters.template get<ReducExposedPartialImmunity<ScalarType>>()[i] +
+                 S_v * model.parameters.template get<ReducExposedImprovedImmunity<ScalarType>>()[i]);
+        ScalarType denom_I =
+            1 / (S + S_pv * model.parameters.template get<ReducInfectedSymptomsPartialImmunity<ScalarType>>()[i] +
+                 S_v * model.parameters.template get<ReducInfectedSymptomsImprovedImmunity<ScalarType>>()[i]);
+        ScalarType denom_HU =
+            1 /
+            (S + S_pv * model.parameters.template get<ReducInfectedSevereCriticalDeadPartialImmunity<ScalarType>>()[i] +
+             S_v * model.parameters.template get<ReducInfectedSevereCriticalDeadImprovedImmunity<ScalarType>>()[i]);
+
+        model.populations[{i, InfectionState::ExposedNaive}] =
+            S * model.populations[{i, InfectionState::ExposedNaive}] * denom_E;
+        model.populations[{i, InfectionState::ExposedPartialImmunity}] =
+            S_pv * model.parameters.template get<ReducExposedPartialImmunity<ScalarType>>()[i] *
+            model.populations[{i, InfectionState::ExposedPartialImmunity}] * denom_E;
+        model.populations[{i, InfectionState::ExposedImprovedImmunity}] =
+            S_v * model.parameters.template get<ReducExposedImprovedImmunity<ScalarType>>()[i] *
+            model.populations[{i, InfectionState::ExposedImprovedImmunity}] * denom_E;
+
+        model.populations[{i, InfectionState::InfectedNoSymptomsNaive}] =
+            S * model.populations[{i, InfectionState::InfectedNoSymptomsNaive}] * denom_C;
+        model.populations[{i, InfectionState::InfectedNoSymptomsPartialImmunity}] =
+            S_pv * model.populations[{i, InfectionState::InfectedNoSymptomsPartialImmunity}] * denom_C;
+        model.populations[{i, InfectionState::InfectedNoSymptomsImprovedImmunity}] =
+            S_v * model.populations[{i, InfectionState::InfectedNoSymptomsImprovedImmunity}] * denom_C;
+
+        model.populations[{i, InfectionState::InfectedNoSymptomsNaiveConfirmed}] =
+            S * model.populations[{i, InfectionState::InfectedNoSymptomsNaiveConfirmed}] * denom_C;
+        model.populations[{i, InfectionState::InfectedNoSymptomsPartialImmunityConfirmed}] =
+            S_pv * model.populations[{i, InfectionState::InfectedNoSymptomsPartialImmunityConfirmed}] * denom_C;
+        model.populations[{i, InfectionState::InfectedNoSymptomsImprovedImmunityConfirmed}] =
+            S_v * model.populations[{i, InfectionState::InfectedNoSymptomsImprovedImmunityConfirmed}] * denom_C;
+
+        model.populations[{i, InfectionState::InfectedSymptomsNaive}] =
+            S * model.populations[{i, InfectionState::InfectedSymptomsNaive}] * denom_I;
+        model.populations[{i, InfectionState::InfectedSymptomsPartialImmunity}] =
+            S_pv * model.parameters.template get<ReducInfectedSymptomsPartialImmunity<ScalarType>>()[i] *
+            model.populations[{i, InfectionState::InfectedSymptomsPartialImmunity}] * denom_I;
+        model.populations[{i, InfectionState::InfectedSymptomsImprovedImmunity}] =
+            S_v * model.parameters.template get<ReducInfectedSymptomsImprovedImmunity<ScalarType>>()[i] *
+            model.populations[{i, InfectionState::InfectedSymptomsImprovedImmunity}] * denom_I;
+
+        model.populations[{i, InfectionState::InfectedSymptomsNaiveConfirmed}] =
+            S * model.populations[{i, InfectionState::InfectedSymptomsNaiveConfirmed}] * denom_I;
+        model.populations[{i, InfectionState::InfectedSymptomsPartialImmunityConfirmed}] =
+            S_pv * model.parameters.template get<ReducInfectedSymptomsPartialImmunity<ScalarType>>()[i] *
+            model.populations[{i, InfectionState::InfectedSymptomsPartialImmunityConfirmed}] * denom_I;
+        model.populations[{i, InfectionState::InfectedSymptomsImprovedImmunityConfirmed}] =
+            S_v * model.parameters.template get<ReducInfectedSymptomsImprovedImmunity<ScalarType>>()[i] *
+            model.populations[{i, InfectionState::InfectedSymptomsImprovedImmunityConfirmed}] * denom_I;
+
+        model.populations[{i, InfectionState::InfectedSevereNaive}] =
+            S * model.populations[{i, InfectionState::InfectedSevereNaive}] * denom_HU;
+        model.populations[{i, InfectionState::InfectedSeverePartialImmunity}] =
+            S_pv * model.parameters.template get<ReducInfectedSevereCriticalDeadPartialImmunity<ScalarType>>()[i] *
+            model.populations[{i, InfectionState::InfectedSeverePartialImmunity}] * denom_HU;
+        model.populations[{i, InfectionState::InfectedSevereImprovedImmunity}] =
+            S_v * model.parameters.template get<ReducInfectedSevereCriticalDeadImprovedImmunity<ScalarType>>()[i] *
+            model.populations[{i, InfectionState::InfectedSevereImprovedImmunity}] * denom_HU;
+
+        model.populations[{i, InfectionState::InfectedCriticalPartialImmunity}] =
+            S_pv * model.parameters.template get<ReducInfectedSevereCriticalDeadPartialImmunity<ScalarType>>()[i] *
+            model.populations[{i, InfectionState::InfectedCriticalNaive}] * denom_HU;
+        model.populations[{i, InfectionState::InfectedCriticalImprovedImmunity}] =
+            S_v * model.parameters.template get<ReducInfectedSevereCriticalDeadImprovedImmunity<ScalarType>>()[i] *
+            model.populations[{i, InfectionState::InfectedCriticalNaive}] * denom_HU;
+        model.populations[{i, InfectionState::InfectedCriticalNaive}] =
+            S * model.populations[{i, InfectionState::InfectedCriticalNaive}] * denom_HU;
+
+        model.populations[{i, InfectionState::SusceptibleImprovedImmunity}] =
+            model.parameters.template get<DailyFullVaccinations<ScalarType>>()[{i, SimulationDay(0)}] +
+            model.populations[{i, InfectionState::SusceptibleImprovedImmunity}] -
+            (model.populations[{i, InfectionState::InfectedSymptomsNaive}] +
+             model.populations[{i, InfectionState::InfectedSymptomsPartialImmunity}] +
+             model.populations[{i, InfectionState::InfectedSymptomsImprovedImmunity}] +
+             model.populations[{i, InfectionState::InfectedSymptomsNaiveConfirmed}] +
+             model.populations[{i, InfectionState::InfectedSymptomsPartialImmunityConfirmed}] +
+             model.populations[{i, InfectionState::InfectedSymptomsImprovedImmunityConfirmed}] +
+             model.populations[{i, InfectionState::InfectedSevereNaive}] +
+             model.populations[{i, InfectionState::InfectedSeverePartialImmunity}] +
+             model.populations[{i, InfectionState::InfectedSevereImprovedImmunity}] +
+             model.populations[{i, InfectionState::InfectedCriticalNaive}] +
+             model.populations[{i, InfectionState::InfectedCriticalPartialImmunity}] +
+             model.populations[{i, InfectionState::InfectedCriticalImprovedImmunity}] +
+             model.populations[{i, InfectionState::DeadNaive}] +
+             model.populations[{i, InfectionState::DeadPartialImmunity}] +
+             model.populations[{i, InfectionState::DeadImprovedImmunity}]);
+
+        model.populations[{i, InfectionState::SusceptibleImprovedImmunity}] =
+            std::min(S_v - model.populations[{i, InfectionState::ExposedImprovedImmunity}] -
+                         model.populations[{i, InfectionState::InfectedNoSymptomsImprovedImmunity}] -
+                         model.populations[{i, InfectionState::InfectedNoSymptomsImprovedImmunityConfirmed}] -
+                         model.populations[{i, InfectionState::InfectedSymptomsImprovedImmunity}] -
+                         model.populations[{i, InfectionState::InfectedSymptomsImprovedImmunityConfirmed}] -
+                         model.populations[{i, InfectionState::InfectedSevereImprovedImmunity}] -
+                         model.populations[{i, InfectionState::InfectedCriticalImprovedImmunity}] -
+                         model.populations[{i, InfectionState::DeadImprovedImmunity}],
+                     std::max(0.0, ScalarType(model.populations[{i, InfectionState::SusceptibleImprovedImmunity}])));
+
+        model.populations[{i, InfectionState::SusceptiblePartialImmunity}] =
+            std::max(0.0, S_pv - model.populations[{i, InfectionState::ExposedPartialImmunity}] -
+                              model.populations[{i, InfectionState::InfectedNoSymptomsPartialImmunity}] -
+                              model.populations[{i, InfectionState::InfectedNoSymptomsPartialImmunityConfirmed}] -
+                              model.populations[{i, InfectionState::InfectedSymptomsPartialImmunity}] -
+                              model.populations[{i, InfectionState::InfectedSymptomsPartialImmunityConfirmed}] -
+                              model.populations[{i, InfectionState::InfectedSeverePartialImmunity}] -
+                              model.populations[{i, InfectionState::InfectedCriticalPartialImmunity}] -
+                              model.populations[{i, InfectionState::DeadPartialImmunity}]);
+
+        model.populations.template set_difference_from_group_total<AgeGroup>({i, InfectionState::SusceptibleNaive},
+                                                                             num_population[size_t(i)]);
+    }
+
+    for (auto i = AgeGroup(0); i < AgeGroup(6); i++) {
+        for (auto j = Index<InfectionState>(0); j < InfectionState::Count; ++j) {
+            if (model.populations[{i, j}] < 0) {
+                log_warning("Compartment at age group {}, infection state {}, is negative: {}", size_t(i), size_t(j),
+                            model.populations[{i, j}] / num_population[size_t(i)]);
             }
         }
     }
 
-    for (size_t region_idx = 0; region_idx < vregion.size(); ++region_idx) {
-        auto region   = vregion[region_idx];
-        auto& num_rec = vnum_rec[region_idx];
-
-        size_t num_groups = ConfirmedCasesDataEntry::age_group_names.size();
-        for (size_t i = 0; i < num_groups; i++) {
-            auto try_fix_constraints = [region, i](double& value, double error, auto str) {
-                if (value < error) {
-                    // this should probably return a failure
-                    // but the algorithm is not robust enough to avoid large negative
-                    // values and there are tests that rely on it
-                    log_error("{:s} for age group {:s} is {:.4f} for region {:d}, "
-                              "exceeds expected negative value.",
-                              str, ConfirmedCasesDataEntry::age_group_names[i], value, region);
-                    value = 0.0;
-                }
-                else if (value < 0) {
-                    log_info("{:s} for age group {:s} is {:.4f} for region {:d}, "
-                             "automatically corrected",
-                             str, ConfirmedCasesDataEntry::age_group_names[i], value, region);
-                    value = 0.0;
-                }
-            };
-            try_fix_constraints(num_rec[i], 0, "Recovered");
+    return success();
+}
+
+IOResult<void> set_population_data(const mio::VectorRange<Node<Model<ScalarType>>>& model, const std::string& path,
+                                   const std::string& path_rki, Date date)
+{
+    std::vector<int> vregion;
+    std::transform(model.begin(), model.end(), std::back_inserter(vregion), [](const auto& m) {
+        return m.id;
+    });
+    BOOST_OUTCOME_TRY(auto&& vnum_population, read_population_data(path, vregion));
+    BOOST_OUTCOME_TRY(auto&& case_data, mio::read_confirmed_cases_data(path_rki));
+
+    // sort case_data into regions and ignore once with no region associated
+    std::vector<std::vector<ConfirmedCasesDataEntry>> vcase_data{model.size()};
+    for (auto&& entry : case_data) {
+        auto it = std::find_if(model.begin(), model.end(), [&entry](auto m) {
+            return m.id == 0 || get_region_id(entry) == m.id;
+        });
+        if (it != model.end()) {
+            auto region_idx = size_t(it - model.begin());
+            vcase_data[region_idx].push_back(entry);
         }
     }
 
+    for (size_t region_idx = 0; region_idx < model.size(); ++region_idx) {
+        BOOST_OUTCOME_TRY(auto&& num_rec, compute_confirmed_cases_data_fix_recovered(vcase_data[region_idx],
+                                                                                     model[region_idx].id, date, 14.));
+        BOOST_OUTCOME_TRY(set_population_data(model[region_idx].property, vnum_population[region_idx],
+                                              model[region_idx].id, num_rec));
+    }
+    return success();
+}
+
+IOResult<void> set_divi_data(Model<ScalarType>& model, const ScalarType num_icu, ScalarType scaling_factor_icu)
+{
+    ScalarType sum_mu_I_U = 0;
+    std::vector<ScalarType> mu_I_U;
+    auto num_groups = model.parameters.get_num_groups();
+    for (auto i = AgeGroup(0); i < num_groups; i++) {
+        sum_mu_I_U += model.parameters.template get<CriticalPerSevere<ScalarType>>()[i] *
+                      model.parameters.template get<SeverePerInfectedSymptoms<ScalarType>>()[i];
+        mu_I_U.push_back(model.parameters.template get<CriticalPerSevere<ScalarType>>()[i] *
+                         model.parameters.template get<SeverePerInfectedSymptoms<ScalarType>>()[i]);
+    }
+
+    for (auto i = AgeGroup(0); i < num_groups; i++) {
+        model.populations[{i, InfectionState::InfectedCriticalNaive}] =
+            scaling_factor_icu * num_icu * mu_I_U[(size_t)i] / sum_mu_I_U;
+    }
+
+    return success();
+}
+
+IOResult<void> set_divi_data(const mio::VectorRange<Node<Model<ScalarType>>>& model, const std::string& path, Date date,
+                             ScalarType scaling_factor_icu)
+{
+    // DIVI dataset will no longer be updated from CW29 2024 on.
+    if (!is_divi_data_available(date)) {
+        log_warning("No DIVI data available for date: {}. "
+                    "ICU compartment will be set based on Case data.",
+                    date);
+        return success();
+    }
+
+    std::vector<int> vregion;
+    std::transform(model.begin(), model.end(), std::back_inserter(vregion), [](const auto& m) {
+        return m.id;
+    });
+    BOOST_OUTCOME_TRY(auto&& num_icu, read_divi_data(path, vregion, date));
+
+    for (size_t region_idx = 0; region_idx < model.size(); ++region_idx) {
+        BOOST_OUTCOME_TRY(set_divi_data(model[region_idx].property, num_icu[region_idx], scaling_factor_icu));
+    }
+
     return success();
 }
 
 } // namespace details
+
+IOResult<void> read_input_data(const mio::VectorRange<Node<Model<ScalarType>>>& model, Date date,
+                               const std::vector<ScalarType>& scaling_factor_inf, ScalarType scaling_factor_icu,
+                               int num_days, const mio::regions::de::EpidataFilenames& epidata_filenames, 
+                               bool set_death)
+{
+
+    BOOST_OUTCOME_TRY(
+        details::set_vaccination_data<ScalarType>(model, epidata_filenames.vaccination_data_path, date, num_days));
+
+    // TODO: Reuse more code, e.g., set_divi_data (in secir) and a set_divi_data (here) only need a different ModelType.
+    // TODO: add option to set ICU data from confirmed cases if DIVI or other data is not available.
+    BOOST_OUTCOME_TRY(details::set_divi_data(model, epidata_filenames.divi_data_path, date, scaling_factor_icu));
+
+    BOOST_OUTCOME_TRY(
+        details::set_confirmed_cases_data(model, epidata_filenames.case_data_path, date, scaling_factor_inf, set_death));
+    BOOST_OUTCOME_TRY(details::set_population_data(model, epidata_filenames.population_data_path,
+                                                   epidata_filenames.case_data_path, date));
+    return success();
+}
+
+#ifdef MEMILIO_HAS_HDF5
+
+IOResult<void> export_input_data_timeseries(const mio::VectorRange<Node<Model<ScalarType>>> model,
+                                            const std::string& results_dir, Date date,
+                                            const std::vector<ScalarType>& scaling_factor_inf,
+                                            const ScalarType scaling_factor_icu, const int num_days,
+                                            const mio::regions::de::EpidataFilenames& epidata_filenames)
+{
+    const auto num_age_groups = (size_t)model[0].property.parameters.get_num_groups();
+    assert(scaling_factor_inf.size() == num_age_groups);
+    assert(num_age_groups == ConfirmedCasesDataEntry::age_group_names.size());
+    std::vector<TimeSeries<ScalarType>> extrapolated_data(
+        model.size(), TimeSeries<ScalarType>::zero(num_days + 1, (size_t)InfectionState::Count * num_age_groups));
+
+    for (int t = 0; t <= num_days; ++t) {
+        auto offset_day = offset_date_by_days(date, t);
+
+        // TODO: empty vaccination data path guard
+        BOOST_OUTCOME_TRY(
+            read_input_data(model, offset_day, scaling_factor_inf, scaling_factor_icu, num_days, epidata_filenames, true));
+
+        for (size_t r = 0; r < model.size(); r++) {
+            extrapolated_data[r][t] = model[r].property.get_initial_values();
+            // in set_population_data the number of death individuals is subtracted from the SusceptibleImprovedImmunity compartment.
+            // Since we should be independent whether we consider them or not, we add them back here before we save the data.
+            for (size_t age = 0; age < num_age_groups; age++) {
+                extrapolated_data[r][t][(size_t)InfectionState::SusceptibleImprovedImmunity +
+                                        age * (size_t)InfectionState::Count] +=
+                    extrapolated_data[r][t][(size_t)InfectionState::DeadNaive + age * (size_t)InfectionState::Count];
+            }
+        }
+    }
+    std::vector<int> vregion;
+    std::transform(model.begin(), model.end(), std::back_inserter(vregion), [](const auto& m) {
+        return m.id;
+    });
+    BOOST_OUTCOME_TRY(save_result(extrapolated_data, vregion, static_cast<int>(num_age_groups),
+                                  path_join(results_dir, "Results_rki.h5")));
+
+    auto extrapolated_rki_data_sum = sum_nodes(std::vector<std::vector<TimeSeries<ScalarType>>>{extrapolated_data});
+    BOOST_OUTCOME_TRY(save_result({extrapolated_rki_data_sum[0][0]}, {0}, static_cast<int>(num_age_groups),
+                                  path_join(results_dir, "Results_rki_sum.h5")));
+
+    return success();
+}
+#else
+IOResult<void> export_input_data_county_timeseries(const mio::VectorRange<Node<Model<ScalarType>>>, const std::string&,
+                                                   Date, const std::vector<int>&, const std::vector<ScalarType>&,
+                                                   const ScalarType, const int,
+                                                   const mio::regions::de::EpidataFilenames&)
+{
+    mio::log_warning("HDF5 not available. Cannot export time series of extrapolated real data.");
+    return success();
+}
+
+#endif //MEMILIO_HAS_HDF5
+
 } // namespace osecirvvs
 } // namespace mio
 
diff --git a/cpp/models/ode_secirvvs/parameters_io.h b/cpp/models/ode_secirvvs/parameters_io.h
index 815f09febf..7b1cb75d1d 100644
--- a/cpp/models/ode_secirvvs/parameters_io.h
+++ b/cpp/models/ode_secirvvs/parameters_io.h
@@ -25,6 +25,7 @@
 #ifdef MEMILIO_HAS_JSONCPP
 
 #include "ode_secirvvs/model.h"
+#include "memilio/geography/regions.h"
 #include "memilio/mobility/graph.h"
 #include "memilio/io/epi_data.h"
 #include "memilio/io/parameters_io.h"
@@ -41,684 +42,125 @@ namespace osecirvvs
 
 namespace details
 {
-/**
-        * @brief Reads subpopulations of infection states from transformed RKI cases file.
-        * @param[in] path Path to transformed RKI cases file.
-        * @param[in] vregion Vector of keys of the region of interest.
-        * @param[in] date Date for which the arrays are initialized.
-        * @param[in, out] num_* Output vector for number of people in the corresponding compartement.
-        * @param[in] vt_* Average time it takes to get from one compartement to another (vector with one element per age group).
-        * @param[in] vmu_* Probabilities to get from one compartement to another (vector with one element per age group).
-        * @param[in] scaling_factor_inf Factor for scaling the confirmed cases to account for estimated undetected cases.
-        * @see mio::read_confirmed_cases_data
-        * @{
-        */
-IOResult<void> read_confirmed_cases_data(
-    std::string const& path, std::vector<int> const& vregion, Date date, std::vector<std::vector<double>>& num_Exposed,
-    std::vector<std::vector<double>>& num_InfectedNoSymptoms, std::vector<std::vector<double>>& num_InfectedSymptoms,
-    std::vector<std::vector<double>>& num_InfectedSevere, std::vector<std::vector<double>>& num_icu,
-    std::vector<std::vector<double>>& num_death, std::vector<std::vector<double>>& num_rec,
-    const std::vector<std::vector<int>>& t_Exposed, const std::vector<std::vector<int>>& t_InfectedNoSymptoms,
-    const std::vector<std::vector<int>>& t_InfectedSymptoms, const std::vector<std::vector<int>>& t_InfectedSevere,
-    const std::vector<std::vector<int>>& t_InfectedCritical, const std::vector<std::vector<double>>& mu_C_R,
-    const std::vector<std::vector<double>>& mu_I_H, const std::vector<std::vector<double>>& mu_H_U,
-    const std::vector<double>& scaling_factor_inf);
-
-IOResult<void> read_confirmed_cases_data(
-    const std::vector<ConfirmedCasesDataEntry>& rki_data, std::vector<int> const& vregion, Date date,
-    std::vector<std::vector<double>>& num_Exposed, std::vector<std::vector<double>>& num_InfectedNoSymptoms,
-    std::vector<std::vector<double>>& num_InfectedSymptoms, std::vector<std::vector<double>>& num_InfectedSevere,
-    std::vector<std::vector<double>>& num_icu, std::vector<std::vector<double>>& num_death,
-    std::vector<std::vector<double>>& num_rec, const std::vector<std::vector<int>>& t_Exposed,
-    const std::vector<std::vector<int>>& t_InfectedNoSymptoms, const std::vector<std::vector<int>>& t_InfectedSymptoms,
-    const std::vector<std::vector<int>>& t_InfectedSevere, const std::vector<std::vector<int>>& t_InfectedCritical,
-    const std::vector<std::vector<double>>& mu_C_R, const std::vector<std::vector<double>>& mu_I_H,
-    const std::vector<std::vector<double>>& mu_H_U, const std::vector<double>& scaling_factor_inf);
-/**@}*/
 
 /**
-        * @brief Reads confirmed cases data and translates data of day t0-delay to recovered compartment.
-        * @param[in] path Path to RKI confirmed cases file.
-        * @param[in] vregion Vector of keys of the region of interest.
-        * @param[in] date Date for which the arrays are initialized.
-        * @param[in, out] num_rec Output vector for number of people in the compartement recovered.
-        * @param[in] delay Number of days in the past the are used to set recovered compartment.
-        * @see mio::read_confirmed_cases_data
-        * @{
-        */
-IOResult<void> read_confirmed_cases_data_fix_recovered(const std::vector<ConfirmedCasesDataEntry>& rki_data,
-                                                       std::vector<int> const& vregion, Date date,
-                                                       std::vector<std::vector<double>>& vnum_rec, double delay = 14.);
-IOResult<void> read_confirmed_cases_data_fix_recovered(std::string const& path, std::vector<int> const& vregion,
-                                                       Date date, std::vector<std::vector<double>>& vnum_rec,
-                                                       double delay = 14.);
-/**@}*/
+ * @brief Reads populations data from rki data.
+ * @param[in] case_data Vector of ConfirmedCasesDataEntry%s.
+ * @param[in] region Key of the region of interest.
+ * @param[in] date Date at which the data is read.
+ * @param[in, out] num_* Output vector for number of people in the corresponding compartement.
+ * @param[in] t_* vector Average time it takes to get from one compartement to another for each age group.
+ * @param[in] mu_* vector Probabilities to get from one compartement to another for each age group.
+ * @param[in] scaling_factor_inf Factors by which to scale the confirmed cases of rki data.
+ */
+IOResult<void>
+compute_confirmed_cases_data(const std::vector<ConfirmedCasesDataEntry>& case_data, const int region, Date date,
+                             std::vector<ScalarType>& num_Exposed, std::vector<ScalarType>& num_InfectedNoSymptoms,
+                             std::vector<ScalarType>& num_InfectedSymptoms, std::vector<ScalarType>& num_InfectedSevere,
+                             std::vector<ScalarType>& num_icu, std::vector<ScalarType>& num_death,
+                             std::vector<ScalarType>& num_rec, const std::vector<int>& t_Exposed,
+                             const std::vector<int>& t_InfectedNoSymptoms, const std::vector<int>& t_InfectedSymptoms,
+                             const std::vector<int>& t_InfectedSevere, const std::vector<int>& t_InfectedCritical,
+                             const std::vector<ScalarType>& mu_C_R, const std::vector<ScalarType>& mu_I_H,
+                             const std::vector<ScalarType>& mu_H_U, const std::vector<ScalarType>& scaling_factor_inf);
 
 /**
-        * @brief Sets the confirmed cases data for a vector of models based on input data.
-        * @param[in, out] model Vector of objects in which the data is set.
-        * @param[in] case_data Vector of case data. Each inner vector represents a different region.
-        * @param[in] region Vector of keys of the region of interest.
-        * @param[in] date Date for which the arrays are initialized.
-        * @param[in] scaling_factor_inf Factors by which to scale the confirmed cases of RKI data.
-        * @param[in] set_death If true, set the number of deaths.
-        */
-template <class Model>
-IOResult<void> set_confirmed_cases_data(std::vector<Model>& model,
-                                        const std::vector<ConfirmedCasesDataEntry>& case_data,
-                                        std::vector<int> const& region, Date date,
-                                        const std::vector<double>& scaling_factor_inf, bool set_death = false)
-{
-    auto num_age_groups = (size_t)model[0].parameters.get_num_groups();
-    assert(scaling_factor_inf.size() == num_age_groups); //TODO: allow vector or scalar valued scaling factors
-    assert(ConfirmedCasesDataEntry::age_group_names.size() == num_age_groups);
-
-    std::vector<std::vector<int>> t_Exposed{model.size()};
-    std::vector<std::vector<int>> t_InfectedNoSymptoms{model.size()};
-    std::vector<std::vector<int>> t_InfectedSymptoms{model.size()};
-    std::vector<std::vector<int>> t_InfectedSevere{model.size()};
-    std::vector<std::vector<int>> t_InfectedCritical{model.size()};
-
-    std::vector<std::vector<double>> mu_C_R{model.size()};
-    std::vector<std::vector<double>> mu_I_H{model.size()};
-    std::vector<std::vector<double>> mu_H_U{model.size()};
-
-    std::vector<std::vector<double>> num_InfectedSymptoms(model.size());
-    std::vector<std::vector<double>> num_death(model.size());
-    std::vector<std::vector<double>> num_rec(model.size());
-    std::vector<std::vector<double>> num_Exposed(model.size());
-    std::vector<std::vector<double>> num_InfectedNoSymptoms(model.size());
-    std::vector<std::vector<double>> num_InfectedSevere(model.size());
-    std::vector<std::vector<double>> num_icu(model.size());
-
-    /*----------- UNVACCINATED -----------*/
-    for (size_t county = 0; county < model.size(); county++) {
-        num_InfectedSymptoms[county]   = std::vector<double>(num_age_groups, 0.0);
-        num_death[county]              = std::vector<double>(num_age_groups, 0.0);
-        num_rec[county]                = std::vector<double>(num_age_groups, 0.0);
-        num_Exposed[county]            = std::vector<double>(num_age_groups, 0.0);
-        num_InfectedNoSymptoms[county] = std::vector<double>(num_age_groups, 0.0);
-        num_InfectedSevere[county]     = std::vector<double>(num_age_groups, 0.0);
-        num_icu[county]                = std::vector<double>(num_age_groups, 0.0);
-        for (size_t group = 0; group < num_age_groups; group++) {
-
-            t_Exposed[county].push_back(static_cast<int>(
-                std::round(model[county].parameters.template get<TimeExposed<double>>()[(AgeGroup)group])));
-            t_InfectedNoSymptoms[county].push_back(static_cast<int>(
-                std::round(model[county].parameters.template get<TimeInfectedNoSymptoms<double>>()[(AgeGroup)group])));
-            t_InfectedSymptoms[county].push_back(static_cast<int>(
-                std::round(model[county].parameters.template get<TimeInfectedSymptoms<double>>()[(AgeGroup)group])));
-            t_InfectedSevere[county].push_back(static_cast<int>(
-                std::round(model[county].parameters.template get<TimeInfectedSevere<double>>()[(AgeGroup)group])));
-            t_InfectedCritical[county].push_back(static_cast<int>(
-                std::round(model[county].parameters.template get<TimeInfectedCritical<double>>()[(AgeGroup)group])));
-
-            mu_C_R[county].push_back(
-                model[county].parameters.template get<RecoveredPerInfectedNoSymptoms<double>>()[(AgeGroup)group]);
-            mu_I_H[county].push_back(
-                model[county].parameters.template get<SeverePerInfectedSymptoms<double>>()[(AgeGroup)group]);
-            mu_H_U[county].push_back(
-                model[county].parameters.template get<CriticalPerSevere<double>>()[(AgeGroup)group]);
-        }
-    }
-
-    BOOST_OUTCOME_TRY(read_confirmed_cases_data(case_data, region, date, num_Exposed, num_InfectedNoSymptoms,
-                                                num_InfectedSymptoms, num_InfectedSevere, num_icu, num_death, num_rec,
-                                                t_Exposed, t_InfectedNoSymptoms, t_InfectedSymptoms, t_InfectedSevere,
-                                                t_InfectedCritical, mu_C_R, mu_I_H, mu_H_U, scaling_factor_inf));
-
-    for (size_t county = 0; county < model.size(); county++) {
-        // if (std::accumulate(
-        //         num_InfectedSymptoms[county].begin(),
-        //         num_InfectedSymptoms[county].end(),
-        //         double(0.0),
-        //         [](const double& a, const double& b) { return evaluate_intermediate<double>(a + b); }
-        //     ) > 0)
-        // {
-        size_t num_groups = (size_t)model[county].parameters.get_num_groups();
-        for (size_t i = 0; i < num_groups; i++) {
-            model[county].populations[{AgeGroup(i), InfectionState::ExposedNaive}] = num_Exposed[county][i];
-            model[county].populations[{AgeGroup(i), InfectionState::InfectedNoSymptomsNaive}] =
-                num_InfectedNoSymptoms[county][i];
-            model[county].populations[{AgeGroup(i), InfectionState::InfectedNoSymptomsNaiveConfirmed}] = 0;
-            model[county].populations[{AgeGroup(i), InfectionState::InfectedSymptomsNaive}] =
-                num_InfectedSymptoms[county][i];
-            model[county].populations[{AgeGroup(i), InfectionState::InfectedSymptomsNaiveConfirmed}] = 0;
-            model[county].populations[{AgeGroup(i), InfectionState::InfectedSevereNaive}] =
-                num_InfectedSevere[county][i];
-            // Only set the number of ICU patients here, if the date is not available in the data.
-            if (!is_divi_data_available(date)) {
-                model[county].populations[{AgeGroup(i), InfectionState::InfectedCriticalNaive}] = num_icu[county][i];
-            }
-            model[county].populations[{AgeGroup(i), InfectionState::SusceptibleImprovedImmunity}] = num_rec[county][i];
-            if (set_death) {
-                // in set_confirmed_cases_data initilization, deaths are now set to 0. In order to visualize
-                // the extrapolated real number of deaths, they have to be set here. In the comparison of data
-                // it has to be paid attention to the fact, the the simulation starts with deaths=0
-                // while this method starts with deaths=number of reported deaths so far...
-                // Additionally, we set the number of reported deaths to DeadNaive since no information on that is
-                // available here.
-                // Do only add deaths after substraction.
-                model[county].populations[{AgeGroup(i), InfectionState::DeadNaive}] = num_death[county][i];
-            }
-        }
-
-        // }
-        if (std::accumulate(num_InfectedSymptoms[county].begin(), num_InfectedSymptoms[county].end(), double(0.0),
-                            [](const double& a, const double& b) {
-                                return evaluate_intermediate<double>(a + b);
-                            }) == 0.0) {
-            log_warning(
-                "No infections for unvaccinated reported on date {} for region {}. Population data has not been set.",
-                date, region[county]);
-        }
-    }
-
-    /*----------- PARTIALLY VACCINATED -----------*/
-    for (size_t county = 0; county < model.size(); county++) {
-        t_InfectedNoSymptoms[county].clear();
-        t_Exposed[county].clear();
-        t_InfectedSymptoms[county].clear();
-        t_InfectedSevere[county].clear();
-        t_InfectedCritical[county].clear();
-
-        mu_C_R[county].clear();
-        mu_I_H[county].clear();
-        mu_H_U[county].clear();
-
-        num_InfectedSymptoms[county]   = std::vector<double>(num_age_groups, 0.0);
-        num_death[county]              = std::vector<double>(num_age_groups, 0.0);
-        num_rec[county]                = std::vector<double>(num_age_groups, 0.0);
-        num_Exposed[county]            = std::vector<double>(num_age_groups, 0.0);
-        num_InfectedNoSymptoms[county] = std::vector<double>(num_age_groups, 0.0);
-        num_InfectedSevere[county]     = std::vector<double>(num_age_groups, 0.0);
-        num_icu[county]                = std::vector<double>(num_age_groups, 0.0);
-        for (size_t group = 0; group < num_age_groups; group++) {
-            double reduc_t = model[0].parameters.template get<ReducTimeInfectedMild<double>>()[(AgeGroup)group];
-            t_Exposed[county].push_back(static_cast<int>(
-                std::round(model[county].parameters.template get<TimeExposed<double>>()[(AgeGroup)group])));
-            t_InfectedNoSymptoms[county].push_back(static_cast<int>(std::round(
-                model[county].parameters.template get<TimeInfectedNoSymptoms<double>>()[(AgeGroup)group] * reduc_t)));
-            t_InfectedSymptoms[county].push_back(static_cast<int>(std::round(
-                model[county].parameters.template get<TimeInfectedSymptoms<double>>()[(AgeGroup)group] * reduc_t)));
-            t_InfectedSevere[county].push_back(static_cast<int>(
-                std::round(model[county].parameters.template get<TimeInfectedSevere<double>>()[(AgeGroup)group])));
-            t_InfectedCritical[county].push_back(static_cast<int>(
-                std::round(model[county].parameters.template get<TimeInfectedCritical<double>>()[(AgeGroup)group])));
-
-            double exp_fac_part_immune =
-                model[county].parameters.template get<ReducExposedPartialImmunity<double>>()[(AgeGroup)group];
-            double inf_fac_part_immune =
-                model[county].parameters.template get<ReducInfectedSymptomsPartialImmunity<double>>()[(AgeGroup)group];
-            double hosp_fac_part_immune =
-                model[county]
-                    .parameters.template get<ReducInfectedSevereCriticalDeadPartialImmunity<double>>()[(AgeGroup)group];
-            double icu_fac_part_immune =
-                model[county]
-                    .parameters.template get<ReducInfectedSevereCriticalDeadPartialImmunity<double>>()[(AgeGroup)group];
-            mu_C_R[county].push_back((
-                1 - inf_fac_part_immune / exp_fac_part_immune *
-                        (1 - model[county]
-                                 .parameters.template get<RecoveredPerInfectedNoSymptoms<double>>()[(AgeGroup)group])));
-            mu_I_H[county].push_back(
-                hosp_fac_part_immune / inf_fac_part_immune *
-                model[county].parameters.template get<SeverePerInfectedSymptoms<double>>()[(AgeGroup)group]);
-            // transfer from H to U, D unchanged.
-            mu_H_U[county].push_back(
-                icu_fac_part_immune / hosp_fac_part_immune *
-                model[county].parameters.template get<CriticalPerSevere<double>>()[(AgeGroup)group]);
-        }
-    }
-
-    BOOST_OUTCOME_TRY(read_confirmed_cases_data(case_data, region, date, num_Exposed, num_InfectedNoSymptoms,
-                                                num_InfectedSymptoms, num_InfectedSevere, num_icu, num_death, num_rec,
-                                                t_Exposed, t_InfectedNoSymptoms, t_InfectedSymptoms, t_InfectedSevere,
-                                                t_InfectedCritical, mu_C_R, mu_I_H, mu_H_U, scaling_factor_inf));
-
-    for (size_t county = 0; county < model.size(); county++) {
-        // if (std::accumulate(
-        //         num_InfectedSymptoms[county].begin(),
-        //         num_InfectedSymptoms[county].end(),
-        //         double(0.0),
-        //         [](const double& a, const double& b) { return evaluate_intermediate<double>(a + b); }
-        //     ) > 0)
-        // {
-        size_t num_groups = (size_t)model[county].parameters.get_num_groups();
-        for (size_t i = 0; i < num_groups; i++) {
-            model[county].populations[{AgeGroup(i), InfectionState::ExposedPartialImmunity}] = num_Exposed[county][i];
-            model[county].populations[{AgeGroup(i), InfectionState::InfectedNoSymptomsPartialImmunity}] =
-                num_InfectedNoSymptoms[county][i];
-            model[county].populations[{AgeGroup(i), InfectionState::InfectedNoSymptomsPartialImmunityConfirmed}] = 0;
-            model[county].populations[{AgeGroup(i), InfectionState::InfectedSymptomsPartialImmunity}] =
-                num_InfectedSymptoms[county][i];
-            model[county].populations[{AgeGroup(i), InfectionState::InfectedSymptomsPartialImmunityConfirmed}] = 0;
-            model[county].populations[{AgeGroup(i), InfectionState::InfectedSeverePartialImmunity}] =
-                num_InfectedSevere[county][i];
-            // Only set the number of ICU patients here, if the date is not available in the data.
-            if (!is_divi_data_available(date)) {
-                model[county].populations[{AgeGroup(i), InfectionState::InfectedCriticalPartialImmunity}] =
-                    num_icu[county][i];
-            }
-        }
-        // }
-        if (std::accumulate(num_InfectedSymptoms[county].begin(), num_InfectedSymptoms[county].end(), double(0.0),
-                            [](const double& a, const double& b) {
-                                return evaluate_intermediate<double>(a + b);
-                            }) == 0.0) {
-            log_warning("No infections for partially vaccinated reported on date {} for region {}. "
-                        "Population data has not been set.",
-                        date, region[county]);
-        }
-    }
-
-    /*----------- FULLY VACCINATED -----------*/
-    for (size_t county = 0; county < model.size(); county++) {
-        t_InfectedNoSymptoms[county].clear();
-        t_Exposed[county].clear();
-        t_InfectedSymptoms[county].clear();
-        t_InfectedSevere[county].clear();
-        t_InfectedCritical[county].clear();
-
-        mu_C_R[county].clear();
-        mu_I_H[county].clear();
-        mu_H_U[county].clear();
-
-        num_InfectedSymptoms[county]   = std::vector<double>(num_age_groups, 0.0);
-        num_death[county]              = std::vector<double>(num_age_groups, 0.0);
-        num_rec[county]                = std::vector<double>(num_age_groups, 0.0);
-        num_Exposed[county]            = std::vector<double>(num_age_groups, 0.0);
-        num_InfectedNoSymptoms[county] = std::vector<double>(num_age_groups, 0.0);
-        num_InfectedSevere[county]     = std::vector<double>(num_age_groups, 0.0);
-        num_icu[county]                = std::vector<double>(num_age_groups, 0.0);
-        for (size_t group = 0; group < num_age_groups; group++) {
-            double reduc_t = model[0].parameters.template get<ReducTimeInfectedMild<double>>()[(AgeGroup)group];
-            t_Exposed[county].push_back(static_cast<int>(
-                std::round(model[county].parameters.template get<TimeExposed<double>>()[(AgeGroup)group])));
-            t_InfectedNoSymptoms[county].push_back(static_cast<int>(std::round(
-                model[county].parameters.template get<TimeInfectedNoSymptoms<double>>()[(AgeGroup)group] * reduc_t)));
-            t_InfectedSymptoms[county].push_back(static_cast<int>(std::round(
-                model[county].parameters.template get<TimeInfectedSymptoms<double>>()[(AgeGroup)group] * reduc_t)));
-            t_InfectedSevere[county].push_back(static_cast<int>(
-                std::round(model[county].parameters.template get<TimeInfectedSevere<double>>()[(AgeGroup)group])));
-            t_InfectedCritical[county].push_back(static_cast<int>(
-                std::round(model[county].parameters.template get<TimeInfectedCritical<double>>()[(AgeGroup)group])));
-
-            double reduc_immune_exp =
-                model[county].parameters.template get<ReducExposedImprovedImmunity<double>>()[(AgeGroup)group];
-            double reduc_immune_inf =
-                model[county].parameters.template get<ReducInfectedSymptomsImprovedImmunity<double>>()[(AgeGroup)group];
-            double reduc_immune_hosp =
-                model[county].parameters.template get<ReducInfectedSevereCriticalDeadImprovedImmunity<double>>()[(
-                    AgeGroup)group];
-            double reduc_immune_icu =
-                model[county].parameters.template get<ReducInfectedSevereCriticalDeadImprovedImmunity<double>>()[(
-                    AgeGroup)group];
-            mu_C_R[county].push_back((
-                1 - reduc_immune_inf / reduc_immune_exp *
-                        (1 - model[county]
-                                 .parameters.template get<RecoveredPerInfectedNoSymptoms<double>>()[(AgeGroup)group])));
-            mu_I_H[county].push_back(
-                reduc_immune_hosp / reduc_immune_inf *
-                model[county].parameters.template get<SeverePerInfectedSymptoms<double>>()[(AgeGroup)group]);
-            // transfer from H to U, D unchanged.
-            mu_H_U[county].push_back(
-                reduc_immune_icu / reduc_immune_hosp *
-                model[county].parameters.template get<CriticalPerSevere<double>>()[(AgeGroup)group]);
-        }
-    }
-
-    BOOST_OUTCOME_TRY(read_confirmed_cases_data(case_data, region, date, num_Exposed, num_InfectedNoSymptoms,
-                                                num_InfectedSymptoms, num_InfectedSevere, num_icu, num_death, num_rec,
-                                                t_Exposed, t_InfectedNoSymptoms, t_InfectedSymptoms, t_InfectedSevere,
-                                                t_InfectedCritical, mu_C_R, mu_I_H, mu_H_U, scaling_factor_inf));
-
-    for (size_t county = 0; county < model.size(); county++) {
-        size_t num_groups = (size_t)model[county].parameters.get_num_groups();
-        for (size_t i = 0; i < num_groups; i++) {
-            model[county].populations[{AgeGroup(i), InfectionState::ExposedImprovedImmunity}] = num_Exposed[county][i];
-            model[county].populations[{AgeGroup(i), InfectionState::InfectedNoSymptomsImprovedImmunity}] =
-                num_InfectedNoSymptoms[county][i];
-            model[county].populations[{AgeGroup(i), InfectionState::InfectedNoSymptomsImprovedImmunityConfirmed}] = 0;
-            model[county].populations[{AgeGroup(i), InfectionState::InfectedSymptomsImprovedImmunity}] =
-                num_InfectedSymptoms[county][i];
-            model[county].populations[{AgeGroup(i), InfectionState::InfectedSymptomsImprovedImmunityConfirmed}] = 0;
-            model[county].populations[{AgeGroup(i), InfectionState::InfectedSevereImprovedImmunity}] =
-                num_InfectedSevere[county][i];
-            // Only set the number of ICU patients here, if the date is not available in the data.
-            if (!is_divi_data_available(date)) {
-                model[county].populations[{AgeGroup(i), InfectionState::InfectedCriticalImprovedImmunity}] =
-                    num_icu[county][i];
-            }
-        }
-
-        if (std::accumulate(num_InfectedSymptoms[county].begin(), num_InfectedSymptoms[county].end(), double(0.0),
-                            [](const double& a, const double& b) {
-                                return evaluate_intermediate<double>(a + b);
-                            }) == 0.0) {
-            log_warning("No infections for vaccinated reported on date {} for region {}. "
-                        "Population data has not been set.",
-                        date, region[county]);
-        }
-    }
-
-    return success();
-}
+ * @brief Reads confirmed cases data and translates data of day t0-delay to recovered compartment.
+ * @param[in] case_data Vector of ConfirmedCasesDataEntry%s.
+ * @param[in] region Key of the region of interest.
+ * @param[in] date Date for which the arrays are initialized.
+ * @param[in] delay Number of days in the past that are used to set recovered compartment.
+ * @return Vector for number of people in the compartment recovered.
+ */
+IOResult<std::vector<ScalarType>>
+compute_confirmed_cases_data_fix_recovered(const std::vector<ConfirmedCasesDataEntry>& case_data, const int region,
+                                           Date date, ScalarType delay = 14.);
 
 /**
-        * @brief sets populations data from a transformed RKI cases file into a Model.
-        * @param[in, out] model vector of objects in which the data is set
-        * @param[in] path Path to transformed RKI cases file
-        * @param[in] region vector of keys of the region of interest
-        * @param[in] date Date for which the arrays are initialized
-        * @param[in] scaling_factor_inf factors by which to scale the confirmed cases of
-        * rki data
-        * @param set_death[in] If true, set the number of deaths.
-        */
-template <class Model>
-IOResult<void> set_confirmed_cases_data(std::vector<Model>& model, const std::string& path,
-                                        std::vector<int> const& region, Date date,
-                                        const std::vector<double>& scaling_factor_inf, bool set_death = false)
-{
-    BOOST_OUTCOME_TRY(auto&& case_data, mio::read_confirmed_cases_data(path));
-    BOOST_OUTCOME_TRY(set_confirmed_cases_data(model, case_data, region, date, scaling_factor_inf, set_death));
-    return success();
-}
+ * @brief Sets populations data from already read case data with multiple age groups into a Model.
+ * @param[in, out] model Model in which the data is set.
+ * @param[in] case_data Vector of ConfirmedCasesDataEntry%s.
+ * @param[in] region Key of the region of interest.
+ * @param[in] date Date at which the data is read.
+ * @param[in] scaling_factor_inf Factors by which to scale the confirmed cases of rki data.
+ * @param[in] set_death If true, set the number of deaths.
+ */
+IOResult<void> set_confirmed_cases_data(Model<ScalarType>& model, const std::vector<ConfirmedCasesDataEntry>& case_data,
+                                        const int region, Date date, const std::vector<ScalarType>& scaling_factor_inf,
+                                        bool set_death = false);
 
 /**
-        * @brief sets populations data from DIVI register into Model
-        * @param[in, out] model vector of objects in which the data is set
-        * @param[in] path Path to transformed DIVI file
-        * @param[in] vregion vector of keys of the regions of interest
-        * @param[in] date Date for which the arrays are initialized
-        * @param[in] scaling_factor_icu factor by which to scale the icu cases of divi data
-        */
-template <class Model>
-IOResult<void> set_divi_data(std::vector<Model>& model, const std::string& path, const std::vector<int>& vregion,
-                             Date date, double scaling_factor_icu)
-{
-    // DIVI dataset will no longer be updated from CW29 2024 on.
-    if (!is_divi_data_available(date)) {
-        log_warning("No DIVI data available for date: {}. "
-                    "ICU compartment will be set based on Case data.",
-                    date);
-        return success();
-    }
-    std::vector<double> sum_mu_I_U(vregion.size(), 0);
-    std::vector<std::vector<double>> mu_I_U{model.size()};
-    for (size_t region = 0; region < vregion.size(); region++) {
-        auto num_groups = model[region].parameters.get_num_groups();
-        for (auto i = AgeGroup(0); i < num_groups; i++) {
-            sum_mu_I_U[region] += model[region].parameters.template get<CriticalPerSevere<double>>()[i] *
-                                  model[region].parameters.template get<SeverePerInfectedSymptoms<double>>()[i];
-            mu_I_U[region].push_back(model[region].parameters.template get<CriticalPerSevere<double>>()[i] *
-                                     model[region].parameters.template get<SeverePerInfectedSymptoms<double>>()[i]);
-        }
-    }
-    std::vector<double> num_icu(model.size(), 0.0);
-    BOOST_OUTCOME_TRY(read_divi_data(path, vregion, date, num_icu));
-
-    for (size_t region = 0; region < vregion.size(); region++) {
-        auto num_groups = model[region].parameters.get_num_groups();
-        for (auto i = AgeGroup(0); i < num_groups; i++) {
-            model[region].populations[{i, InfectionState::InfectedCriticalNaive}] =
-                scaling_factor_icu * num_icu[region] * mu_I_U[region][(size_t)i] / sum_mu_I_U[region];
-        }
-    }
-
-    return success();
-}
+ * @brief Sets the infected populations for the given models based on confirmed cases data. 
+ * Reads the case data from a file and then calls a subfunction that sets the infected population 
+ * for each model.
+ * @param[in, out] model VectorRange of Node%s containing a Model for which the confirmed cases data will be set.
+ * @param[in] path Path to the confirmed cases data file.
+ * @param[in] date Date at which the data is read.
+ * @param[in] scaling_factor_inf Factors by which to scale the confirmed cases of rki data.
+ * @param[in] set_death If true, set the number of deaths.
+ */
+IOResult<void> set_confirmed_cases_data(const mio::VectorRange<Node<Model<ScalarType>>>& model, const std::string& path,
+                                        Date date, const std::vector<ScalarType>& scaling_factor_inf,
+                                        bool set_death = false);
 
 /**
-* @brief sets population data from census data which has been read into num_population
-* @param[in, out] model vector of objects in which the data is set
-* @param[in] num_population vector of population data
-* @param[in] case_data vector of case data. Each inner vector represents a different region
-* @param[in] vregion vector of keys of the regions of interest
-* @param[in] date Date for which the arrays are initialized
-*/
-template <class Model>
-IOResult<void> set_population_data(std::vector<Model>& model, const std::vector<std::vector<double>>& num_population,
-                                   const std::vector<ConfirmedCasesDataEntry>& case_data,
-                                   const std::vector<int>& vregion, Date date)
-{
-    auto num_age_groups = ConfirmedCasesDataEntry::age_group_names.size();
-    std::vector<std::vector<double>> vnum_rec(model.size(), std::vector<double>(num_age_groups, 0.0));
-
-    BOOST_OUTCOME_TRY(read_confirmed_cases_data_fix_recovered(case_data, vregion, date, vnum_rec, 14.));
-
-    for (size_t region = 0; region < vregion.size(); region++) {
-
-        if (std::accumulate(num_population[region].begin(), num_population[region].end(), double(0.0),
-                            [](const double& a, const double& b) {
-                                return evaluate_intermediate<double>(a + b);
-                            }) > 0) {
-            auto num_groups = model[region].parameters.get_num_groups();
-            for (auto i = AgeGroup(0); i < num_groups; i++) {
-
-                double S_v = std::min(
-                    model[region].parameters.template get<DailyFullVaccinations<double>>()[{i, SimulationDay(0)}] +
-                        vnum_rec[region][size_t(i)],
-                    num_population[region][size_t(i)]);
-                double S_pv = std::max(
-                    model[region].parameters.template get<DailyPartialVaccinations<double>>()[{i, SimulationDay(0)}] -
-                        model[region].parameters.template get<DailyFullVaccinations<double>>()[{i, SimulationDay(0)}],
-                    0.0); // use std::max with 0
-                double S;
-                if (num_population[region][size_t(i)] - S_pv - S_v < 0.0) {
-                    log_warning("Number of vaccinated persons greater than population in county {}, age group {}.",
-                                region, size_t(i));
-                    S   = 0.0;
-                    S_v = num_population[region][size_t(i)] - S_pv;
-                }
-                else {
-                    S = num_population[region][size_t(i)] - S_pv - S_v;
-                }
-
-                double denom_E =
-                    1 / (S + S_pv * model[region].parameters.template get<ReducExposedPartialImmunity<double>>()[i] +
-                         S_v * model[region].parameters.template get<ReducExposedImprovedImmunity<double>>()[i]);
-                double denom_C =
-                    1 / (S + S_pv * model[region].parameters.template get<ReducExposedPartialImmunity<double>>()[i] +
-                         S_v * model[region].parameters.template get<ReducExposedImprovedImmunity<double>>()[i]);
-                double denom_I =
-                    1 /
-                    (S +
-                     S_pv * model[region].parameters.template get<ReducInfectedSymptomsPartialImmunity<double>>()[i] +
-                     S_v * model[region].parameters.template get<ReducInfectedSymptomsImprovedImmunity<double>>()[i]);
-                double denom_HU =
-                    1 /
-                    (S +
-                     S_pv * model[region]
-                                .parameters.template get<ReducInfectedSevereCriticalDeadPartialImmunity<double>>()[i] +
-                     S_v * model[region]
-                               .parameters.template get<ReducInfectedSevereCriticalDeadImprovedImmunity<double>>()[i]);
-
-                model[region].populations[{i, InfectionState::ExposedNaive}] =
-                    S * model[region].populations[{i, InfectionState::ExposedNaive}] * denom_E;
-                model[region].populations[{i, InfectionState::ExposedPartialImmunity}] =
-                    S_pv * model[region].parameters.template get<ReducExposedPartialImmunity<double>>()[i] *
-                    model[region].populations[{i, InfectionState::ExposedPartialImmunity}] * denom_E;
-                model[region].populations[{i, InfectionState::ExposedImprovedImmunity}] =
-                    S_v * model[region].parameters.template get<ReducExposedImprovedImmunity<double>>()[i] *
-                    model[region].populations[{i, InfectionState::ExposedImprovedImmunity}] * denom_E;
-
-                model[region].populations[{i, InfectionState::InfectedNoSymptomsNaive}] =
-                    S * model[region].populations[{i, InfectionState::InfectedNoSymptomsNaive}] * denom_C;
-                model[region].populations[{i, InfectionState::InfectedNoSymptomsPartialImmunity}] =
-                    S_pv * model[region].populations[{i, InfectionState::InfectedNoSymptomsPartialImmunity}] * denom_C;
-                model[region].populations[{i, InfectionState::InfectedNoSymptomsImprovedImmunity}] =
-                    S_v * model[region].populations[{i, InfectionState::InfectedNoSymptomsImprovedImmunity}] * denom_C;
-
-                model[region].populations[{i, InfectionState::InfectedNoSymptomsNaiveConfirmed}] =
-                    S * model[region].populations[{i, InfectionState::InfectedNoSymptomsNaiveConfirmed}] * denom_C;
-                model[region].populations[{i, InfectionState::InfectedNoSymptomsPartialImmunityConfirmed}] =
-                    S_pv * model[region].populations[{i, InfectionState::InfectedNoSymptomsPartialImmunityConfirmed}] *
-                    denom_C;
-                model[region].populations[{i, InfectionState::InfectedNoSymptomsImprovedImmunityConfirmed}] =
-                    S_v * model[region].populations[{i, InfectionState::InfectedNoSymptomsImprovedImmunityConfirmed}] *
-                    denom_C;
-
-                model[region].populations[{i, InfectionState::InfectedSymptomsNaive}] =
-                    S * model[region].populations[{i, InfectionState::InfectedSymptomsNaive}] * denom_I;
-                model[region].populations[{i, InfectionState::InfectedSymptomsPartialImmunity}] =
-                    S_pv * model[region].parameters.template get<ReducInfectedSymptomsPartialImmunity<double>>()[i] *
-                    model[region].populations[{i, InfectionState::InfectedSymptomsPartialImmunity}] * denom_I;
-                model[region].populations[{i, InfectionState::InfectedSymptomsImprovedImmunity}] =
-                    S_v * model[region].parameters.template get<ReducInfectedSymptomsImprovedImmunity<double>>()[i] *
-                    model[region].populations[{i, InfectionState::InfectedSymptomsImprovedImmunity}] * denom_I;
-
-                model[region].populations[{i, InfectionState::InfectedSymptomsNaiveConfirmed}] =
-                    S * model[region].populations[{i, InfectionState::InfectedSymptomsNaiveConfirmed}] * denom_I;
-                model[region].populations[{i, InfectionState::InfectedSymptomsPartialImmunityConfirmed}] =
-                    S_pv * model[region].parameters.template get<ReducInfectedSymptomsPartialImmunity<double>>()[i] *
-                    model[region].populations[{i, InfectionState::InfectedSymptomsPartialImmunityConfirmed}] * denom_I;
-                model[region].populations[{i, InfectionState::InfectedSymptomsImprovedImmunityConfirmed}] =
-                    S_v * model[region].parameters.template get<ReducInfectedSymptomsImprovedImmunity<double>>()[i] *
-                    model[region].populations[{i, InfectionState::InfectedSymptomsImprovedImmunityConfirmed}] * denom_I;
-
-                model[region].populations[{i, InfectionState::InfectedSevereNaive}] =
-                    S * model[region].populations[{i, InfectionState::InfectedSevereNaive}] * denom_HU;
-                model[region].populations[{i, InfectionState::InfectedSeverePartialImmunity}] =
-                    S_pv *
-                    model[region].parameters.template get<ReducInfectedSevereCriticalDeadPartialImmunity<double>>()[i] *
-                    model[region].populations[{i, InfectionState::InfectedSeverePartialImmunity}] * denom_HU;
-                model[region].populations[{i, InfectionState::InfectedSevereImprovedImmunity}] =
-                    S_v *
-                    model[region]
-                        .parameters.template get<ReducInfectedSevereCriticalDeadImprovedImmunity<double>>()[i] *
-                    model[region].populations[{i, InfectionState::InfectedSevereImprovedImmunity}] * denom_HU;
-
-                model[region].populations[{i, InfectionState::InfectedCriticalPartialImmunity}] =
-                    S_pv *
-                    model[region].parameters.template get<ReducInfectedSevereCriticalDeadPartialImmunity<double>>()[i] *
-                    model[region].populations[{i, InfectionState::InfectedCriticalNaive}] * denom_HU;
-                model[region].populations[{i, InfectionState::InfectedCriticalImprovedImmunity}] =
-                    S_v *
-                    model[region]
-                        .parameters.template get<ReducInfectedSevereCriticalDeadImprovedImmunity<double>>()[i] *
-                    model[region].populations[{i, InfectionState::InfectedCriticalNaive}] * denom_HU;
-                model[region].populations[{i, InfectionState::InfectedCriticalNaive}] =
-                    S * model[region].populations[{i, InfectionState::InfectedCriticalNaive}] * denom_HU;
-
-                model[region].populations[{i, InfectionState::SusceptibleImprovedImmunity}] =
-                    model[region].parameters.template get<DailyFullVaccinations<double>>()[{i, SimulationDay(0)}] +
-                    model[region].populations[{i, InfectionState::SusceptibleImprovedImmunity}] -
-                    (model[region].populations[{i, InfectionState::InfectedSymptomsNaive}] +
-                     model[region].populations[{i, InfectionState::InfectedSymptomsPartialImmunity}] +
-                     model[region].populations[{i, InfectionState::InfectedSymptomsImprovedImmunity}] +
-                     model[region].populations[{i, InfectionState::InfectedSymptomsNaiveConfirmed}] +
-                     model[region].populations[{i, InfectionState::InfectedSymptomsPartialImmunityConfirmed}] +
-                     model[region].populations[{i, InfectionState::InfectedSymptomsImprovedImmunityConfirmed}] +
-                     model[region].populations[{i, InfectionState::InfectedSevereNaive}] +
-                     model[region].populations[{i, InfectionState::InfectedSeverePartialImmunity}] +
-                     model[region].populations[{i, InfectionState::InfectedSevereImprovedImmunity}] +
-                     model[region].populations[{i, InfectionState::InfectedCriticalNaive}] +
-                     model[region].populations[{i, InfectionState::InfectedCriticalPartialImmunity}] +
-                     model[region].populations[{i, InfectionState::InfectedCriticalImprovedImmunity}] +
-                     model[region].populations[{i, InfectionState::DeadNaive}] +
-                     model[region].populations[{i, InfectionState::DeadPartialImmunity}] +
-                     model[region].populations[{i, InfectionState::DeadImprovedImmunity}]);
-
-                model[region].populations[{i, InfectionState::SusceptibleImprovedImmunity}] = std::min(
-                    S_v - model[region].populations[{i, InfectionState::ExposedImprovedImmunity}] -
-                        model[region].populations[{i, InfectionState::InfectedNoSymptomsImprovedImmunity}] -
-                        model[region].populations[{i, InfectionState::InfectedNoSymptomsImprovedImmunityConfirmed}] -
-                        model[region].populations[{i, InfectionState::InfectedSymptomsImprovedImmunity}] -
-                        model[region].populations[{i, InfectionState::InfectedSymptomsImprovedImmunityConfirmed}] -
-                        model[region].populations[{i, InfectionState::InfectedSevereImprovedImmunity}] -
-                        model[region].populations[{i, InfectionState::InfectedCriticalImprovedImmunity}] -
-                        model[region].populations[{i, InfectionState::DeadImprovedImmunity}],
-                    std::max(0.0, double(model[region].populations[{i, InfectionState::SusceptibleImprovedImmunity}])));
-
-                model[region].populations[{i, InfectionState::SusceptiblePartialImmunity}] = std::max(
-                    0.0,
-                    S_pv - model[region].populations[{i, InfectionState::ExposedPartialImmunity}] -
-                        model[region].populations[{i, InfectionState::InfectedNoSymptomsPartialImmunity}] -
-                        model[region].populations[{i, InfectionState::InfectedNoSymptomsPartialImmunityConfirmed}] -
-                        model[region].populations[{i, InfectionState::InfectedSymptomsPartialImmunity}] -
-                        model[region].populations[{i, InfectionState::InfectedSymptomsPartialImmunityConfirmed}] -
-                        model[region].populations[{i, InfectionState::InfectedSeverePartialImmunity}] -
-                        model[region].populations[{i, InfectionState::InfectedCriticalPartialImmunity}] -
-                        model[region].populations[{i, InfectionState::DeadPartialImmunity}]);
-
-                model[region].populations.template set_difference_from_group_total<AgeGroup>(
-                    {i, InfectionState::SusceptibleNaive}, num_population[region][size_t(i)]);
-            }
-
-            for (auto i = AgeGroup(0); i < AgeGroup(6); i++) {
-                for (auto j = Index<InfectionState>(0); j < InfectionState::Count; ++j) {
-                    if (model[region].populations[{i, j}] < 0) {
-                        log_warning("Compartment at age group {}, infection state {}, is negative: {}", size_t(i),
-                                    size_t(j), model[region].populations[{i, j}] / num_population[region][size_t(i)]);
-                    }
-                }
-            }
-        }
-        else {
-            log_warning("No population data available for region " + std::to_string(region) +
-                        ". Population data has not been set.");
-        }
-    }
+ * @brief Sets the population data for the given model based on the provided population distribution.
+ * @param[in, out] model Model in which the data is set.
+ * @param[in] num_population Vector of population data for the region of interest.
+ * @param[in] region Key of the region of interest.
+ * @param[in] date Date for which the arrays are initialized.
+ */
+IOResult<void> set_population_data(Model<ScalarType>& model, const std::vector<ScalarType>& num_population,
+                                   const int region, const std::vector<ScalarType>& num_rec);
 
-    return success();
-}
+/**
+ * @brief Reads population data from a file and sets it for the each given model.
+ * @param[in, out] model VectorRange of Node%s containing a Model for which the confirmed cases data will be set.
+ * @param[in] path Path to population data file.
+ * @param[in] path_rki Path to rki cases data file.
+ * @param[in] date Date for which the arrays are initialized.
+ */
+IOResult<void> set_population_data(const mio::VectorRange<Node<Model<ScalarType>>>& model, const std::string& path,
+                                   const std::string& path_rki, Date date);
 
 /**
-* @brief Sets population data from census data which has been read into num_population.
-* @param[in, out] model Vector of objects in which the data is set.
-* @param[in] path Path to population data file.
-* @param[in] path_rki Path to RKI cases data file.
-* @param[in] vregion Vector of keys of the regions of interest.
-* @param[in] date Date for which the arrays are initialized.
-*/
-template <class Model>
-IOResult<void> set_population_data(std::vector<Model>& model, const std::string& path, const std::string& path_rki,
-                                   const std::vector<int>& vregion, Date date)
-{
-    BOOST_OUTCOME_TRY(auto&& num_population, read_population_data(path, vregion));
-    BOOST_OUTCOME_TRY(auto&& rki_data, mio::read_confirmed_cases_data(path_rki));
+ * @brief Sets ICU data into a Model, distributed across age groups.
+ * @param[in, out] model Model in which the data is set.
+ * @param[in] num_icu ICU data for the region of interest.
+ * @param[in] scaling_factor_icu Scaling factor for reported ICU cases.
+ */
+IOResult<void> set_divi_data(Model<ScalarType>& model, const ScalarType num_icu, ScalarType scaling_factor_icu);
 
-    BOOST_OUTCOME_TRY(set_population_data(model, num_population, rki_data, vregion, date));
-    return success();
-}
+/**
+ * @brief Sets ICU data from DIVI data into the a vector of models, distributed across age groups.
+ * 
+ * This function reads DIVI data from a file, computes the number of individuals in critical condition (ICU)
+ * for each region, and sets these values in the model. The ICU cases are distributed across age groups
+ * using the transition probabilities from severe to critical.
+ * 
+ * @param[in, out] model VectorRange of Node%s each containing a Model for which the confirmed cases data will be set.
+ * @param[in] path Path to transformed DIVI file.
+ * @param[in] date Date at which the data is read.
+ * @param[in] scaling_factor_icu Scaling factor for reported ICU cases.
+ */
+IOResult<void> set_divi_data(const mio::VectorRange<Node<Model<ScalarType>>>& model, const std::string& path, Date date,
+                             ScalarType scaling_factor_icu);
 
 /**
- * @brief Sets vaccination data for models stored in a vector.
- *
- * @tparam FP Floating point type used in the Model objects.
- * @param[in, out] model Vector of Model objects in which the vaccination data is set.
+ * @brief Sets vaccination data into a Model.
+ * @tparam FP Floating point type (default: double).
+ * @param[in, out] model Model in which the data is set.
  * @param[in] vacc_data Vector of VaccinationDataEntry objects containing the vaccination data.
  * @param[in] date Start date for the simulation.
- * @param[in] vregion Vector of region identifiers.
  * @param[in] num_days Number of days for which the simulation is run.
+ * 
+ * @return An IOResult indicating success or failure.
  */
-template <typename FP = double>
-IOResult<void> set_vaccination_data(std::vector<Model<FP>>& model, const std::vector<VaccinationDataEntry>& vacc_data,
-                                    Date date, const std::vector<int>& vregion, int num_days)
+template <typename FP>
+IOResult<void> set_vaccination_data(Model<FP>& model, const std::vector<VaccinationDataEntry>& vacc_data, Date date,
+                                    int num_days)
 {
-    auto num_groups = model[0].parameters.get_num_groups();
-
-    // type conversion from UncertainValue -> FP -> int
-    auto days_until_effective1 = static_cast<int>(
-        static_cast<FP>(model[0].parameters.template get<DaysUntilEffectivePartialImmunity<FP>>()[AgeGroup(0)]));
-    auto days_until_effective2 = static_cast<int>(
-        static_cast<FP>(model[0].parameters.template get<DaysUntilEffectiveImprovedImmunity<FP>>()[AgeGroup(0)]));
-    auto vaccination_distance =
-        static_cast<int>(static_cast<FP>(model[0].parameters.template get<VaccinationGap<FP>>()[AgeGroup(0)]));
-
-    // iterate over regions (e.g., counties)
-    for (size_t i = 0; i < model.size(); ++i) {
-        // iterate over age groups in region
-        for (auto g = AgeGroup(0); g < num_groups; ++g) {
-
-            model[i].parameters.template get<DailyPartialVaccinations<FP>>().resize(SimulationDay(num_days + 1));
-            model[i].parameters.template get<DailyFullVaccinations<FP>>().resize(SimulationDay(num_days + 1));
-            for (auto d = SimulationDay(0); d < SimulationDay(num_days + 1); ++d) {
-                model[i].parameters.template get<DailyPartialVaccinations<FP>>()[{g, d}] = 0.0;
-                model[i].parameters.template get<DailyFullVaccinations<FP>>()[{g, d}]    = 0.0;
-            }
-        }
-    }
+    using std::floor;
 
     auto max_date_entry = std::max_element(vacc_data.begin(), vacc_data.end(), [](auto&& a, auto&& b) {
         return a.date < b.date;
@@ -739,91 +181,84 @@ IOResult<void> set_vaccination_data(std::vector<Model<FP>>& model, const std::ve
                     min_date, max_date);
     }
 
+    // type conversion from UncertainValue -> FP -> int
+    auto days_until_effective1 = static_cast<int>(
+        floor(static_cast<FP>(model.parameters.template get<DaysUntilEffectivePartialImmunity<FP>>()[AgeGroup(0)])));
+    auto days_until_effective2 = static_cast<int>(
+        floor(static_cast<FP>(model.parameters.template get<DaysUntilEffectiveImprovedImmunity<FP>>()[AgeGroup(0)])));
+    auto vaccination_distance =
+        static_cast<int>(floor(static_cast<FP>(model.parameters.template get<VaccinationGap<FP>>()[AgeGroup(0)])));
+
     for (auto&& vacc_data_entry : vacc_data) {
-        auto it      = std::find_if(vregion.begin(), vregion.end(), [&vacc_data_entry](auto&& r) {
-            return r == 0 || (vacc_data_entry.county_id && vacc_data_entry.county_id == regions::CountyId(r)) ||
-                   (vacc_data_entry.state_id && vacc_data_entry.state_id == regions::StateId(r)) ||
-                   (vacc_data_entry.district_id && vacc_data_entry.district_id == regions::DistrictId(r));
-        });
         auto date_df = vacc_data_entry.date;
-        if (it != vregion.end()) {
-            auto region_idx = size_t(it - vregion.begin());
-            auto age        = vacc_data_entry.age_group;
-
-            for (size_t d = 0; d < (size_t)num_days + 1; ++d) {
-                int days_plus;
-                // In the following, second dose means previous 'full immunization', now 'Grundimmunisierung'.
-                // ---
-                // date: start_date of the simulation (Input from IO call read_input_data_county_vaccmodel())
-                // d: day of simulation, counted from 0 to num_days (for which we need (approximated) vaccination numbers)
-                // root[i]["Vacc_completed"]: accumulated number of total second doses up to day date_df;
-                //                               taken from input dataframe, single value, per county and age group
-                // ----
-                // An averaged distance between first and second doses (vaccination_distance) is assumed in the following
-                // and the first doses are computed based on the second doses given 'vaccination_distance' days later.
-                // ----
-                // a person whose second dose is reported at start_date + simulation_day - days_until_effective1 + vaccination_distance
-                // had the first dose on start_date + simulation_day - days_until_effective1. Furthermore, he/she has the full protection
-                // of the first dose at day X = start_date + simulation_day
-                // Storing its value in get<DailyPartialVaccinations>() will eventually (in the simulation)
-                // transfer the difference (between get<DailyPartialVaccinations>() at d and d-1) of
-                // N susceptible individuals to 'Susceptible Partially Vaccinated' state at day d; see secir_vaccinated.h
-                auto offset_first_date =
-                    offset_date_by_days(date, (int)d - days_until_effective1 + vaccination_distance);
-                if (max_date >= offset_first_date) {
-                    // Option 1: considered offset_first_date is available in input data frame
-                    if (date_df == offset_first_date) {
-                        model[region_idx]
-                            .parameters.template get<DailyPartialVaccinations<FP>>()[{age, SimulationDay(d)}] =
-                            vacc_data_entry.num_vaccinations_completed;
-                    }
+        AgeGroup age = vacc_data_entry.age_group;
+
+        for (size_t d = 0; d < (size_t)num_days + 1; ++d) {
+            int days_plus;
+            // In the following, second dose means previous 'full immunization', now 'Grundimmunisierung'.
+            // ---
+            // date: start_date of the simulation (Input from IO call read_input_data_county_vaccmodel())
+            // d: day of simulation, counted from 0 to num_days (for which we need (approximated) vaccination numbers)
+            // root[i]["Vacc_completed"]: accumulated number of total second doses up to day date_df;
+            //                               taken from input dataframe, single value, per county and age group
+            // ----
+            // An averaged distance between first and second doses (vaccination_distance) is assumed in the following
+            // and the first doses are computed based on the second doses given 'vaccination_distance' days later.
+            // ----
+            // a person whose second dose is reported at start_date + simulation_day - days_until_effective1 + vaccination_distance
+            // had the first dose on start_date + simulation_day - days_until_effective1. Furthermore, he/she has the full protection
+            // of the first dose at day X = start_date + simulation_day
+            // Storing its value in get<DailyPartialVaccinations>() will eventually (in the simulation)
+            // transfer the difference (between get<DailyPartialVaccinations>() at d and d-1) of
+            // N susceptible individuals to 'Susceptible Partially Vaccinated' state at day d; see secir_vaccinated.h
+            auto offset_first_date = offset_date_by_days(date, (int)d - days_until_effective1 + vaccination_distance);
+            if (max_date >= offset_first_date) {
+                // Option 1: considered offset_first_date is available in input data frame
+                if (date_df == offset_first_date) {
+                    model.parameters.template get<DailyPartialVaccinations<FP>>()[{age, SimulationDay(d)}] =
+                        vacc_data_entry.num_vaccinations_completed;
+                }
+            }
+            else { // offset_first_date > max_date
+                // Option 2: considered offset_first_date is NOT available in input data frame
+                // Here, a constant number of first and second doses is assumed, i.e.,
+                // the the number of vaccinationes at day d (N days after max_date) will be:
+                // total number of vaccinations up to day max_date + N * number of vaccinations ON max_date
+                // (where the latter is computed as the difference between the total number at max_date and max_date-1)
+                days_plus = get_offset_in_days(offset_first_date, max_date);
+                if (date_df == offset_date_by_days(max_date, -1)) {
+                    model.parameters.template get<DailyPartialVaccinations<FP>>()[{age, SimulationDay(d)}] -=
+                        days_plus * vacc_data_entry.num_vaccinations_completed;
                 }
-                else { // offset_first_date > max_date
-                    // Option 2: considered offset_first_date is NOT available in input data frame
-                    // Here, a constant number of first and second doses is assumed, i.e.,
-                    // the the number of vaccinationes at day d (N days after max_date) will be:
-                    // total number of vaccinations up to day max_date + N * number of vaccinations ON max_date
-                    // (where the latter is computed as the difference between the total number at max_date and max_date-1)
-                    days_plus = get_offset_in_days(offset_first_date, max_date);
-                    if (date_df == offset_date_by_days(max_date, -1)) {
-                        model[region_idx]
-                            .parameters.template get<DailyPartialVaccinations<FP>>()[{age, SimulationDay(d)}] -=
-                            days_plus * vacc_data_entry.num_vaccinations_completed;
-                    }
-                    else if (date_df == max_date) {
-                        model[region_idx]
-                            .parameters.template get<DailyPartialVaccinations<FP>>()[{age, SimulationDay(d)}] +=
-                            (days_plus + 1) * vacc_data_entry.num_vaccinations_completed;
-                    }
+                else if (date_df == max_date) {
+                    model.parameters.template get<DailyPartialVaccinations<FP>>()[{age, SimulationDay(d)}] +=
+                        (days_plus + 1) * vacc_data_entry.num_vaccinations_completed;
                 }
+            }
 
-                // a person whose second dose is reported at start_date + simulation_day - days_until_effective2
-                // has the full protection of the second dose at day X = start_date + simulation_day
-                // Storing its value in get<DailyFullVaccinations>() will eventually (in the simulation)
-                // transfer the difference (between get<DailyFullVaccinations>() at d and d-1) of
-                // N susceptible, partially vaccinated individuals to 'SusceptibleImprovedImmunity' state at day d; see secir_vaccinated.h
-                auto offset_full_date = offset_date_by_days(date, (int)d - days_until_effective2);
-                if (max_date >= offset_full_date) {
-                    // Option 1: considered offset_full_date is available in input data frame
-                    if (date_df == offset_full_date) {
-                        model[region_idx]
-                            .parameters.template get<DailyFullVaccinations<FP>>()[{age, SimulationDay(d)}] =
-                            vacc_data_entry.num_vaccinations_completed;
-                    }
+            // a person whose second dose is reported at start_date + simulation_day - days_until_effective2
+            // has the full protection of the second dose at day X = start_date + simulation_day
+            // Storing its value in get<DailyFullVaccinations>() will eventually (in the simulation)
+            // transfer the difference (between get<DailyFullVaccinations>() at d and d-1) of
+            // N susceptible, partially vaccinated individuals to 'SusceptibleImprovedImmunity' state at day d; see secir_vaccinated.h
+            auto offset_full_date = offset_date_by_days(date, (int)d - days_until_effective2);
+            if (max_date >= offset_full_date) {
+                // Option 1: considered offset_full_date is available in input data frame
+                if (date_df == offset_full_date) {
+                    model.parameters.template get<DailyFullVaccinations<FP>>()[{age, SimulationDay(d)}] =
+                        vacc_data_entry.num_vaccinations_completed;
+                }
+            }
+            else { // offset_full_date > max_full_date
+                // Option 2: considered offset_full_date is NOT available in input data frame
+                days_plus = get_offset_in_days(offset_full_date, max_date);
+                if (date_df == offset_date_by_days(max_date, -1)) {
+                    model.parameters.template get<DailyFullVaccinations<FP>>()[{age, SimulationDay(d)}] -=
+                        days_plus * vacc_data_entry.num_vaccinations_completed;
                 }
-                else { // offset_full_date > max_full_date
-                    // Option 2: considered offset_full_date is NOT available in input data frame
-                    days_plus = get_offset_in_days(offset_full_date, max_date);
-                    if (date_df == offset_date_by_days(max_date, -1)) {
-                        model[region_idx]
-                            .parameters.template get<DailyFullVaccinations<FP>>()[{age, SimulationDay(d)}] -=
-                            days_plus * vacc_data_entry.num_vaccinations_completed;
-                    }
-                    else if (date_df == max_date) {
-                        model[region_idx]
-                            .parameters.template get<DailyFullVaccinations<FP>>()[{age, SimulationDay(d)}] +=
-                            (days_plus + 1) * vacc_data_entry.num_vaccinations_completed;
-                    }
+                else if (date_df == max_date) {
+                    model.parameters.template get<DailyFullVaccinations<FP>>()[{age, SimulationDay(d)}] +=
+                        (days_plus + 1) * vacc_data_entry.num_vaccinations_completed;
                 }
             }
         }
@@ -833,260 +268,152 @@ IOResult<void> set_vaccination_data(std::vector<Model<FP>>& model, const std::ve
 
 /**
  * @brief Reads vaccination data from a file and sets it for each model.
- *
- * @tparam FP Floating point type used in the Model objects.
- * @param[in, out] model Vector of Model objects in which the vaccination data is set.
+ * @tparam FP Floating point type (default: double).
+ * @param[in, out] model VectorRange of Node%s each containing a Model in which the vaccination data is set.
  * @param[in] path Path to vaccination data file.
  * @param[in] date Start date for the simulation.
- * @param[in] vregion Vector of region identifiers.
  * @param[in] num_days Number of days for which the simulation is run.
+ * 
+ * @return An IOResult indicating success or failure.
  */
-template <typename FP = double>
-IOResult<void> set_vaccination_data(std::vector<Model<FP>>& model, const std::string& path, Date date,
-                                    const std::vector<int>& vregion, int num_days)
+template <typename FP>
+IOResult<void> set_vaccination_data(const mio::VectorRange<Node<Model<FP>>>& model, const std::string& path, Date date,
+                                    int num_days)
 {
+    // Set vaccination data to 0 for all models
+    for (auto& m : model) {
+        m.property.parameters.template get<DailyPartialVaccinations<FP>>().resize(SimulationDay(num_days + 1));
+        m.property.parameters.template get<DailyFullVaccinations<FP>>().resize(SimulationDay(num_days + 1));
+        for (auto d = SimulationDay(0); d < SimulationDay(num_days + 1); ++d) {
+            for (auto a = AgeGroup(0); a < m.property.parameters.get_num_groups(); ++a) {
+                m.property.parameters.template get<DailyPartialVaccinations<FP>>()[{a, d}] = 0.0;
+                m.property.parameters.template get<DailyFullVaccinations<FP>>()[{a, d}]    = 0.0;
+            }
+        }
+    }
+
     // Check if vaccination data is available for the given date range
     auto end_date = offset_date_by_days(date, num_days);
     if (!is_vaccination_data_available(date, end_date)) {
         log_warning("No vaccination data available in range from {} to {}. "
                     "Vaccination data will be set to 0.",
                     date, end_date);
-        // Set vaccination data to 0 for all models
-        for (auto& m : model) {
-            m.parameters.template get<DailyPartialVaccinations<FP>>().resize(SimulationDay(num_days + 1));
-            m.parameters.template get<DailyFullVaccinations<FP>>().resize(SimulationDay(num_days + 1));
-
-            for (auto d = SimulationDay(0); d < SimulationDay(num_days + 1); ++d) {
-                for (auto a = AgeGroup(0); a < m.parameters.get_num_groups(); ++a) {
-                    m.parameters.template get<DailyPartialVaccinations<FP>>()[{a, d}] = 0.0;
-                    m.parameters.template get<DailyFullVaccinations<FP>>()[{a, d}]    = 0.0;
-                }
-            }
-        }
         return success();
     }
     BOOST_OUTCOME_TRY(auto&& vacc_data, read_vaccination_data(path));
-    BOOST_OUTCOME_TRY(set_vaccination_data(model, vacc_data, date, vregion, num_days));
-    return success();
-}
-
-} // namespace details
-
-#ifdef MEMILIO_HAS_HDF5
-
-/**
-* @brief Uses the initialisation method, which uses the reported data to set the initial conditions for the model for a given day.
-* The initialisation is applied for a predefined number of days and finally saved in a timeseries for each region. In the end,
-* we save the files "Results_rki.h5" and "Results_rki_sum.h5" in the results_dir.
-* Results_rki.h5 contains a time series for each region and Results_rki_sum.h5 contains the sum of all regions.
-* @param[in] models Vector of models in which the data is set. Copy is made to avoid changing the original model.
-* @param[in] results_dir Path to result files.
-* @param[in] counties Vector of keys of the counties of interest.
-* @param[in] date Date for which the data should be read.
-* @param[in] scaling_factor_inf Factors by which to scale the confirmed cases of rki data.
-* @param[in] scaling_factor_icu Factor by which to scale the icu cases of divi data.
-* @param[in] num_days Number of days to be simulated/initialized.
-* @param[in] divi_data_path Path to DIVI file.
-* @param[in] confirmed_cases_path Path to confirmed cases file.
-* @param[in] population_data_path Path to population data file.
-* @param[in] vaccination_data_path Path to vaccination data file.
-*/
-template <class Model>
-IOResult<void> export_input_data_county_timeseries(
-    std::vector<Model> models, const std::string& results_dir, const std::vector<int>& counties, Date date,
-    const std::vector<double>& scaling_factor_inf, const double scaling_factor_icu, const int num_days,
-    const std::string& divi_data_path, const std::string& confirmed_cases_path, const std::string& population_data_path,
-    const std::string& vaccination_data_path = "")
-{
-    const auto num_groups = (size_t)models[0].parameters.get_num_groups();
-    assert(scaling_factor_inf.size() == num_groups);
-    assert(num_groups == ConfirmedCasesDataEntry::age_group_names.size());
-    assert(models.size() == counties.size());
-    std::vector<TimeSeries<double>> extrapolated_data(
-        models.size(), TimeSeries<double>::zero(num_days + 1, (size_t)InfectionState::Count * num_groups));
-
-    BOOST_OUTCOME_TRY(auto&& case_data, read_confirmed_cases_data(confirmed_cases_path));
-    BOOST_OUTCOME_TRY(auto&& population_data, read_population_data(population_data_path, counties));
 
-    // empty vector if set_vaccination_data is not set
-    std::vector<VaccinationDataEntry> vacc_data;
-    if (!vaccination_data_path.empty()) {
-        BOOST_OUTCOME_TRY(vacc_data, read_vaccination_data(vaccination_data_path));
-    }
-
-    for (int t = 0; t <= num_days; ++t) {
-        auto offset_day = offset_date_by_days(date, t);
-
-        if (!vaccination_data_path.empty()) {
-            BOOST_OUTCOME_TRY(details::set_vaccination_data(models, vacc_data, offset_day, counties, num_days));
-        }
-
-        // TODO: Reuse more code, e.g., set_divi_data (in secir) and a set_divi_data (here) only need a different ModelType.
-        // TODO: add option to set ICU data from confirmed cases if DIVI or other data is not available.
-        BOOST_OUTCOME_TRY(details::set_divi_data(models, divi_data_path, counties, offset_day, scaling_factor_icu));
-
-        BOOST_OUTCOME_TRY(
-            details::set_confirmed_cases_data(models, case_data, counties, offset_day, scaling_factor_inf, true));
-        BOOST_OUTCOME_TRY(details::set_population_data(models, population_data, case_data, counties, offset_day));
-
-        for (size_t r = 0; r < counties.size(); r++) {
-            extrapolated_data[r][t] = models[r].get_initial_values();
-            // in set_population_data the number of death individuals is subtracted from the SusceptibleImprovedImmunity compartment.
-            // Since we should be independent whether we consider them or not, we add them back here before we save the data.
-            for (size_t age = 0; age < num_groups; age++) {
-                extrapolated_data[r][t][(size_t)InfectionState::SusceptibleImprovedImmunity +
-                                        age * (size_t)InfectionState::Count] +=
-                    extrapolated_data[r][t][(size_t)InfectionState::DeadNaive + age * (size_t)InfectionState::Count];
-            }
+    // Sort vacc_data into regions and ignore once with no region associated
+    std::vector<std::vector<VaccinationDataEntry>> vvacc_data{model.size()};
+    for (auto&& vacc_data_entry : vacc_data) {
+        auto it = std::find_if(model.begin(), model.end(), [&vacc_data_entry](auto&& m) {
+            return m.id == 0 || (vacc_data_entry.county_id && vacc_data_entry.county_id == regions::CountyId(m.id)) ||
+                   (vacc_data_entry.state_id && vacc_data_entry.state_id == regions::StateId(m.id)) ||
+                   (vacc_data_entry.district_id && vacc_data_entry.district_id == regions::DistrictId(m.id));
+        });
+        if (it != model.end()) {
+            auto region_idx = size_t(it - model.begin());
+            vvacc_data[region_idx].push_back(vacc_data_entry);
         }
     }
-    BOOST_OUTCOME_TRY(save_result(extrapolated_data, counties, static_cast<int>(num_groups),
-                                  path_join(results_dir, "Results_rki.h5")));
 
-    auto extrapolated_rki_data_sum = sum_nodes(std::vector<std::vector<TimeSeries<double>>>{extrapolated_data});
-    BOOST_OUTCOME_TRY(save_result({extrapolated_rki_data_sum[0][0]}, {0}, static_cast<int>(num_groups),
-                                  path_join(results_dir, "Results_rki_sum.h5")));
+    for (size_t region_idx = 0; region_idx < model.size(); ++region_idx) {
+        BOOST_OUTCOME_TRY(set_vaccination_data<FP>(model[region_idx].property, vvacc_data[region_idx], date, num_days));
+    }
 
     return success();
 }
-#else
-template <class Model>
-IOResult<void> export_input_data_county_timeseries(std::vector<Model>, const std::string&, const std::vector<int>&,
-                                                   Date, const std::vector<double>&, const double, const int,
-                                                   const std::string&, const std::string&, const std::string&,
-                                                   const std::string&)
-{
-    mio::log_warning("HDF5 not available. Cannot export time series of extrapolated real data.");
-    return success();
-}
 
-#endif //MEMILIO_HAS_HDF5
+} // namespace details
 
 /**
-    * Reads compartments for German counties at a specified date from data files.
-    * Estimates all compartments from available data using the model parameters, so the
-    * model parameters must be set before calling this function.
-    * Uses data files that contain centered 7-day moving average.
-    * @param[in, out] model Vector of SECIRVVS models, one per county.
-    * @param[in] date Date for which the data should be read.
-    * @param[in] county Ids of the counties.
-    * @param[in] scaling_factor_inf Factor of confirmed cases to account for undetected cases in each county.
-    * @param[in] scaling_factor_icu Factor of ICU cases to account for underreporting.
-    * @param[in] pydata_dir Directory that contains the data files.
-    * @param[in] num_days Number of days to be simulated; required to load data for vaccinations during the simulation.
-    * @param[in] export_time_series If true, reads data for each day of simulation and writes it in the same directory as the input files.
-    */
-template <class Model>
-IOResult<void> read_input_data_county(std::vector<Model>& model, Date date, const std::vector<int>& county,
-                                      const std::vector<double>& scaling_factor_inf, double scaling_factor_icu,
-                                      const std::string& pydata_dir, int num_days, bool export_time_series = false)
-{
-    BOOST_OUTCOME_TRY(details::set_vaccination_data(model, path_join(pydata_dir, "vacc_county_ageinf_ma7.json"), date,
-                                                    county, num_days));
-
-    // TODO: Reuse more code, e.g., set_divi_data (in secir) and a set_divi_data (here) only need a different ModelType.
-    // TODO: add option to set ICU data from confirmed cases if DIVI or other data is not available.
-    BOOST_OUTCOME_TRY(
-        details::set_divi_data(model, path_join(pydata_dir, "county_divi_ma7.json"), county, date, scaling_factor_icu));
-
-    BOOST_OUTCOME_TRY(details::set_confirmed_cases_data(model, path_join(pydata_dir, "cases_all_county_age_ma7.json"),
-                                                        county, date, scaling_factor_inf));
-    BOOST_OUTCOME_TRY(details::set_population_data(model, path_join(pydata_dir, "county_current_population.json"),
-                                                   path_join(pydata_dir, "cases_all_county_age_ma7.json"), county,
-                                                   date));
-
-    if (export_time_series) {
-        // Use only if extrapolated real data is needed for comparison. EXPENSIVE !
-        // Run time equals run time of the previous functions times the num_days !
-        // (This only represents the vectorization of the previous function over all simulation days...)
-        log_warning("Exporting time series of extrapolated real data. This may take some minutes. "
-                    "For simulation runs over the same time period, deactivate it.");
-        BOOST_OUTCOME_TRY(export_input_data_county_timeseries(
-            model, pydata_dir, county, date, scaling_factor_inf, scaling_factor_icu, num_days,
-            path_join(pydata_dir, "county_divi_ma7.json"), path_join(pydata_dir, "cases_all_county_age_ma7.json"),
-            path_join(pydata_dir, "county_current_population.json"),
-            path_join(pydata_dir, "vacc_county_ageinf_ma7.json")));
-    }
+ * @brief Reads compartments for geographic units at a specified date from data files.
+ *
+ * This function estimates all compartments from available data using the provided model parameters.
+ *
+ * @param[in,out] model VectorRange of Node%s each containing a Model to be initialized with data.
+ * @param[in] date Date for which the data should be read.
+ * @param[in] scaling_factor_inf Vector of scaling factors for confirmed cases.
+ * @param[in] scaling_factor_icu Scaling factor for ICU cases.
+ * @param[in] num_days Number of days to simulate.
+ * @param[in] epidata_filenames Object containing the input data file paths.
+ * @param[in] set_death If true, set the number of deaths.
+ *
+ * @return An IOResult indicating success or failure.
+ */
+IOResult<void> read_input_data(const mio::VectorRange<Node<Model<ScalarType>>>& model, Date date,
+                               const std::vector<ScalarType>& scaling_factor_inf, ScalarType scaling_factor_icu,
+                               int num_days, const mio::regions::de::EpidataFilenames& epidata_filenames,
+                               bool set_death = false);
 
-    return success();
-}
+#ifdef MEMILIO_HAS_HDF5
 
 /**
-    * Reads compartments for German counties at a specified date from data files.
-    * Estimates all compartments from available data using the model parameters, so the
-    * model parameters must be set before calling this function.
-    * Uses data files that contain centered 7-day moving average.
-    * @param[in, out] model Vector of SECIRVVS models, one per county.
-    * @param[in] date Date for which the data should be read.
-    * @param[in] node_ids Ids of the nodes.
-    * @param[in] scaling_factor_inf Factor of confirmed cases to account for undetected cases in each county.
-    * @param[in] scaling_factor_icu Factor of ICU cases to account for underreporting.
-    * @param[in] pydata_dir Directory that contains the data files.
-    * @param[in] num_days Number of days to be simulated; required to load data for vaccinations during the simulation.
-    * @param[in] export_time_series If true, reads data for each day of simulation and writes it in the same directory as the input files.
-    */
-template <class Model>
-IOResult<void> read_input_data(std::vector<Model>& model, Date date, const std::vector<int>& node_ids,
-                               const std::vector<double>& scaling_factor_inf, double scaling_factor_icu,
-                               const std::string& pydata_dir, int num_days, bool export_time_series = false)
-{
-
-    BOOST_OUTCOME_TRY(
-        details::set_vaccination_data(model, path_join(pydata_dir, "vaccination_data.json"), date, node_ids, num_days));
-
-    // TODO: Reuse more code, e.g., set_divi_data (in secir) and a set_divi_data (here) only need a different ModelType.
-    // TODO: add option to set ICU data from confirmed cases if DIVI or other data is not available.
-    BOOST_OUTCOME_TRY(details::set_divi_data(model, path_join(pydata_dir, "critical_cases.json"), node_ids, date,
-                                             scaling_factor_icu));
-
-    BOOST_OUTCOME_TRY(details::set_confirmed_cases_data(model, path_join(pydata_dir, "confirmed_cases.json"), node_ids,
-                                                        date, scaling_factor_inf));
-    BOOST_OUTCOME_TRY(details::set_population_data(model, path_join(pydata_dir, "population_data.json"),
-                                                   path_join(pydata_dir, "confirmed_cases.json"), node_ids, date));
-
-    if (export_time_series) {
-        // Use only if extrapolated real data is needed for comparison. EXPENSIVE !
-        // Run time equals run time of the previous functions times the num_days !
-        // (This only represents the vectorization of the previous function over all simulation days...)
-        log_warning("Exporting time series of extrapolated real data. This may take some minutes. "
-                    "For simulation runs over the same time period, deactivate it.");
-        BOOST_OUTCOME_TRY(export_input_data_county_timeseries(
-            model, pydata_dir, node_ids, date, scaling_factor_inf, scaling_factor_icu, num_days,
-            path_join(pydata_dir, "divi_data.json"), path_join(pydata_dir, "confirmed_cases.json"),
-            path_join(pydata_dir, "population_data.json"), path_join(pydata_dir, "vaccination_data.json")));
-    }
+ * @brief Uses the initialisation method, which uses the reported data to set the initial conditions for the model for a given day. 
+ * 
+ * The initialisation is applied for a predefined number of days and finally saved in a timeseries for each region. In the end,
+ * we save the files "Results_rki.h5" and "Results_rki_sum.h5" in the results_dir.
+ * Results_rki.h5 contains a time series for each region and Results_rki_sum.h5 contains the sum of all regions.
+ * 
+ * @param[in] model VectorRange of Node%s each containing a Model in which the data is set.
+ * @param[in] results_dir Path to result files.
+ * @param[in] date Date for which the data should be read.
+ * @param[in] scaling_factor_inf Vector of scaling factors for confirmed cases.
+ * @param[in] scaling_factor_icu Scaling factor for ICU cases.
+ * @param[in] num_days Number of days to be simulated/initialized.
+ * @param[in] epidata_filenames Object containing the input data file paths.
+ * 
+ * @return An IOResult indicating success or failure.
+ */
+IOResult<void> export_input_data_timeseries(const mio::VectorRange<Node<Model<ScalarType>>> model,
+                                            const std::string& results_dir, Date date,
+                                            const std::vector<ScalarType>& scaling_factor_inf,
+                                            const ScalarType scaling_factor_icu, const int num_days,
+                                            const mio::regions::de::EpidataFilenames& epidata_filenames);
+#else
+IOResult<void> export_input_data_county_timeseries(const mio::VectorRange<Node<Model<ScalarType>>>, const std::string&,
+                                                   Date, const std::vector<int>&, const std::vector<ScalarType>&,
+                                                   const ScalarType, const int,
+                                                   const mio::regions::de::EpidataFilenames&);
 
-    return success();
-}
+#endif //MEMILIO_HAS_HDF5
 
 /**
  * @brief Converts input data from one range of models to another with different type.
  * 
  * @tparam FP Floating point type.
- * @param[in] model_from VectorRange of Node%s each containing a Model with the input data.
- * @param[in,out] model_to VectorRange of Node%s each containing a Model to be initialized with data.
+ * @param[in,out] model VectorRange of Node%s each containing a Model to be initialized with data.
+ * @param[in] date Date for which the data should be read.
+ * @param[in] scaling_factor_inf Vector of scaling factors for confirmed cases.
+ * @param[in] scaling_factor_icu Scaling factor for ICU cases.
+ * @param[in] num_days Number of days to simulate.
+ * @param[in] epidata_filenames Object containing the input data file paths.
+ * @param[in] set_death If true, set the number of deaths.
  * 
  * @return An IOResult indicating success or failure.
  */
 template <class FP>
-IOResult<void> convert_model_data_type(mio::VectorRange<Node<Model<ScalarType>>> model_from,
-                                       mio::VectorRange<Node<Model<FP>>> model_to)
+IOResult<void> convert_model_data_type(const mio::VectorRange<Node<Model<ScalarType>>>& model_from,
+                                       const mio::VectorRange<Node<Model<FP>>>& model_to, Date date, int num_days,
+                                       const mio::regions::de::EpidataFilenames& epidata_filenames)
 {
     assert(model_from.size() == model_to.size());
     assert((size_t)model_from[0].property.parameters.get_num_groups() ==
            (size_t)model_to[0].property.parameters.get_num_groups());
-    // Todo: Add conversion of ParameterSet and then re-use code from all model parameters io
-    //       OR call set_vacination_data with FP to set correct parameters
+    // Todo: add conversion of ParameterSet and then re-use code from all model parameters io
+    // For now call set_vacination_data with FP to set correct parameters
+    BOOST_OUTCOME_TRY(
+        details::set_vaccination_data<FP>(model_to, epidata_filenames.vaccination_data_path, date, num_days));
 
     for (size_t region_idx = 0; region_idx < model_from.size(); ++region_idx) {
         // convert populations to mio::UncertainValue<FP>
         // needs 2 converts as mio::UncertainValue<ScalarType> -> mio::UncertainValue<FP> does not work
         model_to[region_idx].property.populations = model_from[region_idx].property.populations.template convert<FP>();
     }
-    return mio::success();
+    return success();
 }
 
 } // namespace osecirvvs
+
 } // namespace mio
 
 #endif // MEMILIO_HAS_JSONCPP
diff --git a/cpp/tests/data/District/pydata/confirmed_cases.json b/cpp/tests/data/District/pydata/cases_all_county_age_ma7.json
similarity index 100%
rename from cpp/tests/data/District/pydata/confirmed_cases.json
rename to cpp/tests/data/District/pydata/cases_all_county_age_ma7.json
diff --git a/cpp/tests/data/District/pydata/population_data.json b/cpp/tests/data/District/pydata/county_current_population.json
similarity index 100%
rename from cpp/tests/data/District/pydata/population_data.json
rename to cpp/tests/data/District/pydata/county_current_population.json
diff --git a/cpp/tests/data/District/pydata/critical_cases.json b/cpp/tests/data/District/pydata/county_divi_ma7.json
similarity index 100%
rename from cpp/tests/data/District/pydata/critical_cases.json
rename to cpp/tests/data/District/pydata/county_divi_ma7.json
diff --git a/cpp/tests/data/District/pydata/vaccination_data.json b/cpp/tests/data/District/pydata/vacc_county_ageinf_ma7.json
similarity index 100%
rename from cpp/tests/data/District/pydata/vaccination_data.json
rename to cpp/tests/data/District/pydata/vacc_county_ageinf_ma7.json
diff --git a/cpp/tests/data/Germany/pydata/confirmed_cases.json b/cpp/tests/data/Germany/pydata/confirmed_cases.json
deleted file mode 100644
index 603ac57cc4..0000000000
--- a/cpp/tests/data/Germany/pydata/confirmed_cases.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-10-01","Confirmed":6.1428571429,"Deaths":0.0,"Recovered":6.1428571429},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-10-02","Confirmed":6.4285714286,"Deaths":0.0,"Recovered":6.4285714286},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-10-03","Confirmed":6.7142857143,"Deaths":0.0,"Recovered":6.7142857143},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-10-04","Confirmed":6.8571428571,"Deaths":0.0,"Recovered":6.8571428571},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-10-05","Confirmed":7.0,"Deaths":0.0,"Recovered":7.0},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-10-06","Confirmed":7.0,"Deaths":0.0,"Recovered":7.0},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-10-07","Confirmed":7.0,"Deaths":0.0,"Recovered":7.0},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-10-08","Confirmed":7.0,"Deaths":0.0,"Recovered":7.0},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-10-09","Confirmed":7.0,"Deaths":0.0,"Recovered":7.0},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-10-10","Confirmed":7.0,"Deaths":0.0,"Recovered":7.0},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-10-11","Confirmed":7.0,"Deaths":0.0,"Recovered":7.0},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-10-12","Confirmed":7.1428571429,"Deaths":0.0,"Recovered":7.1428571429},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-10-13","Confirmed":7.2857142857,"Deaths":0.0,"Recovered":7.2857142857},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-10-14","Confirmed":7.4285714286,"Deaths":0.0,"Recovered":7.4285714286},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-10-15","Confirmed":7.5714285714,"Deaths":0.0,"Recovered":7.5714285714},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-10-16","Confirmed":7.7142857143,"Deaths":0.0,"Recovered":7.7142857143},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-10-17","Confirmed":7.8571428571,"Deaths":0.0,"Recovered":7.8571428571},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-10-18","Confirmed":8.0,"Deaths":0.0,"Recovered":8.0},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-10-19","Confirmed":8.2857142857,"Deaths":0.0,"Recovered":8.2857142857},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-10-20","Confirmed":8.5714285714,"Deaths":0.0,"Recovered":8.5714285714},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-10-21","Confirmed":9.1428571429,"Deaths":0.0,"Recovered":9.1428571429},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-10-22","Confirmed":9.7142857143,"Deaths":0.0,"Recovered":9.7142857143},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-10-23","Confirmed":10.4285714286,"Deaths":0.0,"Recovered":10.4285714286},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-10-24","Confirmed":11.1428571429,"Deaths":0.0,"Recovered":11.1428571429},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-10-25","Confirmed":11.8571428571,"Deaths":0.0,"Recovered":11.8571428571},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-10-26","Confirmed":12.2857142857,"Deaths":0.0,"Recovered":12.2857142857},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-10-27","Confirmed":12.7142857143,"Deaths":0.0,"Recovered":12.7142857143},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-10-28","Confirmed":12.8571428571,"Deaths":0.0,"Recovered":12.8571428571},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-10-29","Confirmed":13.0,"Deaths":0.0,"Recovered":13.0},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-10-30","Confirmed":13.0,"Deaths":0.0,"Recovered":13.0},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-10-31","Confirmed":13.0,"Deaths":0.0,"Recovered":13.0},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-11-01","Confirmed":13.0,"Deaths":0.0,"Recovered":13.0},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-11-02","Confirmed":13.1428571429,"Deaths":0.0,"Recovered":13.1428571429},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-11-03","Confirmed":13.2857142857,"Deaths":0.0,"Recovered":13.2857142857},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-11-04","Confirmed":13.4285714286,"Deaths":0.0,"Recovered":13.4285714286},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-11-05","Confirmed":13.5714285714,"Deaths":0.0,"Recovered":13.5714285714},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-11-06","Confirmed":13.7142857143,"Deaths":0.0,"Recovered":13.7142857143},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-11-07","Confirmed":13.8571428571,"Deaths":0.0,"Recovered":13.8571428571},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-11-08","Confirmed":14.0,"Deaths":0.0,"Recovered":14.0},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-11-09","Confirmed":14.0,"Deaths":0.0,"Recovered":14.0},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-11-10","Confirmed":14.1428571429,"Deaths":0.0,"Recovered":14.1428571429},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-11-11","Confirmed":14.2857142857,"Deaths":0.0,"Recovered":14.2857142857},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-11-12","Confirmed":14.4285714286,"Deaths":0.0,"Recovered":14.4285714286},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-11-13","Confirmed":14.7142857143,"Deaths":0.0,"Recovered":14.7142857143},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-11-14","Confirmed":15.0,"Deaths":0.0,"Recovered":15.0},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-11-15","Confirmed":15.2857142857,"Deaths":0.0,"Recovered":15.2857142857},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-11-16","Confirmed":15.5714285714,"Deaths":0.0,"Recovered":15.5714285714},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-11-17","Confirmed":15.7142857143,"Deaths":0.0,"Recovered":15.7142857143},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-11-18","Confirmed":16.0,"Deaths":0.0,"Recovered":16.0},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-11-19","Confirmed":16.4285714286,"Deaths":0.0,"Recovered":16.4285714286},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-11-20","Confirmed":17.0,"Deaths":0.0,"Recovered":17.0},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-11-21","Confirmed":17.7142857143,"Deaths":0.0,"Recovered":17.7142857143},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-11-22","Confirmed":18.4285714286,"Deaths":0.0,"Recovered":18.4285714286},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-11-23","Confirmed":19.5714285714,"Deaths":0.0,"Recovered":19.5714285714},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-11-24","Confirmed":21.0,"Deaths":0.0,"Recovered":21.0},{"ID_County":1002,"County":"SK Kiel","Age_RKI":"A00-A04","Date":"2020-11-25","Confirmed":22.4285714286,"Deaths":0.0,"Recovered":22.4285714286},{"ID_County":1002,"County":"SK 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diff --git a/cpp/tests/data/Germany/pydata/critical_cases.json b/cpp/tests/data/Germany/pydata/critical_cases.json
deleted file mode 100644
index 9c361855b1..0000000000
--- a/cpp/tests/data/Germany/pydata/critical_cases.json
+++ /dev/null
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deleted file mode 100644
index b5a0af9b52..0000000000
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diff --git a/cpp/tests/data/Germany/pydata/vaccination_data.json b/cpp/tests/data/Germany/pydata/vaccination_data.json
deleted file mode 100644
index e8c47d14fb..0000000000
--- a/cpp/tests/data/Germany/pydata/vaccination_data.json
+++ /dev/null
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-    "Vacc_completed": 2174.4162160121,
-    "Vacc_refreshed": 254.1541702573,
-    "Vacc_refreshed_2": 127.07708512865
-  },
-  {
-    "Date": "2020-11-16",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15337.5758896556,
-    "Vacc_completed": 2180.7560110181,
-    "Vacc_refreshed": 255.4491825287,
-    "Vacc_refreshed_2": 127.72459126435
-  },
-  {
-    "Date": "2020-11-17",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15346.8261947795,
-    "Vacc_completed": 2184.562660479,
-    "Vacc_refreshed": 256.0794667401,
-    "Vacc_refreshed_2": 128.03973337005
-  },
-  {
-    "Date": "2020-11-18",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15347.5063487999,
-    "Vacc_completed": 2192.7053918922,
-    "Vacc_refreshed": 264.8657432896,
-    "Vacc_refreshed_2": 132.4328716448
-  },
-  {
-    "Date": "2020-11-19",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15355.2915215882,
-    "Vacc_completed": 2197.3639423288,
-    "Vacc_refreshed": 266.2003474583,
-    "Vacc_refreshed_2": 133.10017372915
-  },
-  {
-    "Date": "2020-11-20",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15357.0291297717,
-    "Vacc_completed": 2203.5668703416,
-    "Vacc_refreshed": 266.2591235692,
-    "Vacc_refreshed_2": 133.1295617846
-  },
-  {
-    "Date": "2020-11-21",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15363.7351300479,
-    "Vacc_completed": 2212.51470716,
-    "Vacc_refreshed": 268.1554009506,
-    "Vacc_refreshed_2": 134.0777004753
-  },
-  {
-    "Date": "2020-11-22",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15366.2054877706,
-    "Vacc_completed": 2220.7805776981,
-    "Vacc_refreshed": 270.2429125628,
-    "Vacc_refreshed_2": 135.1214562814
-  },
-  {
-    "Date": "2020-11-23",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15374.7828886007,
-    "Vacc_completed": 2223.4779151007,
-    "Vacc_refreshed": 271.3258668517,
-    "Vacc_refreshed_2": 135.66293342585
-  },
-  {
-    "Date": "2020-11-24",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15375.8644766411,
-    "Vacc_completed": 2223.902202818,
-    "Vacc_refreshed": 277.3418287874,
-    "Vacc_refreshed_2": 138.6709143937
-  },
-  {
-    "Date": "2020-11-25",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15384.0103684975,
-    "Vacc_completed": 2233.2600148125,
-    "Vacc_refreshed": 286.2795142721,
-    "Vacc_refreshed_2": 143.13975713605
-  },
-  {
-    "Date": "2020-11-26",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15388.2433647335,
-    "Vacc_completed": 2242.2405085188,
-    "Vacc_refreshed": 295.9058348626,
-    "Vacc_refreshed_2": 147.9529174313
-  },
-  {
-    "Date": "2020-11-27",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15395.874552707,
-    "Vacc_completed": 2242.5126967683,
-    "Vacc_refreshed": 297.0217883024,
-    "Vacc_refreshed_2": 148.5108941512
-  },
-  {
-    "Date": "2020-11-28",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15399.399317417,
-    "Vacc_completed": 2250.19318626,
-    "Vacc_refreshed": 297.4497074595,
-    "Vacc_refreshed_2": 148.72485372975
-  },
-  {
-    "Date": "2020-11-29",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15407.4994879004,
-    "Vacc_completed": 2258.8146667712,
-    "Vacc_refreshed": 302.8025504676,
-    "Vacc_refreshed_2": 151.4012752338
-  },
-  {
-    "Date": "2020-11-30",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15410.1346998553,
-    "Vacc_completed": 2259.5361618465,
-    "Vacc_refreshed": 305.0699464575,
-    "Vacc_refreshed_2": 152.53497322875
-  },
-  {
-    "Date": "2020-12-01",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15415.5662938607,
-    "Vacc_completed": 2266.2419298528,
-    "Vacc_refreshed": 306.5583985118,
-    "Vacc_refreshed_2": 153.2791992559
-  },
-  {
-    "Date": "2020-12-02",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15424.362408066,
-    "Vacc_completed": 2270.6973906417,
-    "Vacc_refreshed": 307.7300944183,
-    "Vacc_refreshed_2": 153.86504720915
-  },
-  {
-    "Date": "2020-12-03",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15427.4641661532,
-    "Vacc_completed": 2273.4475287465,
-    "Vacc_refreshed": 315.9257071161,
-    "Vacc_refreshed_2": 157.96285355805
-  },
-  {
-    "Date": "2020-12-04",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15430.3003374401,
-    "Vacc_completed": 2280.4953549554,
-    "Vacc_refreshed": 320.4057109928,
-    "Vacc_refreshed_2": 160.2028554964
-  },
-  {
-    "Date": "2020-12-05",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15434.7161891817,
-    "Vacc_completed": 2283.6641990131,
-    "Vacc_refreshed": 329.6425079267,
-    "Vacc_refreshed_2": 164.82125396335
-  },
-  {
-    "Date": "2020-12-06",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15439.6389263804,
-    "Vacc_completed": 2286.269595866,
-    "Vacc_refreshed": 336.6032551175,
-    "Vacc_refreshed_2": 168.30162755875
-  },
-  {
-    "Date": "2020-12-07",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15445.607595077,
-    "Vacc_completed": 2292.1795115763,
-    "Vacc_refreshed": 339.4752869467,
-    "Vacc_refreshed_2": 169.73764347335
-  },
-  {
-    "Date": "2020-12-08",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15445.7317234732,
-    "Vacc_completed": 2299.0428376394,
-    "Vacc_refreshed": 342.2213588852,
-    "Vacc_refreshed_2": 171.1106794426
-  },
-  {
-    "Date": "2020-12-09",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15448.8137925508,
-    "Vacc_completed": 2303.3104194582,
-    "Vacc_refreshed": 346.3780017598,
-    "Vacc_refreshed_2": 173.1890008799
-  },
-  {
-    "Date": "2020-12-10",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15449.4812178935,
-    "Vacc_completed": 2311.9261799324,
-    "Vacc_refreshed": 351.1765190663,
-    "Vacc_refreshed_2": 175.58825953315
-  },
-  {
-    "Date": "2020-12-11",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15457.9089483618,
-    "Vacc_completed": 2315.5711542884,
-    "Vacc_refreshed": 352.532745084,
-    "Vacc_refreshed_2": 176.266372542
-  },
-  {
-    "Date": "2020-12-12",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15457.9943266817,
-    "Vacc_completed": 2322.1716179078,
-    "Vacc_refreshed": 355.0774800773,
-    "Vacc_refreshed_2": 177.53874003865
-  },
-  {
-    "Date": "2020-12-13",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15459.7074068986,
-    "Vacc_completed": 2323.3027059178,
-    "Vacc_refreshed": 364.5645466744,
-    "Vacc_refreshed_2": 182.2822733372
-  },
-  {
-    "Date": "2020-12-14",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15465.686171573,
-    "Vacc_completed": 2325.8040208828,
-    "Vacc_refreshed": 365.9637341301,
-    "Vacc_refreshed_2": 182.98186706505
-  },
-  {
-    "Date": "2020-12-15",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15466.2025927257,
-    "Vacc_completed": 2332.7537065257,
-    "Vacc_refreshed": 366.2841969775,
-    "Vacc_refreshed_2": 183.14209848875
-  },
-  {
-    "Date": "2020-12-16",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15471.0643499093,
-    "Vacc_completed": 2333.7718947218,
-    "Vacc_refreshed": 370.7174701468,
-    "Vacc_refreshed_2": 185.3587350734
-  },
-  {
-    "Date": "2020-12-17",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15480.1820736709,
-    "Vacc_completed": 2336.425519339,
-    "Vacc_refreshed": 378.1719392306,
-    "Vacc_refreshed_2": 189.0859696153
-  },
-  {
-    "Date": "2020-12-18",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15489.2619602904,
-    "Vacc_completed": 2342.4314320637,
-    "Vacc_refreshed": 382.1070985379,
-    "Vacc_refreshed_2": 191.05354926895
-  },
-  {
-    "Date": "2020-12-19",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15497.197059921,
-    "Vacc_completed": 2351.5508059781,
-    "Vacc_refreshed": 390.0131449372,
-    "Vacc_refreshed_2": 195.0065724686
-  },
-  {
-    "Date": "2020-12-20",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15506.8997820546,
-    "Vacc_completed": 2354.7951207515,
-    "Vacc_refreshed": 397.4755552416,
-    "Vacc_refreshed_2": 198.7377776208
-  },
-  {
-    "Date": "2020-12-21",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15514.6356932026,
-    "Vacc_completed": 2359.4543858263,
-    "Vacc_refreshed": 405.5265192328,
-    "Vacc_refreshed_2": 202.7632596164
-  },
-  {
-    "Date": "2020-12-22",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15517.1065215082,
-    "Vacc_completed": 2362.3705119741,
-    "Vacc_refreshed": 406.2503084488,
-    "Vacc_refreshed_2": 203.1251542244
-  },
-  {
-    "Date": "2020-12-23",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15526.1890623234,
-    "Vacc_completed": 2369.2123027947,
-    "Vacc_refreshed": 407.9863557734,
-    "Vacc_refreshed_2": 203.9931778867
-  },
-  {
-    "Date": "2020-12-24",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15529.1257916241,
-    "Vacc_completed": 2371.0230854052,
-    "Vacc_refreshed": 415.4330021443,
-    "Vacc_refreshed_2": 207.71650107215
-  },
-  {
-    "Date": "2020-12-25",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15534.1422800755,
-    "Vacc_completed": 2376.4314141852,
-    "Vacc_refreshed": 417.8710629027,
-    "Vacc_refreshed_2": 208.93553145135
-  },
-  {
-    "Date": "2020-12-26",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15536.9779956012,
-    "Vacc_completed": 2380.9412020655,
-    "Vacc_refreshed": 418.6065556413,
-    "Vacc_refreshed_2": 209.30327782065
-  },
-  {
-    "Date": "2020-12-27",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15542.623117778,
-    "Vacc_completed": 2383.2295524317,
-    "Vacc_refreshed": 425.5318613018,
-    "Vacc_refreshed_2": 212.7659306509
-  },
-  {
-    "Date": "2020-12-28",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15545.7349127212,
-    "Vacc_completed": 2388.7438112703,
-    "Vacc_refreshed": 435.2708995295,
-    "Vacc_refreshed_2": 217.63544976475
-  },
-  {
-    "Date": "2020-12-29",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15552.7663039717,
-    "Vacc_completed": 2398.7179519564,
-    "Vacc_refreshed": 436.7806978293,
-    "Vacc_refreshed_2": 218.39034891465
-  },
-  {
-    "Date": "2020-12-30",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15553.9038494269,
-    "Vacc_completed": 2401.9470135031,
-    "Vacc_refreshed": 445.294989416,
-    "Vacc_refreshed_2": 222.647494708
-  },
-  {
-    "Date": "2020-12-31",
-    "ID_County": 1002,
-    "Age_RKI": "80-99",
-    "Vacc_partially": 15563.5219522465,
-    "Vacc_completed": 2408.3277109489,
-    "Vacc_refreshed": 449.1868908695,
-    "Vacc_refreshed_2": 224.59344543475
-  }
-]
\ No newline at end of file
diff --git a/cpp/tests/data/export_time_series_initialization_osecirvvs.h5 b/cpp/tests/data/export_time_series_initialization_osecirvvs.h5
index 9d4ac4d8ed..ec4924dcda 100644
Binary files a/cpp/tests/data/export_time_series_initialization_osecirvvs.h5 and b/cpp/tests/data/export_time_series_initialization_osecirvvs.h5 differ
diff --git a/cpp/tests/test_epi_data_io.cpp b/cpp/tests/test_epi_data_io.cpp
index f29467e775..503603fe44 100644
--- a/cpp/tests/test_epi_data_io.cpp
+++ b/cpp/tests/test_epi_data_io.cpp
@@ -411,42 +411,6 @@ TEST(TestEpiDataIO, read_vaccination_data)
     ASSERT_EQ(vacc_data[1].num_vaccinations_refreshed_additional, 3.0);
 }
 
-TEST(TestEpiData, set_vaccination_data)
-{
-    auto num_age_groups = 1;
-    auto num_days       = 9;
-
-    std::vector<int> county_ids = {1001};
-    mio::osecirts::Model<double> model(num_age_groups);
-    model.parameters.set<mio::osecirts::DaysUntilEffectivePartialVaccination<double>>(1);
-    model.parameters.set<mio::osecirts::DaysUntilEffectiveImprovedVaccination<double>>(2);
-    model.parameters.set<mio::osecirts::DaysUntilEffectiveBoosterImmunity<double>>(1);
-    std::vector<mio::osecirts::Model<double>> model_vector{model};
-
-    auto f = mio::osecirts::details::set_vaccination_data(model_vector,
-                                                          mio::path_join(TEST_DATA_DIR, "vaccination_test.json"),
-                                                          mio::Date(2022, 4, 15), county_ids, num_days);
-
-    auto expected_values_PI =
-        (Eigen::ArrayXd(num_age_groups * (num_days + 1)) << 7, 10, 20, 15, 10, 5, 2, 15, 8, 0).finished();
-
-    auto expected_values_II =
-        (Eigen::ArrayXd(num_age_groups * (num_days + 1)) << 2, 4, 5, 5, 7, 8, 9, 9, 10, 12).finished();
-
-    auto expected_values_B =
-        (Eigen::ArrayXd(num_age_groups * (num_days + 1)) << 5, 7, 9, 11, 13, 9, 7, 5, 5, 0).finished();
-
-    ASSERT_THAT(
-        print_wrap(model_vector[0].parameters.template get<mio::osecirts::DailyPartialVaccinations<double>>().array()),
-        MatrixNear(print_wrap(expected_values_PI), 1e-8, 1e-8));
-    ASSERT_THAT(
-        print_wrap(model_vector[0].parameters.template get<mio::osecirts::DailyFullVaccinations<double>>().array()),
-        MatrixNear(print_wrap(expected_values_II), 1e-8, 1e-8));
-    ASSERT_THAT(
-        print_wrap(model_vector[0].parameters.template get<mio::osecirts::DailyBoosterVaccinations<double>>().array()),
-        MatrixNear(print_wrap(expected_values_B), 1e-8, 1e-8));
-}
-
 TEST(TestEpiData, vaccination_data)
 {
     auto js                   = Json::Value(Json::arrayValue);
diff --git a/cpp/tests/test_graph.cpp b/cpp/tests/test_graph.cpp
index bfffc6c8c2..c399fc1875 100644
--- a/cpp/tests/test_graph.cpp
+++ b/cpp/tests/test_graph.cpp
@@ -148,7 +148,7 @@ TEST(TestGraph, graph_without_edges)
     std::vector<int> ids          = {1, 2};
 
     mio::Graph<MockModel, MockMobility> g(ids, models);
-    
+
     EXPECT_EQ(g.edges().size(), 0);
     EXPECT_EQ(g.nodes().size(), 2);
     EXPECT_EQ(g.nodes()[0].id, 1);
@@ -160,60 +160,6 @@ TEST(TestGraph, graph_without_edges)
     EXPECT_FALSE(model_type_false);
 }
 
-TEST(TestGraph, set_nodes_secir)
-{
-
-    mio::osecir::Parameters<double> params(1);
-    mio::Graph<mio::osecir::Model<double>, mio::MobilityParameters<double>> params_graph;
-    const auto& read_function_nodes = mock_read_function<mio::osecir::Model<double>>;
-    const auto& node_id_function    = mock_node_function;
-
-    const fs::path& dir = " ";
-
-    auto result =
-        mio::set_nodes<double, mio::osecir::TestAndTraceCapacity<double>, mio::osecir::ContactPatterns<double>,
-                       mio::osecir::Model<double>, mio::MobilityParameters<double>, mio::osecir::Parameters<double>,
-                       decltype(read_function_nodes), decltype(node_id_function)>(
-            params, mio::Date(2020, 5, 10), mio::Date(2020, 5, 11), dir, " ", false, params_graph, read_function_nodes,
-            node_id_function, std::vector<double>(size_t(1), 1.0), 1.0, 0.01);
-
-    EXPECT_EQ(params_graph.nodes().size(), 2);
-    EXPECT_EQ(params_graph.nodes()[0].id, 1001);
-    EXPECT_EQ(params_graph.nodes()[1].id, 1002);
-    auto model_type_true1 = std::is_same<decltype(params_graph.nodes()[0].property), mio::osecir::Model<double>>::value;
-    EXPECT_TRUE(model_type_true1);
-    auto model_type_true2 = std::is_same<decltype(params_graph.nodes()[1].property), mio::osecir::Model<double>>::value;
-    EXPECT_TRUE(model_type_true2);
-}
-
-TEST(TestGraph, set_nodes_secirvvs)
-{
-
-    mio::osecirvvs::Parameters<double> params(1);
-    mio::Graph<mio::osecirvvs::Model<double>, mio::MobilityParameters<double>> params_graph;
-    const auto& read_function_nodes = mock_read_function<mio::osecirvvs::Model<double>>;
-    const auto& node_id_function    = mock_node_function;
-
-    const fs::path& dir = " ";
-
-    auto result =
-        mio::set_nodes<double, mio::osecirvvs::TestAndTraceCapacity<double>, mio::osecirvvs::ContactPatterns<double>,
-                       mio::osecirvvs::Model<double>, mio::MobilityParameters<double>,
-                       mio::osecirvvs::Parameters<double>, decltype(read_function_nodes), decltype(node_id_function)>(
-            params, mio::Date(2020, 5, 10), mio::Date(2020, 5, 11), dir, " ", false, params_graph, read_function_nodes,
-            node_id_function, std::vector<double>(size_t(1), 1.0), 1.0, 0.01);
-
-    EXPECT_EQ(params_graph.nodes().size(), 2);
-    EXPECT_EQ(params_graph.nodes()[0].id, 1001);
-    EXPECT_EQ(params_graph.nodes()[1].id, 1002);
-    auto model_type_true1 =
-        std::is_same<decltype(params_graph.nodes()[0].property), mio::osecirvvs::Model<double>>::value;
-    EXPECT_TRUE(model_type_true1);
-    auto model_type_true2 =
-        std::is_same<decltype(params_graph.nodes()[1].property), mio::osecirvvs::Model<double>>::value;
-    EXPECT_TRUE(model_type_true2);
-}
-
 TEST(TestGraph, set_edges)
 {
     mio::osecir::Model<double> model(6);
diff --git a/cpp/tests/test_odesecir.cpp b/cpp/tests/test_odesecir.cpp
index 24533cc68d..4add67c08f 100755
--- a/cpp/tests/test_odesecir.cpp
+++ b/cpp/tests/test_odesecir.cpp
@@ -32,9 +32,12 @@
 #include "memilio/geography/regions.h"
 
 #include <numbers>
+#include <vector>
 
 #include <gtest/gtest.h>
 
+using ModelT = mio::osecir::Model<double>;
+
 TEST(TestOdeSecir, compareWithPreviousRun)
 {
     /*
@@ -50,7 +53,7 @@ TEST(TestOdeSecir, compareWithPreviousRun)
     double nb_total_t0 = 10000, nb_exp_t0 = 100, nb_inf_t0 = 50, nb_car_t0 = 50, nb_hosp_t0 = 20, nb_icu_t0 = 10,
            nb_rec_t0 = 10, nb_dead_t0 = 0;
 
-    mio::osecir::Model<double> model(1);
+    ModelT model(1);
 
     model.parameters.set<mio::osecir::StartDay<double>>(60);
     model.parameters.set<mio::osecir::Seasonality<double>>(0.2);
@@ -95,8 +98,7 @@ TEST(TestOdeSecir, compareWithPreviousRun)
     integrator->set_dt_max(1.0);
     integrator->set_rel_tolerance(1e-3);
     integrator->set_abs_tolerance(1e-1);
-    mio::TimeSeries<double> secihurd =
-        mio::simulate<double, mio::osecir::Model<double>>(t0, tmax, dt, model, std::move(integrator));
+    mio::TimeSeries<double> secihurd = mio::simulate<double, ModelT>(t0, tmax, dt, model, std::move(integrator));
 
     auto compare = load_test_data_csv<double>("secihurd-compare.csv");
 
@@ -116,7 +118,7 @@ TEST(TestOdeSecir, simulateDefault)
     double tmax = 1;
     double dt   = 0.1;
 
-    mio::osecir::Model<double> model(1);
+    ModelT model(1);
     mio::TimeSeries<double> result = mio::simulate<double>(t0, tmax, dt, model);
 
     EXPECT_NEAR(result.get_last_time(), tmax, 1e-10);
@@ -132,7 +134,7 @@ TEST(TestOdeSecir, checkPopulationConservation)
 
     double nb_total_t0 = 10000;
 
-    mio::osecir::Model<double> model(1);
+    ModelT model(1);
 
     model.parameters.get<mio::osecir::TestAndTraceCapacity<double>>() = 35;
 
@@ -190,7 +192,7 @@ TEST(TestOdeSecir, testParamConstructors)
     double icu_cap   = 4444;
     double start_day = 30, seasonality = 0.3;
 
-    mio::osecir::Model<double> model(1);
+    ModelT model(1);
 
     model.parameters.set<mio::osecir::ICUCapacity<double>>(icu_cap);
 
@@ -226,7 +228,7 @@ TEST(TestOdeSecir, testParamConstructors)
     contact_matrix[0] = mio::ContactMatrix<double>(Eigen::MatrixXd::Constant(1, 1, cont_freq));
     contact_matrix[0].add_damping(0.7, mio::SimulationTime<double>(30.));
 
-    mio::osecir::Model<double> model2{model}; // copy constructor
+    ModelT model2{model}; // copy constructor
 
     EXPECT_EQ(model.parameters.get<mio::osecir::ICUCapacity<double>>(),
               model2.parameters.get<mio::osecir::ICUCapacity<double>>());
@@ -283,7 +285,7 @@ TEST(TestOdeSecir, testParamConstructors)
     EXPECT_EQ(model.parameters.get<mio::osecir::ContactPatterns<double>>().get_cont_freq_mat(),
               model2.parameters.get<mio::osecir::ContactPatterns<double>>().get_cont_freq_mat());
 
-    mio::osecir::Model<double> model3 = std::move(model2); // move constructor
+    ModelT model3 = std::move(model2); // move constructor
 
     EXPECT_EQ(model.parameters.get<mio::osecir::ICUCapacity<double>>(),
               model3.parameters.get<mio::osecir::ICUCapacity<double>>());
@@ -339,7 +341,7 @@ TEST(TestOdeSecir, testParamConstructors)
     EXPECT_EQ(model.parameters.get<mio::osecir::ContactPatterns<double>>().get_cont_freq_mat(),
               model3.parameters.get<mio::osecir::ContactPatterns<double>>().get_cont_freq_mat());
 
-    mio::osecir::Model<double> model4 = model3; // copy assignment constructor
+    ModelT model4 = model3; // copy assignment constructor
 
     EXPECT_EQ(model4.parameters.get<mio::osecir::ICUCapacity<double>>(),
               model3.parameters.get<mio::osecir::ICUCapacity<double>>());
@@ -395,7 +397,7 @@ TEST(TestOdeSecir, testParamConstructors)
     EXPECT_EQ(model4.parameters.get<mio::osecir::ContactPatterns<double>>().get_cont_freq_mat(),
               model3.parameters.get<mio::osecir::ContactPatterns<double>>().get_cont_freq_mat());
 
-    mio::osecir::Model<double> model5 = std::move(model4); // move assignment constructor
+    ModelT model5 = std::move(model4); // move assignment constructor
 
     EXPECT_EQ(model5.parameters.get<mio::osecir::ICUCapacity<double>>(),
               model3.parameters.get<mio::osecir::ICUCapacity<double>>());
@@ -462,7 +464,7 @@ TEST(TestOdeSecir, testSettersAndGetters)
         vec.push_back(val);
     }
 
-    mio::osecir::Model<double> model(1);
+    ModelT model(1);
     EXPECT_EQ(model.parameters.get<mio::osecir::TimeExposed<double>>()[(mio::AgeGroup)0].get_distribution().get(),
               nullptr);
 
@@ -585,7 +587,7 @@ TEST(TestOdeSecir, testSettersAndGetters)
 TEST(TestOdeSecir, population_zero_no_nan)
 {
     // initialize simple model with total population 0
-    mio::osecir::Model<double> model(1);
+    ModelT model(1);
     model.populations.set_total(0.0);
 
     // call the get_flows function
@@ -616,7 +618,7 @@ TEST(TestOdeSecir, testDamping)
     double nb_total_t0 = 1000, nb_inf_t0 = 10;
 
     // default model run to be compared against
-    mio::osecir::Model<double> model_a(1);
+    ModelT model_a(1);
     model_a.populations[{mio::AgeGroup(0), mio::osecir::InfectionState::InfectedSymptoms}] = nb_inf_t0;
     model_a.populations.set_difference_from_total({mio::AgeGroup(0), mio::osecir::InfectionState::Susceptible},
                                                   nb_total_t0);
@@ -630,7 +632,7 @@ TEST(TestOdeSecir, testDamping)
                                                     std::make_unique<mio::EulerIntegratorCore<ScalarType>>());
 
     // reduced transmission
-    mio::osecir::Model<double> model_b{model_a};
+    ModelT model_b{model_a};
     model_b.populations.set_total(nb_total_t0);
     model_b.populations[{mio::AgeGroup(0), mio::osecir::InfectionState::InfectedSymptoms}] = nb_inf_t0;
     model_b.populations.set_difference_from_total({mio::AgeGroup(0), mio::osecir::InfectionState::Susceptible},
@@ -644,7 +646,7 @@ TEST(TestOdeSecir, testDamping)
     EXPECT_EQ(2 * result_b[1].get_last_value()[0], result_a[1].get_last_value()[0]);
 
     // no transmission
-    mio::osecir::Model<double> model_c{model_a};
+    ModelT model_c{model_a};
     model_c.populations.set_total(nb_total_t0);
     model_c.populations[{mio::AgeGroup(0), mio::osecir::InfectionState::InfectedSymptoms}] = nb_inf_t0;
     model_c.populations.set_difference_from_total({mio::AgeGroup(0), mio::osecir::InfectionState::Susceptible},
@@ -658,7 +660,7 @@ TEST(TestOdeSecir, testDamping)
     EXPECT_EQ(result_c[1].get_last_value()[0], 0.0);
 
     // increased transmission to a factor of two (by +1)
-    mio::osecir::Model<double> model_d{model_a};
+    ModelT model_d{model_a};
     model_d.populations.set_total(nb_total_t0);
     model_d.populations[{mio::AgeGroup(0), mio::osecir::InfectionState::InfectedSymptoms}] = nb_inf_t0;
     model_d.populations.set_difference_from_total({mio::AgeGroup(0), mio::osecir::InfectionState::Susceptible},
@@ -685,7 +687,7 @@ TEST(TestOdeSecir, testModelConstraints)
     double nb_total_t0 = 1000000, nb_exp_t0 = 10000, nb_inf_t0 = 5000, nb_car_t0 = 500, nb_hosp_t0 = 20, nb_icu_t0 = 0,
            nb_rec_t0 = 10, nb_dead_t0 = 0;
 
-    mio::osecir::Model<double> model(1);
+    ModelT model(1);
 
     model.parameters.get<mio::osecir::TimeExposed<double>>()[(mio::AgeGroup)0]            = 2.6;
     model.parameters.get<mio::osecir::TimeInfectedNoSymptoms<double>>()[(mio::AgeGroup)0] = 2.6;
@@ -771,7 +773,7 @@ TEST(TestOdeSecir, testAndTraceCapacity)
 
     double nb_total_t0 = 10000, nb_exp_t0 = 100, nb_inf_t0 = 50, nb_car_t0 = 50;
 
-    mio::osecir::Model<double> model(1);
+    ModelT model(1);
     auto& params = model.parameters;
 
     params.get<mio::osecir::TimeExposed<double>>()[(mio::AgeGroup)0]            = 3.2;
@@ -818,7 +820,7 @@ TEST(TestOdeSecir, testAndTraceCapacity)
 TEST(TestOdeSecir, getInfectionsRelative)
 {
     size_t num_groups = 3;
-    mio::osecir::Model<double> model((int)num_groups);
+    ModelT model((int)num_groups);
     model.populations[{mio::AgeGroup(0), mio::osecir::InfectionState::InfectedSymptoms}] = 100.0;
     model.populations.set_difference_from_group_total<mio::AgeGroup>(
         {mio::AgeGroup(0), mio::osecir::InfectionState::Susceptible}, 10'000.0);
@@ -837,7 +839,7 @@ TEST(TestOdeSecir, getInfectionsRelative)
 TEST(TestOdeSecir, get_reproduction_number)
 {
     const size_t num_groups = 3;
-    mio::osecir::Model<double> model((int)num_groups);
+    ModelT model((int)num_groups);
 
     mio::ContactMatrixGroup<double>& contact_matrix = model.parameters.get<mio::osecir::ContactPatterns<double>>();
     contact_matrix[0]                               = mio::ContactMatrix<double>(Eigen::MatrixXd::Constant(3, 3, 10));
@@ -1004,13 +1006,13 @@ TEST(TestOdeSecir, get_reproduction_number)
 
 TEST(Secir, get_mobility_factors)
 {
-    auto beta                                                                           = 0.25;
-    auto max_beta                                                                       = 0.5;
-    auto model                                                                          = mio::osecir::Model<double>(1);
-    model.parameters.get<mio::osecir::TimeExposed<double>>().array()                    = 3.;
-    model.parameters.get<mio::osecir::TimeInfectedNoSymptoms<double>>().array()         = 2.;
-    model.parameters.get<mio::osecir::RecoveredPerInfectedNoSymptoms<double>>().array() = 0.1;
-    model.parameters.get<mio::osecir::RiskOfInfectionFromSymptomatic<double>>().array() = beta;
+    auto beta                                                                              = 0.25;
+    auto max_beta                                                                          = 0.5;
+    auto model                                                                             = ModelT(1);
+    model.parameters.get<mio::osecir::TimeExposed<double>>().array()                       = 3.;
+    model.parameters.get<mio::osecir::TimeInfectedNoSymptoms<double>>().array()            = 2.;
+    model.parameters.get<mio::osecir::RecoveredPerInfectedNoSymptoms<double>>().array()    = 0.1;
+    model.parameters.get<mio::osecir::RiskOfInfectionFromSymptomatic<double>>().array()    = beta;
     model.parameters.get<mio::osecir::MaxRiskOfInfectionFromSymptomatic<double>>().array() = max_beta;
     model.populations[{mio::AgeGroup(0), mio::osecir::InfectionState::InfectedNoSymptoms}] = 100;
     mio::osecir::Simulation<double> sim(model, 0.0);
@@ -1038,7 +1040,7 @@ TEST(Secir, get_mobility_factors)
 
 TEST(TestOdeSecir, test_commuters)
 {
-    auto model                                      = mio::osecir::Model<double>(2);
+    auto model                                      = ModelT(2);
     auto mobility_factor                            = 0.1;
     auto non_detection_factor                       = 0.4;
     model.parameters.get_start_commuter_detection() = 0.0;
@@ -1072,7 +1074,7 @@ TEST(TestOdeSecir, test_commuters)
 
 TEST(TestOdeSecir, check_constraints_parameters)
 {
-    auto model = mio::osecir::Model<double>(1);
+    auto model = ModelT(1);
     EXPECT_EQ(model.parameters.check_constraints(), 0);
 
     mio::set_log_level(mio::LogLevel::off);
@@ -1150,7 +1152,7 @@ TEST(TestOdeSecir, check_constraints_parameters)
 
 TEST(TestOdeSecir, apply_constraints_parameters)
 {
-    auto model             = mio::osecir::Model<double>(1);
+    auto model             = ModelT(1);
     auto indx_agegroup     = mio::AgeGroup(0);
     const double tol_times = 1e-1;
 
@@ -1242,7 +1244,7 @@ class ModelTestOdeSecir : public testing::Test
         , num_age_groups(6)
     {
     }
-    mio::osecir::Model<double> model;
+    ModelT model;
     size_t num_age_groups;
 
 protected:
@@ -1274,24 +1276,20 @@ class ModelTestOdeSecir : public testing::Test
 // Initialize model with data frrom external reported data. Check if the model is initialized correctly.
 TEST_F(ModelTestOdeSecir, read_input_data)
 {
-    auto model1 = std::vector<mio::osecir::Model<double>>{model};
-    auto model2 = std::vector<mio::osecir::Model<double>>{model};
-    auto model3 = std::vector<mio::osecir::Model<double>>{model};
-
-    auto read_result1 = mio::osecir::read_input_data_county(model1, {2020, 12, 01}, {1002},
-                                                            std::vector<double>(size_t(num_age_groups), 1.0), 1.0,
-                                                            TEST_GERMANY_PYDATA_DIR, 10);
+    auto model1 = std::vector<mio::Node<ModelT>>{mio::Node<ModelT>(1002, model)};
+    auto model2 = std::vector<mio::Node<ModelT>>{mio::Node<ModelT>(1002, model)};
 
-    auto read_result2 =
-        mio::osecir::read_input_data(model2, {2020, 12, 01}, {1002}, std::vector<double>(size_t(num_age_groups), 1.0),
-                                     1.0, TEST_GERMANY_PYDATA_DIR, 10);
+    auto pydata_germany_path = std::string(TEST_GERMANY_PYDATA_DIR);
+    auto read_result1 = mio::osecir::read_input_data(mio::make_range(model1.begin(), model1.end()), {2020, 12, 01},
+                                                     std::vector<double>(size_t(num_age_groups), 1.0), 1.0,
+                                                     mio::regions::de::EpidataFilenames::county(pydata_germany_path));
 
-    auto read_result_district =
-        mio::osecir::read_input_data(model3, {2020, 12, 01}, {1002}, std::vector<double>(size_t(num_age_groups), 1.0),
-                                     1.0, mio::path_join(TEST_DATA_DIR, "District", "pydata"), 10);
+    auto pydata_district_path = mio::path_join(TEST_DATA_DIR, "District", "pydata");
+    auto read_result_district = mio::osecir::read_input_data(
+        mio::make_range(model2.begin(), model2.end()), {2020, 12, 01}, std::vector<double>(size_t(num_age_groups), 1.0),
+        1.0, mio::regions::de::EpidataFilenames::county(pydata_district_path));
 
     EXPECT_THAT(read_result1, IsSuccess());
-    EXPECT_THAT(read_result2, IsSuccess());
     EXPECT_THAT(read_result_district, IsSuccess());
 
     // values were generated by the tested function; can only check stability of the implementation, not correctness
@@ -1303,11 +1301,9 @@ TEST_F(ModelTestOdeSecir, read_input_data)
          10.5181, 3.2967, 0, 3.28571, 0, 0.657143, 1.33333, 38.3, 7.57143)
             .finished();
 
-    EXPECT_THAT(print_wrap(model1[0].populations.array().cast<double>()),
-                MatrixNear(print_wrap(expected_values), 1e-5, 1e-5));
-    EXPECT_THAT(print_wrap(model2[0].populations.array().cast<double>()),
+    EXPECT_THAT(print_wrap(model1[0].property.populations.array().cast<double>()),
                 MatrixNear(print_wrap(expected_values), 1e-5, 1e-5));
-    EXPECT_THAT(print_wrap(model3[0].populations.array().cast<double>()),
+    EXPECT_THAT(print_wrap(model2[0].property.populations.array().cast<double>()),
                 MatrixNear(print_wrap(expected_values), 1e-5, 1e-5));
 }
 
@@ -1317,16 +1313,15 @@ TEST_F(ModelTestOdeSecir, export_time_series_init)
     TempFileRegister temp_file_register;
     auto tmp_results_dir = temp_file_register.get_unique_path();
     EXPECT_THAT(mio::create_directory(tmp_results_dir), IsSuccess());
-    const auto pydata_dir_Germany = mio::path_join(TEST_DATA_DIR, "Germany", "pydata");
+    auto pydata_dir_Germany = mio::path_join(TEST_DATA_DIR, "Germany", "pydata");
 
     // Test exporting time series
-    std::vector<mio::osecir::Model<double>> models{model};
-    EXPECT_THAT(mio::osecir::export_input_data_county_timeseries(
-                    models, tmp_results_dir, {1002}, {2020, 12, 01}, std::vector<double>(size_t(num_age_groups), 1.0),
-                    1.0, 2, mio::path_join(pydata_dir_Germany, "county_divi_ma7.json"),
-                    mio::path_join(pydata_dir_Germany, "cases_all_county_age_ma7.json"),
-                    mio::path_join(pydata_dir_Germany, "county_current_population.json")),
-                IsSuccess());
+    std::vector<mio::Node<ModelT>> models{mio::Node<ModelT>(1002, model)};
+    EXPECT_THAT(
+        mio::osecir::export_input_data_timeseries(mio::make_range(models.begin(), models.end()), tmp_results_dir,
+                                                  {2020, 12, 01}, std::vector<double>(size_t(num_age_groups), 1.0), 1.0,
+                                                  2, mio::regions::de::EpidataFilenames::county(pydata_dir_Germany)),
+        IsSuccess());
 
     auto data_extrapolated = mio::read_result(mio::path_join(tmp_results_dir, "Results_rki.h5"));
     EXPECT_THAT(data_extrapolated, IsSuccess());
@@ -1347,16 +1342,15 @@ TEST_F(ModelTestOdeSecir, export_time_series_init_old_date)
     TempFileRegister temp_file_register;
     auto tmp_results_dir = temp_file_register.get_unique_path();
     EXPECT_THAT(mio::create_directory(tmp_results_dir), IsSuccess());
-    const auto pydata_dir_Germany = mio::path_join(TEST_DATA_DIR, "Germany", "pydata");
+    auto pydata_dir_Germany = mio::path_join(TEST_DATA_DIR, "Germany", "pydata");
 
     // Test exporting time series
-    std::vector<mio::osecir::Model<double>> models{model};
-    EXPECT_THAT(mio::osecir::export_input_data_county_timeseries(
-                    models, tmp_results_dir, {1002}, {1000, 12, 01}, std::vector<double>(size_t(num_age_groups), 1.0),
-                    1.0, 0, mio::path_join(pydata_dir_Germany, "county_divi_ma7.json"),
-                    mio::path_join(pydata_dir_Germany, "cases_all_county_age_ma7.json"),
-                    mio::path_join(pydata_dir_Germany, "county_current_population.json")),
-                IsSuccess());
+    std::vector<mio::Node<ModelT>> models{mio::Node<ModelT>(1002, model)};
+    EXPECT_THAT(
+        mio::osecir::export_input_data_timeseries(mio::make_range(models.begin(), models.end()), tmp_results_dir,
+                                                  {1000, 12, 01}, std::vector<double>(size_t(num_age_groups), 1.0), 1.0,
+                                                  2, mio::regions::de::EpidataFilenames::county(pydata_dir_Germany)),
+        IsSuccess());
 
     auto data_extrapolated = mio::read_result(mio::path_join(tmp_results_dir, "Results_rki.h5"));
     EXPECT_THAT(data_extrapolated, IsSuccess());
@@ -1382,12 +1376,12 @@ TEST_F(ModelTestOdeSecir, export_time_series_init_old_date)
 TEST_F(ModelTestOdeSecir, model_initialization)
 {
     // Vector assignment necessary as read_input_data_county changes model
-    auto model_vector             = std::vector<mio::osecir::Model<double>>{model};
-    const auto pydata_dir_Germany = mio::path_join(TEST_DATA_DIR, "Germany", "pydata");
+    auto model_vector       = std::vector<mio::Node<ModelT>>{mio::Node<ModelT>(1002, model)};
+    auto pydata_dir_Germany = mio::path_join(TEST_DATA_DIR, "Germany", "pydata");
 
-    EXPECT_THAT(mio::osecir::read_input_data_county(model_vector, {2020, 12, 01}, {1002},
-                                                    std::vector<double>(size_t(num_age_groups), 1.0), 1.0,
-                                                    pydata_dir_Germany, 2, false),
+    EXPECT_THAT(mio::osecir::read_input_data(mio::make_range(model_vector.begin(), model_vector.end()), {2020, 12, 01},
+                                             std::vector<double>(size_t(num_age_groups), 1.0), 1.0,
+                                             mio::regions::de::EpidataFilenames::county(pydata_dir_Germany)),
                 IsSuccess());
 
     // Values from data/export_time_series_init_osecir.h5, for reading in comparison
@@ -1400,7 +1394,7 @@ TEST_F(ModelTestOdeSecir, model_initialization)
          10.5181, 3.2967, 0, 3.28571, 0, 0.657143, 1.33333, 38.3, 7.57143)
             .finished();
 
-    EXPECT_THAT(print_wrap(model_vector[0].populations.array().cast<double>()),
+    EXPECT_THAT(print_wrap(model_vector[0].property.populations.array().cast<double>()),
                 MatrixNear(print_wrap(expected_values), 1e-5, 1e-5));
 }
 
@@ -1409,12 +1403,12 @@ TEST_F(ModelTestOdeSecir, model_initialization_old_date)
 {
     mio::set_log_level(mio::LogLevel::off);
     // Vector assignment necessary as read_input_data_county changes model
-    auto model_vector             = std::vector<mio::osecir::Model<double>>{model};
-    const auto pydata_dir_Germany = mio::path_join(TEST_DATA_DIR, "Germany", "pydata");
+    auto model_vector       = std::vector<mio::Node<ModelT>>{mio::Node<ModelT>(1002, model)};
+    auto pydata_dir_Germany = mio::path_join(TEST_DATA_DIR, "Germany", "pydata");
 
-    EXPECT_THAT(mio::osecir::read_input_data_county(model_vector, {1000, 12, 01}, {1002},
-                                                    std::vector<double>(size_t(num_age_groups), 1.0), 1.0,
-                                                    pydata_dir_Germany, 0, false),
+    EXPECT_THAT(mio::osecir::read_input_data(mio::make_range(model_vector.begin(), model_vector.end()), {1000, 12, 01},
+                                             std::vector<double>(size_t(num_age_groups), 1.0), 1.0,
+                                             mio::regions::de::EpidataFilenames::county(pydata_dir_Germany)),
                 IsSuccess());
 
     // if we enter an old date, the model only should be initialized with the population data.
@@ -1431,7 +1425,7 @@ TEST_F(ModelTestOdeSecir, model_initialization_old_date)
          population_data[0][5], 0, 0, 0, 0, 0, 0, 0, 0, 0)
             .finished();
 
-    auto results_extrapolated = model_vector[0].populations.array().cast<double>();
+    auto results_extrapolated = model_vector[0].property.populations.array().cast<double>();
 
     for (size_t i = 0; i < num_age_groups; i++) {
         EXPECT_EQ(results_extrapolated(i * Eigen::Index(mio::osecir::InfectionState::Count)), population_data[0][i]);
@@ -1444,14 +1438,17 @@ TEST_F(ModelTestOdeSecir, model_initialization_old_date)
 TEST(TestOdeSecir, set_divi_data_invalid_dates)
 {
     mio::set_log_level(mio::LogLevel::off);
-    auto model = mio::osecir::Model<double>(1);
+    auto model = ModelT(1);
     model.populations.array().setConstant(1);
-    auto model_vector = std::vector<mio::osecir::Model<double>>{model};
+    auto model_vector = std::vector<mio::Node<ModelT>>{mio::Node<ModelT>(1001, model)};
 
     // Test with date before DIVI dataset was available.
-    EXPECT_THAT(mio::osecir::details::set_divi_data(model_vector, "", {1001}, {2019, 12, 01}, 1.0), IsSuccess());
+    EXPECT_THAT(mio::osecir::details::set_divi_data(mio::make_range(model_vector.begin(), model_vector.end()), "",
+                                                    {2019, 12, 01}, 1.0),
+                IsSuccess());
+
     // Assure that populations is the same as before.
-    EXPECT_THAT(print_wrap(model_vector[0].populations.array().cast<double>()),
+    EXPECT_THAT(print_wrap(model_vector[0].property.populations.array().cast<double>()),
                 MatrixNear(print_wrap(model.populations.array().cast<double>()), 1e-10, 1e-10));
 
     mio::set_log_level(mio::LogLevel::warn);
@@ -1462,10 +1459,9 @@ TEST(TestOdeSecir, set_divi_data_empty_data)
     // Create an empty DIVI data vector
     std::vector<mio::DiviEntry> empty_data;
     std::vector<int> regions = {0, 1};
-    std::vector<double> num_icu(regions.size(), 0.0);
     mio::Date date(2020, 4, 1);
 
-    auto result = mio::compute_divi_data(empty_data, regions, date, num_icu);
+    auto result = mio::compute_divi_data(empty_data, regions, date);
 
     // Expect failure due to empty DIVI data
     EXPECT_FALSE(result);
@@ -1475,13 +1471,16 @@ TEST(TestOdeSecir, set_divi_data_empty_data)
 
 TEST_F(ModelTestOdeSecir, set_confirmed_cases_data_with_ICU)
 {
+    auto model_test = ModelT{model};
+    model_test.populations.array().setConstant(1);
+
     // set params
     for (auto age_group = mio::AgeGroup(0); age_group < (mio::AgeGroup)num_age_groups; age_group++) {
-        model.parameters.get<mio::osecir::CriticalPerSevere<double>>()[age_group]         = 1.0;
-        model.parameters.get<mio::osecir::SeverePerInfectedSymptoms<double>>()[age_group] = 1.0;
-        model.parameters.get<mio::osecir::TimeInfectedSymptoms<double>>()[age_group]      = 1.0;
-        model.parameters.get<mio::osecir::TimeInfectedSevere<double>>()[age_group]        = 1.0;
-        model.parameters.get<mio::osecir::TimeInfectedCritical<double>>()[age_group]      = 1.0;
+        model_test.parameters.get<mio::osecir::CriticalPerSevere<double>>()[age_group]         = 1.0;
+        model_test.parameters.get<mio::osecir::SeverePerInfectedSymptoms<double>>()[age_group] = 1.0;
+        model_test.parameters.get<mio::osecir::TimeInfectedSymptoms<double>>()[age_group]      = 1.0;
+        model_test.parameters.get<mio::osecir::TimeInfectedSevere<double>>()[age_group]        = 1.0;
+        model_test.parameters.get<mio::osecir::TimeInfectedCritical<double>>()[age_group]      = 1.0;
     }
 
     // read case data
@@ -1502,10 +1501,9 @@ TEST_F(ModelTestOdeSecir, set_confirmed_cases_data_with_ICU)
     auto mid_day = case_data[(size_t)case_data.size() / 2].date;
 
     // calculate ICU values using set_confirmed_cases_data
-    auto model_vector       = std::vector<mio::osecir::Model<double>>{model};
-    auto scaling_factor_inf = std::vector<double>(size_t(model.parameters.get_num_groups()), 1.0);
+    auto scaling_factor_inf = std::vector<double>(size_t(model_test.parameters.get_num_groups()), 1.0);
     EXPECT_THAT(
-        mio::osecir::details::set_confirmed_cases_data(model_vector, case_data, {1002}, mid_day, scaling_factor_inf),
+        mio::osecir::details::set_confirmed_cases_data(model_test, case_data, 1002, mid_day, scaling_factor_inf),
         IsSuccess());
 
     // Since, TimeInfectedCritical is 1, the number of ICU cases is the difference of confirmed cases between two days, which is 1.
@@ -1513,7 +1511,7 @@ TEST_F(ModelTestOdeSecir, set_confirmed_cases_data_with_ICU)
     for (size_t i = 0; i < num_age_groups; ++i) {
         const auto expected_value = (i == 2) ? 1.0 : 0.0;
         const auto actual_value =
-            model_vector[0].populations[{mio::AgeGroup(i), mio::osecir::InfectionState::InfectedCritical}].value();
+            model_test.populations[{mio::AgeGroup(i), mio::osecir::InfectionState::InfectedCritical}].value();
         EXPECT_NEAR(actual_value, expected_value, 1e-10);
     }
 }
@@ -1536,35 +1534,36 @@ TEST(TestOdeSecirIO, read_input_data_county_aggregates_one_group)
 {
     // Set up two models with different number of age groups.
     const size_t num_age_groups = 6;
-    std::vector<mio::osecir::Model<double>> models6{mio::osecir::Model<double>((int)num_age_groups)};
-    std::vector<mio::osecir::Model<double>> models1{mio::osecir::Model<double>(1)};
+    std::vector<mio::Node<ModelT>> models6{mio::Node<ModelT>(1002, (int)num_age_groups)};
+    std::vector<mio::Node<ModelT>> models1{mio::Node<ModelT>(1002, 1)};
 
     // Relevant parameters for model with 6 age groups
     for (auto i = mio::AgeGroup(0); i < (mio::AgeGroup)num_age_groups; ++i) {
-        models6[0].parameters.get<mio::osecir::SeverePerInfectedSymptoms<double>>()[i] = 0.2;
-        models6[0].parameters.get<mio::osecir::CriticalPerSevere<double>>()[i]         = 0.25;
+        models6[0].property.parameters.get<mio::osecir::SeverePerInfectedSymptoms<double>>()[i] = 0.2;
+        models6[0].property.parameters.get<mio::osecir::CriticalPerSevere<double>>()[i]         = 0.25;
     }
 
     // Relevant parameters for model with 1 age group
-    models1[0].parameters.get<mio::osecir::SeverePerInfectedSymptoms<double>>()[mio::AgeGroup(0)] = 0.2;
-    models1[0].parameters.get<mio::osecir::CriticalPerSevere<double>>()[mio::AgeGroup(0)]         = 0.25;
+    models1[0].property.parameters.get<mio::osecir::SeverePerInfectedSymptoms<double>>()[mio::AgeGroup(0)] = 0.2;
+    models1[0].property.parameters.get<mio::osecir::CriticalPerSevere<double>>()[mio::AgeGroup(0)]         = 0.25;
 
-    const auto pydata_dir_Germany = mio::path_join(TEST_DATA_DIR, "Germany", "pydata");
-    const std::vector<int> counties{1002};
-    const auto date = mio::Date(2020, 12, 1);
+    auto pydata_dir_Germany = mio::path_join(TEST_DATA_DIR, "Germany", "pydata");
+    const auto date         = mio::Date(2020, 12, 1);
 
     std::vector<double> scale6(num_age_groups, 1.0);
     std::vector<double> scale1{1.0};
 
     // Initialize both models
-    ASSERT_THAT(mio::osecir::read_input_data_county(models6, date, counties, scale6, 1.0, pydata_dir_Germany),
+    ASSERT_THAT(mio::osecir::read_input_data(mio::make_range(models6.begin(), models6.end()), date, scale6, 1.0,
+                                             mio::regions::de::EpidataFilenames::county(pydata_dir_Germany)),
                 IsSuccess());
-    ASSERT_THAT(mio::osecir::read_input_data_county(models1, date, counties, scale1, 1.0, pydata_dir_Germany),
+    ASSERT_THAT(mio::osecir::read_input_data(mio::make_range(models1.begin(), models1.end()), date, scale1, 1.0,
+                                             mio::regions::de::EpidataFilenames::county(pydata_dir_Germany)),
                 IsSuccess());
 
     // Aggreagate the results from the model with 6 age groups and compare with the model with 1 age group
-    const auto& m6   = models6[0];
-    const auto& m1   = models1[0];
+    const auto& m6   = models6[0].property;
+    const auto& m1   = models1[0].property;
     const double tol = 1e-10;
     for (int s = 0; s < (int)mio::osecir::InfectionState::Count; ++s) {
         double sum6 = 0.0;
@@ -1584,32 +1583,32 @@ TEST(TestOdeSecirIO, set_population_data_single_age_group)
     const size_t num_age_groups = 6;
 
     // Create two models: one with 6 age groups, one with 1 age group
-    std::vector<mio::osecir::Model<double>> models6{mio::osecir::Model<double>((int)num_age_groups)};
-    std::vector<mio::osecir::Model<double>> models1{mio::osecir::Model<double>(1)};
+    ModelT models6{(int)num_age_groups};
+    ModelT models1{1};
 
     // Test population data with 6 different values for age groups
-    std::vector<std::vector<double>> population_data6 = {{10000.0, 20000.0, 30000.0, 25000.0, 15000.0, 8000.0}};
-    std::vector<std::vector<double>> population_data1 = {{108000.0}}; // sum of all age groups
-    std::vector<int> regions                          = {1002};
+    std::vector<double> population_data6 = {10000.0, 20000.0, 30000.0, 25000.0, 15000.0, 8000.0};
+    std::vector<double> population_data1 = {108000.0}; // sum of all age groups
+    const int region                     = 1002;
 
     // Set population data for both models
-    EXPECT_THAT(mio::osecir::details::set_population_data(models6, population_data6, regions), IsSuccess());
-    EXPECT_THAT(mio::osecir::details::set_population_data(models1, population_data1, regions), IsSuccess());
+    EXPECT_THAT(mio::osecir::details::set_population_data(models6, population_data6, region), IsSuccess());
+    EXPECT_THAT(mio::osecir::details::set_population_data(models1, population_data1, region), IsSuccess());
 
     // Sum all compartments across age groups in 6-group model and compare 1-group model
     const double tol = 1e-10;
     for (int s = 0; s < (int)mio::osecir::InfectionState::Count; ++s) {
         double sum6 = 0.0;
         for (size_t ag = 0; ag < num_age_groups; ++ag) {
-            sum6 += models6[0].populations[{mio::AgeGroup(ag), (mio::osecir::InfectionState)s}].value();
+            sum6 += models6.populations[{mio::AgeGroup(ag), (mio::osecir::InfectionState)s}].value();
         }
-        double val1 = models1[0].populations[{mio::AgeGroup(0), (mio::osecir::InfectionState)s}].value();
+        double val1 = models1.populations[{mio::AgeGroup(0), (mio::osecir::InfectionState)s}].value();
 
         EXPECT_NEAR(sum6, val1, tol);
     }
 
     // Total population should also match
-    EXPECT_NEAR(models6[0].populations.get_total(), models1[0].populations.get_total(), tol);
+    EXPECT_NEAR(models6.populations.get_total(), models1.populations.get_total(), tol);
 }
 
 TEST(TestOdeSecirIO, set_confirmed_cases_data_single_age_group)
@@ -1617,8 +1616,8 @@ TEST(TestOdeSecirIO, set_confirmed_cases_data_single_age_group)
     const size_t num_age_groups = 6;
 
     // Create two models: one with 6 age groups, one with 1 age group
-    std::vector<mio::osecir::Model<double>> models6{mio::osecir::Model<double>((int)num_age_groups)};
-    std::vector<mio::osecir::Model<double>> models1{mio::osecir::Model<double>(1)};
+    ModelT models6{(int)num_age_groups};
+    ModelT models1{1};
 
     // Create case data for all 6 age groups over multiple days (current day + 6 days back)
     std::vector<mio::ConfirmedCasesDataEntry> case_data;
@@ -1626,7 +1625,7 @@ TEST(TestOdeSecirIO, set_confirmed_cases_data_single_age_group)
     for (int day_offset = -6; day_offset <= 0; ++day_offset) {
         mio::Date current_date = mio::offset_date_by_days(mio::Date(2020, 12, 1), day_offset);
 
-        for (int age_group = 0; age_group < 6; ++age_group) {
+        for (int age_group = 0; age_group < (int)num_age_groups; ++age_group) {
             double base_confirmed = 80.0 + age_group * 8.0 + (day_offset + 6) * 5.0;
             double base_recovered = 40.0 + age_group * 4.0 + (day_offset + 6) * 3.0;
             double base_deaths    = 3.0 + age_group * 0.5 + (day_offset + 6) * 0.5;
@@ -1643,14 +1642,14 @@ TEST(TestOdeSecirIO, set_confirmed_cases_data_single_age_group)
         }
     }
 
-    std::vector<int> regions            = {1002};
+    const int region                    = 1002;
     std::vector<double> scaling_factors = {1.0};
 
     // Set confirmed cases data for both models
-    EXPECT_THAT(mio::osecir::details::set_confirmed_cases_data(models6, case_data, regions, mio::Date(2020, 12, 1),
+    EXPECT_THAT(mio::osecir::details::set_confirmed_cases_data(models6, case_data, region, mio::Date(2020, 12, 1),
                                                                scaling_factors),
                 IsSuccess());
-    EXPECT_THAT(mio::osecir::details::set_confirmed_cases_data(models1, case_data, regions, mio::Date(2020, 12, 1),
+    EXPECT_THAT(mio::osecir::details::set_confirmed_cases_data(models1, case_data, region, mio::Date(2020, 12, 1),
                                                                scaling_factors),
                 IsSuccess());
 
@@ -1658,43 +1657,46 @@ TEST(TestOdeSecirIO, set_confirmed_cases_data_single_age_group)
     for (int s = 0; s < (int)mio::osecir::InfectionState::Count; ++s) {
         double sum6 = 0.0;
         for (size_t ag = 0; ag < num_age_groups; ++ag) {
-            sum6 += models6[0].populations[{mio::AgeGroup(ag), (mio::osecir::InfectionState)s}].value();
+            sum6 += models6.populations[{mio::AgeGroup(ag), (mio::osecir::InfectionState)s}].value();
         }
 
-        double val1 = models1[0].populations[{mio::AgeGroup(0), (mio::osecir::InfectionState)s}].value();
+        double val1 = models1.populations[{mio::AgeGroup(0), (mio::osecir::InfectionState)s}].value();
 
         EXPECT_NEAR(sum6, val1, 1e-10);
     }
 
     // Total population
-    EXPECT_NEAR(models6[0].populations.get_total(), models1[0].populations.get_total(), 1e-10);
+    EXPECT_NEAR(models6.populations.get_total(), models1.populations.get_total(), 1e-10);
 }
 
 TEST(TestOdeSecirIO, set_divi_data_single_age_group)
 {
+    const size_t num_age_groups = 6;
+
     // Create models with 6 age groups and 1 age group
-    std::vector<mio::osecir::Model<double>> models_6_groups{mio::osecir::Model<double>(6)};
-    std::vector<mio::osecir::Model<double>> models_1_group{mio::osecir::Model<double>(1)};
+    ModelT models6{(int)num_age_groups};
+    ModelT models1{1};
 
     // Set relevant parameters for all age groups
-    for (int i = 0; i < 6; i++) {
-        models_6_groups[0].parameters.get<mio::osecir::SeverePerInfectedSymptoms<double>>()[mio::AgeGroup(i)] = 0.2;
-        models_6_groups[0].parameters.get<mio::osecir::CriticalPerSevere<double>>()[mio::AgeGroup(i)]         = 0.25;
+    for (int i = 0; i < (int)num_age_groups; i++) {
+        models6.parameters.get<mio::osecir::SeverePerInfectedSymptoms<double>>()[mio::AgeGroup(i)] = 0.2;
+        models6.parameters.get<mio::osecir::CriticalPerSevere<double>>()[mio::AgeGroup(i)]         = 0.25;
     }
 
     // Set relevant parameters for 1 age group model
-    models_1_group[0].parameters.get<mio::osecir::SeverePerInfectedSymptoms<double>>()[mio::AgeGroup(0)] = 0.2;
-    models_1_group[0].parameters.get<mio::osecir::CriticalPerSevere<double>>()[mio::AgeGroup(0)]         = 0.25;
+    models1.parameters.get<mio::osecir::SeverePerInfectedSymptoms<double>>()[mio::AgeGroup(0)] = 0.2;
+    models1.parameters.get<mio::osecir::CriticalPerSevere<double>>()[mio::AgeGroup(0)]         = 0.25;
 
     // Apply DIVI data to both models
-    std::vector<int> regions  = {1002};
+    const int region          = 1002;
     double scaling_factor_icu = 1.0;
     mio::Date date(2020, 12, 1);
     std::string divi_data_path = mio::path_join(TEST_DATA_DIR, "Germany", "pydata", "county_divi_ma7.json");
-    auto result_6_groups =
-        mio::osecir::details::set_divi_data(models_6_groups, divi_data_path, regions, date, scaling_factor_icu);
-    auto result_1_group =
-        mio::osecir::details::set_divi_data(models_1_group, divi_data_path, regions, date, scaling_factor_icu);
+
+    auto num_icu = read_divi_data(divi_data_path, {region}, date).value();
+
+    auto result_6_groups = mio::osecir::details::set_divi_data(models6, num_icu[0], scaling_factor_icu);
+    auto result_1_group  = mio::osecir::details::set_divi_data(models1, num_icu[0], scaling_factor_icu);
 
     EXPECT_THAT(result_6_groups, IsSuccess());
     EXPECT_THAT(result_1_group, IsSuccess());
@@ -1703,10 +1705,10 @@ TEST(TestOdeSecirIO, set_divi_data_single_age_group)
     double total_icu_6_groups_after = 0.0;
     for (int i = 0; i < 6; i++) {
         total_icu_6_groups_after +=
-            models_6_groups[0].populations[{mio::AgeGroup(i), mio::osecir::InfectionState::InfectedCritical}].value();
+            models6.populations[{mio::AgeGroup(i), mio::osecir::InfectionState::InfectedCritical}].value();
     }
     double icu_1_group_after =
-        models_1_group[0].populations[{mio::AgeGroup(0), mio::osecir::InfectionState::InfectedCritical}].value();
+        models1.populations[{mio::AgeGroup(0), mio::osecir::InfectionState::InfectedCritical}].value();
 
     EXPECT_NEAR(total_icu_6_groups_after, icu_1_group_after, 1e-10);
 }
diff --git a/cpp/tests/test_odesecirts.cpp b/cpp/tests/test_odesecirts.cpp
index 4bf0d0e1a8..f0be75b0d8 100755
--- a/cpp/tests/test_odesecirts.cpp
+++ b/cpp/tests/test_odesecirts.cpp
@@ -50,9 +50,11 @@
 #include <iterator>
 #include <limits>
 
-const mio::osecirts::Model<double>& osecirts_testing_model()
+using ModelT = mio::osecirts::Model<double>;
+
+const ModelT& osecirts_testing_model()
 {
-    static mio::osecirts::Model<double> model(1);
+    static ModelT model(1);
     model.populations.array().setConstant(1);
     auto nb_groups = model.parameters.get_num_groups();
 
@@ -153,7 +155,7 @@ TEST(TestOdeSECIRTS, Simulation)
     EXPECT_EQ(sim.get_result().get_last_value(), expected_result);
 }
 
-using FlowSim = mio::osecirts::Simulation<double, mio::FlowSimulation<double, mio::osecirts::Model<double>>>;
+using FlowSim = mio::osecirts::Simulation<double, mio::FlowSimulation<double, ModelT>>;
 
 TEST(TestOdeSECIRTS, FlowSimulation)
 {
@@ -182,7 +184,7 @@ TEST(TestOdeSECIRTS, simulateDefault)
     double tmax = 1;
     double dt   = 0.1;
 
-    mio::osecirts::Model<double> model(1);
+    ModelT model(1);
     model.populations.set_total(10);
     model.populations.set_difference_from_total({(mio::AgeGroup)0, mio::osecirts::InfectionState::SusceptibleNaive},
                                                 10);
@@ -192,7 +194,7 @@ TEST(TestOdeSECIRTS, simulateDefault)
     model.parameters.get<mio::osecirts::DailyFullVaccinations<double>>().array().setConstant(0);
     model.parameters.get<mio::osecirts::DailyBoosterVaccinations<double>>().resize(mio::SimulationDay(size_t(1000)));
     model.parameters.get<mio::osecirts::DailyBoosterVaccinations<double>>().array().setConstant(0);
-    auto result = mio::simulate<double, mio::osecirts::Model<double>>(t0, tmax, dt, model);
+    auto result = mio::simulate<double, ModelT>(t0, tmax, dt, model);
 
     EXPECT_NEAR(result.get_last_time(), tmax, 1e-10);
 }
@@ -201,7 +203,7 @@ TEST(TestOdeSECIRTS, simulateDefault)
 TEST(TestOdeSECIRTS, population_zero_no_nan)
 {
     // initialize simple model with total population 0
-    mio::osecirts::Model<double> model(1);
+    ModelT model(1);
     model.populations.set_total(0.0);
 
     model.parameters.get<mio::osecirts::DailyPartialVaccinations<double>>().resize(mio::SimulationDay(size_t(1000)));
@@ -230,7 +232,7 @@ TEST(TestOdeSECIRTS, overflow_vaccinations)
     const double dt   = 1;
 
     // init simple model
-    mio::osecirts::Model<double> model(1);
+    ModelT model(1);
     model.populations[{mio::AgeGroup(0), mio::osecirts::InfectionState::SusceptibleNaive}]            = 10.;
     model.populations[{mio::AgeGroup(0), mio::osecirts::InfectionState::SusceptiblePartialImmunity}]  = 10.;
     model.populations[{mio::AgeGroup(0), mio::osecirts::InfectionState::SusceptibleImprovedImmunity}] = 10.;
@@ -258,7 +260,7 @@ TEST(TestOdeSECIRTS, overflow_vaccinations)
     }
 
     // simulate one step with explicit Euler
-    auto result     = mio::simulate_flows<double, mio::osecirts::Model<double>>(t0, tmax, dt, model, std::make_unique<mio::EulerIntegratorCore<double>>());
+    auto result     = mio::simulate_flows<double, ModelT>(t0, tmax, dt, model, std::make_unique<mio::EulerIntegratorCore<double>>());
 
     // get the flow indices for each type of vaccination and also the indices of the susceptible compartments
     auto flow_indx_partial_vaccination =
@@ -286,10 +288,10 @@ TEST(TestOdeSECIRTS, overflow_vaccinations)
 
 TEST(TestOdeSECIRTS, smooth_vaccination_rate)
 {
-    const ScalarType tmax = 2.;
+    const double tmax = 2.;
 
     // init simple model
-    mio::osecirts::Model<double> model(1);
+    ModelT model(1);
     auto& daily_vaccinations = model.parameters.get<mio::osecirts::DailyPartialVaccinations<double>>();
     daily_vaccinations.resize(mio::SimulationDay(static_cast<size_t>(tmax + 1)));
 
@@ -372,7 +374,7 @@ void array_assign_uniform_distribution(mio::CustomIndexArray<mio::UncertainValue
 }
 } // namespace
 
-void set_synthetic_population_data(mio::osecirts::Model<double>::Populations& populations,
+void set_synthetic_population_data(ModelT::Populations& populations,
                                    bool set_invalid_initial_value)
 {
     for (mio::AgeGroup i = 0; i < mio::get<mio::AgeGroup>(populations.size()); i++) {
@@ -433,7 +435,7 @@ void set_synthetic_population_data(mio::osecirts::Model<double>::Populations& po
     }
 }
 
-void set_demographic_parameters(mio::osecirts::Model<double>::ParameterSet& parameters, bool set_invalid_initial_value)
+void set_demographic_parameters(ModelT::ParameterSet& parameters, bool set_invalid_initial_value)
 {
     assign_uniform_distribution(parameters.get<mio::osecirts::ICUCapacity<double>>(), 20, 50,
                                 set_invalid_initial_value);
@@ -447,7 +449,7 @@ void set_demographic_parameters(mio::osecirts::Model<double>::ParameterSet& para
     parameters.get<mio::osecirts::DailyBoosterVaccinations<double>>().array().setConstant(3);
 }
 
-void set_contact_parameters(mio::osecirts::Model<double>::ParameterSet& parameters, bool set_invalid_initial_value)
+void set_contact_parameters(ModelT::ParameterSet& parameters, bool set_invalid_initial_value)
 {
     auto& contacts       = parameters.get<mio::osecirts::ContactPatterns<double>>();
     auto& contact_matrix = contacts.get_cont_freq_mat();
@@ -467,7 +469,7 @@ void set_contact_parameters(mio::osecirts::Model<double>::ParameterSet& paramete
     parameters.get_end_dynamic_npis() = 10.0; //required for dynamic NPIs to have effect in this model
 }
 
-void set_covid_parameters(mio::osecirts::Model<double>::ParameterSet& params, bool set_invalid_initial_value)
+void set_covid_parameters(ModelT::ParameterSet& params, bool set_invalid_initial_value)
 {
     //times
     const double timeExposedMin            = 2.67;
@@ -587,10 +589,10 @@ void set_covid_parameters(mio::osecirts::Model<double>::ParameterSet& params, bo
 
 namespace
 {
-mio::osecirts::Model<double> make_model(int num_age_groups, bool set_invalid_initial_value = false)
+ModelT make_model(int num_age_groups, bool set_invalid_initial_value = false)
 {
     assert(num_age_groups <= 6 && "Provide more values in functions above to test more age groups.");
-    mio::osecirts::Model<double> model(num_age_groups);
+    ModelT model(num_age_groups);
     set_covid_parameters(model.parameters, set_invalid_initial_value);
     set_synthetic_population_data(model.populations, set_invalid_initial_value);
     set_demographic_parameters(model.parameters, set_invalid_initial_value);
@@ -604,7 +606,7 @@ TEST(TestOdeSECIRTS, draw_sample_graph)
 {
     mio::log_thread_local_rng_seeds(mio::LogLevel::warn);
 
-    mio::Graph<mio::osecirts::Model<double>, mio::MobilityParameters<double>> graph;
+    mio::Graph<ModelT, mio::MobilityParameters<double>> graph;
 
     auto num_age_groups = 6;
     //create model with invalid initials so the test fails if no sampling is done
@@ -711,44 +713,90 @@ TEST(TestOdeSECIRTS, checkPopulationConservation)
 TEST(TestOdeSECIRTS, read_confirmed_cases)
 {
     auto num_age_groups = 6; //reading data requires RKI data age groups
-    auto model          = std::vector<mio::osecirts::Model<double>>({make_model(num_age_groups)});
-    const std::vector<int> region{1002};
+    ModelT model = make_model(num_age_groups);
+    const int region = 1002;
     auto path = mio::path_join(TEST_DATA_DIR, "Germany/pydata/cases_all_county_age_ma7.json");
 
-    std::vector<std::vector<double>> num_InfectedSymptoms(1);
-    std::vector<std::vector<double>> num_death(1);
-    std::vector<std::vector<double>> num_timm(1);
-    std::vector<std::vector<double>> num_Exposed(1);
-    std::vector<std::vector<double>> num_InfectedNoSymptoms(1);
-    std::vector<std::vector<double>> num_InfectedSevere(1);
-    std::vector<std::vector<double>> num_icu(1);
-
-    num_InfectedSymptoms[0]   = std::vector<double>(num_age_groups, 0.0);
-    num_death[0]              = std::vector<double>(num_age_groups, 0.0);
-    num_timm[0]               = std::vector<double>(num_age_groups, 0.0);
-    num_Exposed[0]            = std::vector<double>(num_age_groups, 0.0);
-    num_InfectedNoSymptoms[0] = std::vector<double>(num_age_groups, 0.0);
-    num_InfectedSevere[0]     = std::vector<double>(num_age_groups, 0.0);
-    num_icu[0]                = std::vector<double>(num_age_groups, 0.0);
-
-    ASSERT_THAT(mio::osecirts::details::read_confirmed_cases_data(
-                    path, region, {2020, 12, 01}, num_Exposed, num_InfectedNoSymptoms, num_InfectedSymptoms,
-                    num_InfectedSevere, num_icu, num_death, num_timm, model,
+    std::vector<int> t_Exposed;
+    std::vector<int> t_InfectedNoSymptoms;
+    std::vector<int> t_InfectedSymptoms;
+    std::vector<int> t_InfectedSevere;
+    std::vector<int> t_InfectedCritical;
+    std::vector<int> t_imm_interval_i;
+
+    std::vector<double> mu_C_R;
+    std::vector<double> mu_I_H;
+    std::vector<double> mu_H_U;
+
+    std::vector<double> reduc_t_Infected;
+    std::vector<double> reduc_Exposed;
+    std::vector<double> reduc_InfectedSymptoms;
+    std::vector<double> reduc_icu_death;
+
+    std::vector<double> num_InfectedSymptoms(num_age_groups, 0.0);
+    std::vector<double> num_death(num_age_groups, 0.0);
+    std::vector<double> num_rec(num_age_groups, 0.0);
+    std::vector<double> num_Exposed(num_age_groups, 0.0);
+    std::vector<double> num_InfectedNoSymptoms(num_age_groups, 0.0);
+    std::vector<double> num_InfectedSevere(num_age_groups, 0.0);
+    std::vector<double> num_icu(num_age_groups, 0.0);
+    std::vector<double> num_timm(num_age_groups, 0.0);
+
+    for (int group = 0; group < num_age_groups; group++) {
+        t_Exposed.push_back(
+            static_cast<int>(std::round(model.parameters.template get<mio::osecirts::TimeExposed<double>>()[(mio::AgeGroup)group])));
+        t_InfectedNoSymptoms.push_back(static_cast<int>(
+            std::round(model.parameters.template get<mio::osecirts::TimeInfectedNoSymptoms<double>>()[(mio::AgeGroup)group])));
+        t_InfectedSymptoms.push_back(static_cast<int>(
+            std::round(model.parameters.template get<mio::osecirts::TimeInfectedSymptoms<double>>()[(mio::AgeGroup)group])));
+        t_InfectedSevere.push_back(static_cast<int>(
+            std::round(model.parameters.template get<mio::osecirts::TimeInfectedSevere<double>>()[(mio::AgeGroup)group])));
+        t_InfectedCritical.push_back(static_cast<int>(
+            std::round(model.parameters.template get<mio::osecirts::TimeInfectedCritical<double>>()[(mio::AgeGroup)group])));
+        t_imm_interval_i.push_back(static_cast<int>(
+            std::round(model.parameters.template get<mio::osecirts::TimeTemporaryImmunityPI<double>>()[(mio::AgeGroup)group])));
+
+        mu_C_R.push_back(model.parameters.template get<mio::osecirts::RecoveredPerInfectedNoSymptoms<double>>()[(mio::AgeGroup)group]);
+        mu_I_H.push_back(model.parameters.template get<mio::osecirts::SeverePerInfectedSymptoms<double>>()[(mio::AgeGroup)group]);
+        mu_H_U.push_back(model.parameters.template get<mio::osecirts::CriticalPerSevere<double>>()[(mio::AgeGroup)group]);
+
+        reduc_t_Infected.push_back(model.parameters.template get<mio::osecirts::ReducTimeInfectedMild<double>>()[(mio::AgeGroup)group]);
+        reduc_Exposed.push_back(
+            model.parameters.template get<mio::osecirts::ReducExposedPartialImmunity<double>>()[(mio::AgeGroup)group]);
+        reduc_InfectedSymptoms.push_back(
+            model.parameters.template get<mio::osecirts::ReducInfectedSymptomsPartialImmunity<double>>()[(mio::AgeGroup)group]);
+        reduc_icu_death.push_back(
+            model.parameters
+                .template get<mio::osecirts::ReducInfectedSevereCriticalDeadPartialImmunity<double>>()[(mio::AgeGroup)group]);
+    }
+
+    auto case_data =
+        mio::read_confirmed_cases_data(path).value();
+
+    ASSERT_THAT(mio::osecirts::details::compute_confirmed_cases_data(
+                    case_data, region, {2020, 12, 01}, num_Exposed, num_InfectedNoSymptoms, num_InfectedSymptoms,
+                    num_InfectedSevere, num_icu, num_death, num_timm, t_Exposed, t_InfectedNoSymptoms, t_InfectedSymptoms,
+                    t_InfectedSevere, t_InfectedCritical, t_imm_interval_i, mu_C_R, mu_I_H, mu_H_U, reduc_t_Infected, 
+                    reduc_Exposed, reduc_InfectedSymptoms, reduc_icu_death, 
                     std::vector<double>(size_t(num_age_groups), 1.0), 0),
                 IsSuccess());
 
     // read again with invalid date
-    ASSERT_THAT(mio::osecirts::details::read_confirmed_cases_data(
-                    path, region, {3020, 12, 01}, num_Exposed, num_InfectedNoSymptoms, num_InfectedSymptoms,
-                    num_InfectedSevere, num_icu, num_death, num_timm, model,
+    ASSERT_THAT(mio::osecirts::details::compute_confirmed_cases_data(
+                    case_data, region, {3020, 12, 01}, num_Exposed, num_InfectedNoSymptoms, num_InfectedSymptoms,
+                    num_InfectedSevere, num_icu, num_death, num_timm, t_Exposed, t_InfectedNoSymptoms, t_InfectedSymptoms,
+                    t_InfectedSevere, t_InfectedCritical, t_imm_interval_i, mu_C_R, mu_I_H, mu_H_U, reduc_t_Infected, 
+                    reduc_Exposed, reduc_InfectedSymptoms, reduc_icu_death, 
                     std::vector<double>(size_t(num_age_groups), 1.0), 0),
                 IsFailure(mio::StatusCode::OutOfRange));
 
     // call the compute function with empty case data
     const std::vector<mio::ConfirmedCasesDataEntry> empty_case_data;
     ASSERT_THAT(mio::osecirts::details::compute_confirmed_cases_data(
-                    empty_case_data, num_Exposed, num_InfectedNoSymptoms, num_InfectedSymptoms, num_InfectedSevere,
-                    num_icu, num_death, num_timm, region, {2020, 12, 01}, model,
+                    empty_case_data, region, {2020, 12, 01}, num_Exposed, num_InfectedNoSymptoms, num_InfectedSymptoms,
+                    num_InfectedSevere, num_icu, num_death, num_timm, t_Exposed, t_InfectedNoSymptoms, t_InfectedSymptoms,
+                    t_InfectedSevere, t_InfectedCritical, t_imm_interval_i, mu_C_R, mu_I_H, mu_H_U, reduc_t_Infected, 
+                    reduc_Exposed, reduc_InfectedSymptoms, reduc_icu_death, 
                     std::vector<double>(size_t(num_age_groups), 1.0), 0),
                 IsFailure(mio::StatusCode::InvalidValue));
 }
@@ -756,14 +804,16 @@ TEST(TestOdeSECIRTS, read_confirmed_cases)
 TEST(TestOdeSECIRTS, set_divi_data_invalid_dates)
 {
     mio::set_log_level(mio::LogLevel::off);
-    auto model = mio::osecirts::Model<double>(1);
+    auto model = ModelT(1);
     model.populations.array().setConstant(1);
-    auto model_vector = std::vector<mio::osecirts::Model<double>>{model};
+    auto model_vector = std::vector<mio::Node<ModelT>>{mio::Node<ModelT>(1001, model)};
 
     // Test with date before DIVI dataset was available.
-    EXPECT_THAT(mio::osecirts::details::set_divi_data(model_vector, "", {1001}, {2019, 12, 01}, 1.0), IsSuccess());
+    EXPECT_THAT(mio::osecirts::details::set_divi_data(mio::make_range(model_vector.begin(), model_vector.end()), 
+                                                    "", {2019, 12, 01}, 1.0), 
+                IsSuccess());
     // Assure that populations is the same as before.
-    EXPECT_THAT(print_wrap(model_vector[0].populations.array().cast<double>()),
+    EXPECT_THAT(print_wrap(model_vector[0].property.populations.array().cast<double>()),
                 MatrixNear(print_wrap(model.populations.array().cast<double>()), 1e-10, 1e-10));
     mio::set_log_level(mio::LogLevel::warn);
 }
@@ -771,7 +821,7 @@ TEST(TestOdeSECIRTS, set_divi_data_invalid_dates)
 TEST(TestOdeSECIRTS, set_confirmed_cases_data_with_ICU)
 {
     const auto num_age_groups = 6;
-    auto model                = mio::osecirts::Model<double>(num_age_groups);
+    auto model                = ModelT(num_age_groups);
     model.populations.array().setConstant(1);
 
     const std::vector<std::vector<double>> immunity_population = {{0.04, 0.04, 0.075, 0.08, 0.035, 0.01},
@@ -805,10 +855,9 @@ TEST(TestOdeSECIRTS, set_confirmed_cases_data_with_ICU)
     auto mid_day = case_data[(size_t)case_data.size() / 2].date;
 
     // calculate ICU values using set_confirmed_cases_data
-    auto model_vector       = std::vector<mio::osecirts::Model<double>>{model};
     auto scaling_factor_inf = std::vector<double>(size_t(model.parameters.get_num_groups()), 1.0);
 
-    EXPECT_THAT(mio::osecirts::details::set_confirmed_cases_data(model_vector, case_data, {1002}, mid_day,
+    EXPECT_THAT(mio::osecirts::details::set_confirmed_cases_data(model, case_data, 1002, mid_day,
                                                                  scaling_factor_inf, immunity_population),
                 IsSuccess());
 
@@ -818,20 +867,17 @@ TEST(TestOdeSECIRTS, set_confirmed_cases_data_with_ICU)
         const auto expected_value = (i == 2) ? 1.0 : 0.0;
 
         auto actual_value_naive =
-            model_vector[0]
-                .populations[{mio::AgeGroup(i), mio::osecirts::InfectionState::InfectedCriticalNaive}]
+            model.populations[{mio::AgeGroup(i), mio::osecirts::InfectionState::InfectedCriticalNaive}]
                 .value();
         EXPECT_NEAR(actual_value_naive, expected_value * immunity_population[0][i], 1e-10);
 
         auto actual_value_pi =
-            model_vector[0]
-                .populations[{mio::AgeGroup(i), mio::osecirts::InfectionState::InfectedCriticalPartialImmunity}]
+            model.populations[{mio::AgeGroup(i), mio::osecirts::InfectionState::InfectedCriticalPartialImmunity}]
                 .value();
         EXPECT_NEAR(actual_value_pi, expected_value * immunity_population[1][i], 1e-10);
 
         auto actual_value_ii =
-            model_vector[0]
-                .populations[{mio::AgeGroup(i), mio::osecirts::InfectionState::InfectedCriticalImprovedImmunity}]
+            model.populations[{mio::AgeGroup(i), mio::osecirts::InfectionState::InfectedCriticalImprovedImmunity}]
                 .value();
         EXPECT_NEAR(actual_value_ii, expected_value * immunity_population[2][i], 1e-10);
     }
@@ -840,28 +886,27 @@ TEST(TestOdeSECIRTS, set_confirmed_cases_data_with_ICU)
 TEST(TestOdeSECIRTS, read_data)
 {
     auto num_age_groups = 6; //reading data requires RKI data age groups
-    auto model1         = std::vector<mio::osecirts::Model<double>>({make_model(num_age_groups)});
-    auto model2         = std::vector<mio::osecirts::Model<double>>({make_model(num_age_groups)});
-    auto model3         = std::vector<mio::osecirts::Model<double>>({make_model(num_age_groups)});
+    auto model = make_model(num_age_groups);
+    auto model1 = std::vector<mio::Node<ModelT>>{mio::Node<ModelT>(1002, model)};
+    auto model2 = std::vector<mio::Node<ModelT>>{mio::Node<ModelT>(1002, model)};
 
     const std::vector<std::vector<double>> immunity_population = {{0.04, 0.04, 0.075, 0.08, 0.035, 0.01},
                                                                   {0.61, 0.61, 0.62, 0.62, 0.58, 0.41},
                                                                   {0.35, 0.35, 0.305, 0.3, 0.385, 0.58}};
 
-    auto read_result1 = mio::osecirts::read_input_data_county(model1, {2020, 12, 01}, {1002},
-                                                              std::vector<double>(size_t(num_age_groups), 1.0), 1.0,
-                                                              TEST_GERMANY_PYDATA_DIR, 10, immunity_population);
-
-    auto read_result2 =
-        mio::osecirts::read_input_data(model2, {2020, 12, 01}, {1002}, std::vector<double>(size_t(num_age_groups), 1.0),
-                                       1.0, TEST_GERMANY_PYDATA_DIR, 10, immunity_population);
-
-    auto read_result_district =
-        mio::osecirts::read_input_data(model3, {2020, 12, 01}, {1002}, std::vector<double>(size_t(num_age_groups), 1.0),
-                                       1.0, mio::path_join(TEST_DATA_DIR, "District/pydata"), 10, immunity_population);
+    auto pydata_germany_path = std::string(TEST_GERMANY_PYDATA_DIR);
+    auto read_result1 = 
+        mio::osecirts::read_input_data(mio::make_range(model1.begin(), model1.end()), {2020, 12, 01}, 
+                                       std::vector<double>(size_t(num_age_groups), 1.0), 1.0, 10, immunity_population,
+                                       mio::regions::de::EpidataFilenames::county(pydata_germany_path));
+    
+    auto pydata_district_path = mio::path_join(TEST_DATA_DIR, "District", "pydata");
+    auto read_result_district = 
+        mio::osecirts::read_input_data(mio::make_range(model2.begin(), model2.end()), {2020, 12, 01}, 
+                                     std::vector<double>(size_t(num_age_groups), 1.0), 1.0, 10, immunity_population,
+                                     mio::regions::de::EpidataFilenames::county(pydata_district_path));
 
     ASSERT_THAT(read_result1, IsSuccess());
-    ASSERT_THAT(read_result2, IsSuccess());
     ASSERT_THAT(read_result_district, IsSuccess());
 
     // values were generated by the tested function; can only check stability of the implementation, not correctness
@@ -881,118 +926,86 @@ TEST(TestOdeSECIRTS, read_data)
          0.530478, 0.155246)
             .finished();
 
-    ASSERT_THAT(print_wrap(model1[0].populations.array().cast<double>()),
-                MatrixNear(print_wrap(model2[0].populations.array().cast<double>()), 1e-5, 1e-5));
+    ASSERT_THAT(print_wrap(model1[0].property.populations.array().cast<double>()),
+                MatrixNear(print_wrap(model2[0].property.populations.array().cast<double>()), 1e-5, 1e-5));
 
-    ASSERT_THAT(print_wrap(model1[0].populations.array().cast<double>()),
-                MatrixNear(print_wrap(model3[0].populations.array().cast<double>()), 1e-5, 1e-5));
-    ASSERT_THAT(print_wrap(model1[0].populations.array().cast<double>()),
-                MatrixNear(print_wrap(expected_values), 1e-5, 1e-5));
-    ASSERT_THAT(print_wrap(model2[0].populations.array().cast<double>()),
+    ASSERT_THAT(print_wrap(model1[0].property.populations.array().cast<double>()),
                 MatrixNear(print_wrap(expected_values), 1e-5, 1e-5));
-    ASSERT_THAT(print_wrap(model3[0].populations.array().cast<double>()),
+    ASSERT_THAT(print_wrap(model2[0].property.populations.array().cast<double>()),
                 MatrixNear(print_wrap(expected_values), 1e-5, 1e-5));
 
     // some more tests which are actually not necessary but can help if the previous tests fails or needs to get changed
-    for (mio::AgeGroup i = 0; i < model1[0].parameters.get_num_groups(); i++) {
-        EXPECT_GE(double(model1[0].populations[{i, mio::osecirts::InfectionState::InfectedNoSymptomsNaive}]), 0);
-        EXPECT_GE(double(model2[0].populations[{i, mio::osecirts::InfectionState::InfectedNoSymptomsNaive}]), 0);
-        EXPECT_GE(double(model3[0].populations[{i, mio::osecirts::InfectionState::InfectedNoSymptomsNaive}]), 0);
-        EXPECT_GE(double(model1[0].populations[{i, mio::osecirts::InfectionState::InfectedSymptomsNaive}]), 0);
-        EXPECT_GE(double(model2[0].populations[{i, mio::osecirts::InfectionState::InfectedSymptomsNaive}]), 0);
-        EXPECT_GE(double(model3[0].populations[{i, mio::osecirts::InfectionState::InfectedSymptomsNaive}]), 0);
-        EXPECT_GE(double(model1[0].populations[{i, mio::osecirts::InfectionState::InfectedNoSymptomsPartialImmunity}]),
-                  0);
-        EXPECT_GE(double(model2[0].populations[{i, mio::osecirts::InfectionState::InfectedNoSymptomsPartialImmunity}]),
-                  0);
-        EXPECT_GE(double(model3[0].populations[{i, mio::osecirts::InfectionState::InfectedNoSymptomsPartialImmunity}]),
+    for (mio::AgeGroup i = 0; i < model1[0].property.parameters.get_num_groups(); i++) {
+        EXPECT_GE(double(model1[0].property.populations[{i, mio::osecirts::InfectionState::InfectedNoSymptomsNaive}]), 0);
+        EXPECT_GE(double(model2[0].property.populations[{i, mio::osecirts::InfectionState::InfectedNoSymptomsNaive}]), 0);
+        EXPECT_GE(double(model1[0].property.populations[{i, mio::osecirts::InfectionState::InfectedSymptomsNaive}]), 0);
+        EXPECT_GE(double(model2[0].property.populations[{i, mio::osecirts::InfectionState::InfectedSymptomsNaive}]), 0);
+        EXPECT_GE(double(model1[0].property.populations[{i, mio::osecirts::InfectionState::InfectedNoSymptomsPartialImmunity}]),
                   0);
-        EXPECT_GE(double(model1[0].populations[{i, mio::osecirts::InfectionState::InfectedSymptomsPartialImmunity}]),
+        EXPECT_GE(double(model2[0].property.populations[{i, mio::osecirts::InfectionState::InfectedNoSymptomsPartialImmunity}]),
                   0);
-        EXPECT_GE(double(model2[0].populations[{i, mio::osecirts::InfectionState::InfectedSymptomsPartialImmunity}]),
+        EXPECT_GE(double(model1[0].property.populations[{i, mio::osecirts::InfectionState::InfectedSymptomsPartialImmunity}]),
                   0);
-        EXPECT_GE(double(model3[0].populations[{i, mio::osecirts::InfectionState::InfectedSymptomsPartialImmunity}]),
+        EXPECT_GE(double(model2[0].property.populations[{i, mio::osecirts::InfectionState::InfectedSymptomsPartialImmunity}]),
                   0);
-        EXPECT_GE(double(model1[0].populations[{i, mio::osecirts::InfectionState::InfectedNoSymptomsImprovedImmunity}]),
+        EXPECT_GE(double(model1[0].property.populations[{i, mio::osecirts::InfectionState::InfectedNoSymptomsImprovedImmunity}]),
                   0);
-        EXPECT_GE(double(model2[0].populations[{i, mio::osecirts::InfectionState::InfectedNoSymptomsImprovedImmunity}]),
+        EXPECT_GE(double(model2[0].property.populations[{i, mio::osecirts::InfectionState::InfectedNoSymptomsImprovedImmunity}]),
                   0);
-        EXPECT_GE(double(model3[0].populations[{i, mio::osecirts::InfectionState::InfectedNoSymptomsImprovedImmunity}]),
+        EXPECT_GE(double(model1[0].property.populations[{i, mio::osecirts::InfectionState::InfectedSymptomsImprovedImmunity}]),
                   0);
-        EXPECT_GE(double(model1[0].populations[{i, mio::osecirts::InfectionState::InfectedSymptomsImprovedImmunity}]),
+        EXPECT_GE(double(model2[0].property.populations[{i, mio::osecirts::InfectionState::InfectedSymptomsImprovedImmunity}]),
                   0);
-        EXPECT_GE(double(model2[0].populations[{i, mio::osecirts::InfectionState::InfectedSymptomsImprovedImmunity}]),
+        EXPECT_GE(double(model1[0].property.populations[{i, mio::osecirts::InfectionState::TemporaryImmunePartialImmunity}]), 0);
+        EXPECT_GE(double(model2[0].property.populations[{i, mio::osecirts::InfectionState::TemporaryImmunePartialImmunity}]), 0);
+        EXPECT_GE(double(model1[0].property.populations[{i, mio::osecirts::InfectionState::TemporaryImmuneImprovedImmunity}]),
                   0);
-        EXPECT_GE(double(model3[0].populations[{i, mio::osecirts::InfectionState::InfectedSymptomsImprovedImmunity}]),
-                  0);
-        EXPECT_GE(double(model2[0].populations[{i, mio::osecirts::InfectionState::TemporaryImmunePartialImmunity}]), 0);
-        EXPECT_GE(double(model3[0].populations[{i, mio::osecirts::InfectionState::TemporaryImmuneImprovedImmunity}]),
+        EXPECT_GE(double(model2[0].property.populations[{i, mio::osecirts::InfectionState::TemporaryImmuneImprovedImmunity}]),
                   0);
 
         // currently dead and confirmed after commuting compartments are initialized as zero
-        EXPECT_EQ(double(model1[0].populations[{i, mio::osecirts::InfectionState::InfectedNoSymptomsNaiveConfirmed}]),
-                  0);
-        EXPECT_EQ(double(model2[0].populations[{i, mio::osecirts::InfectionState::InfectedNoSymptomsNaiveConfirmed}]),
+        EXPECT_EQ(double(model1[0].property.populations[{i, mio::osecirts::InfectionState::InfectedNoSymptomsNaiveConfirmed}]),
                   0);
-        EXPECT_EQ(double(model3[0].populations[{i, mio::osecirts::InfectionState::InfectedNoSymptomsNaiveConfirmed}]),
+        EXPECT_EQ(double(model2[0].property.populations[{i, mio::osecirts::InfectionState::InfectedNoSymptomsNaiveConfirmed}]),
                   0);
-        EXPECT_EQ(double(model1[0].populations[{i, mio::osecirts::InfectionState::InfectedSymptomsNaiveConfirmed}]), 0);
-        EXPECT_EQ(double(model2[0].populations[{i, mio::osecirts::InfectionState::InfectedSymptomsNaiveConfirmed}]), 0);
-        EXPECT_EQ(double(model3[0].populations[{i, mio::osecirts::InfectionState::InfectedSymptomsNaiveConfirmed}]), 0);
+        EXPECT_EQ(double(model1[0].property.populations[{i, mio::osecirts::InfectionState::InfectedSymptomsNaiveConfirmed}]), 0);
+        EXPECT_EQ(double(model2[0].property.populations[{i, mio::osecirts::InfectionState::InfectedSymptomsNaiveConfirmed}]), 0);
         EXPECT_EQ(
             double(
-                model1[0].populations[{i, mio::osecirts::InfectionState::InfectedNoSymptomsPartialImmunityConfirmed}]),
+                model1[0].property.populations[{i, mio::osecirts::InfectionState::InfectedNoSymptomsPartialImmunityConfirmed}]),
             0);
         EXPECT_EQ(
             double(
-                model2[0].populations[{i, mio::osecirts::InfectionState::InfectedNoSymptomsPartialImmunityConfirmed}]),
+                model2[0].property.populations[{i, mio::osecirts::InfectionState::InfectedNoSymptomsPartialImmunityConfirmed}]),
             0);
         EXPECT_EQ(
-            double(
-                model3[0].populations[{i, mio::osecirts::InfectionState::InfectedNoSymptomsPartialImmunityConfirmed}]),
+            double(model1[0].property.populations[{i, mio::osecirts::InfectionState::InfectedSymptomsPartialImmunityConfirmed}]),
             0);
         EXPECT_EQ(
-            double(model1[0].populations[{i, mio::osecirts::InfectionState::InfectedSymptomsPartialImmunityConfirmed}]),
-            0);
-        EXPECT_EQ(
-            double(model2[0].populations[{i, mio::osecirts::InfectionState::InfectedSymptomsPartialImmunityConfirmed}]),
-            0);
-        EXPECT_EQ(
-            double(model3[0].populations[{i, mio::osecirts::InfectionState::InfectedSymptomsPartialImmunityConfirmed}]),
-            0);
-        EXPECT_EQ(
-            double(
-                model1[0].populations[{i, mio::osecirts::InfectionState::InfectedNoSymptomsImprovedImmunityConfirmed}]),
-            0);
-        EXPECT_EQ(
-            double(
-                model2[0].populations[{i, mio::osecirts::InfectionState::InfectedNoSymptomsImprovedImmunityConfirmed}]),
+            double(model2[0].property.populations[{i, mio::osecirts::InfectionState::InfectedSymptomsPartialImmunityConfirmed}]),
             0);
         EXPECT_EQ(
             double(
-                model3[0].populations[{i, mio::osecirts::InfectionState::InfectedNoSymptomsImprovedImmunityConfirmed}]),
+                model1[0].property.populations[{i, mio::osecirts::InfectionState::InfectedNoSymptomsImprovedImmunityConfirmed}]),
             0);
         EXPECT_EQ(
             double(
-                model1[0].populations[{i, mio::osecirts::InfectionState::InfectedSymptomsImprovedImmunityConfirmed}]),
+                model2[0].property.populations[{i, mio::osecirts::InfectionState::InfectedNoSymptomsImprovedImmunityConfirmed}]),
             0);
         EXPECT_EQ(
             double(
-                model2[0].populations[{i, mio::osecirts::InfectionState::InfectedSymptomsImprovedImmunityConfirmed}]),
+                model1[0].property.populations[{i, mio::osecirts::InfectionState::InfectedSymptomsImprovedImmunityConfirmed}]),
             0);
         EXPECT_EQ(
             double(
-                model3[0].populations[{i, mio::osecirts::InfectionState::InfectedSymptomsImprovedImmunityConfirmed}]),
+                model2[0].property.populations[{i, mio::osecirts::InfectionState::InfectedSymptomsImprovedImmunityConfirmed}]),
             0);
-        EXPECT_EQ(double(model1[0].populations[{i, mio::osecirts::InfectionState::DeadNaive}]), 0);
-        EXPECT_EQ(double(model2[0].populations[{i, mio::osecirts::InfectionState::DeadNaive}]), 0);
-        EXPECT_EQ(double(model3[0].populations[{i, mio::osecirts::InfectionState::DeadNaive}]), 0);
-        EXPECT_EQ(double(model1[0].populations[{i, mio::osecirts::InfectionState::DeadPartialImmunity}]), 0);
-        EXPECT_EQ(double(model2[0].populations[{i, mio::osecirts::InfectionState::DeadPartialImmunity}]), 0);
-        EXPECT_EQ(double(model3[0].populations[{i, mio::osecirts::InfectionState::DeadPartialImmunity}]), 0);
-        EXPECT_EQ(double(model1[0].populations[{i, mio::osecirts::InfectionState::DeadImprovedImmunity}]), 0);
-        EXPECT_EQ(double(model2[0].populations[{i, mio::osecirts::InfectionState::DeadImprovedImmunity}]), 0);
-        EXPECT_EQ(double(model3[0].populations[{i, mio::osecirts::InfectionState::DeadImprovedImmunity}]), 0);
+        EXPECT_EQ(double(model1[0].property.populations[{i, mio::osecirts::InfectionState::DeadNaive}]), 0);
+        EXPECT_EQ(double(model2[0].property.populations[{i, mio::osecirts::InfectionState::DeadNaive}]), 0);
+        EXPECT_EQ(double(model1[0].property.populations[{i, mio::osecirts::InfectionState::DeadPartialImmunity}]), 0);
+        EXPECT_EQ(double(model2[0].property.populations[{i, mio::osecirts::InfectionState::DeadPartialImmunity}]), 0);
+        EXPECT_EQ(double(model1[0].property.populations[{i, mio::osecirts::InfectionState::DeadImprovedImmunity}]), 0);
+        EXPECT_EQ(double(model2[0].property.populations[{i, mio::osecirts::InfectionState::DeadImprovedImmunity}]), 0);
     }
 }
 
@@ -1003,20 +1016,18 @@ TEST(TestOdeSECIRTS, export_time_series_init)
     ASSERT_THAT(mio::create_directory(tmp_results_dir), IsSuccess());
 
     auto num_age_groups = 6; // Data to be read requires RKI confirmed cases data age groups
-    auto model          = make_model(num_age_groups);
+    std::vector<mio::Node<ModelT>> model{mio::Node<ModelT>(0, make_model(num_age_groups))};
 
     const std::vector<std::vector<double>> immunity_population = {{0.04, 0.04, 0.075, 0.08, 0.035, 0.01},
                                                                   {0.61, 0.61, 0.62, 0.62, 0.58, 0.41},
                                                                   {0.35, 0.35, 0.305, 0.3, 0.385, 0.58}};
 
+    std::string pydata_test_dir = TEST_DATA_DIR;
     // Test exporting time series
-    ASSERT_THAT(mio::osecirts::export_input_data_county_timeseries(
-                    std::vector<mio::osecirts::Model<double>>{model}, tmp_results_dir, {0}, {2020, 12, 01},
-                    std::vector<double>(size_t(num_age_groups), 1.0), 1.0, 2,
-                    mio::path_join(TEST_DATA_DIR, "county_divi_ma7.json"),
-                    mio::path_join(TEST_DATA_DIR, "cases_all_county_age_ma7.json"),
-                    mio::path_join(TEST_DATA_DIR, "county_current_population.json"), immunity_population,
-                    mio::path_join(TEST_DATA_DIR, "vacc_county_ageinf_ma7.json")),
+    ASSERT_THAT(mio::osecirts::export_input_data_timeseries(
+                    mio::make_range(model.begin(), model.end()), tmp_results_dir, {2020, 12, 01},
+                    std::vector<double>(size_t(num_age_groups), 1.0), 1.0, 2, immunity_population,
+                    mio::regions::de::EpidataFilenames::county(pydata_test_dir)),
                 IsSuccess());
 
     auto data_extrapolated = mio::read_result(mio::path_join(tmp_results_dir, "Results_rki.h5"));
@@ -1035,17 +1046,16 @@ TEST(TestOdeSECIRTS, export_time_series_init)
 TEST(TestOdeSECIRTS, model_initialization)
 {
     auto num_age_groups = 6; // Data to be read requires RKI confirmed cases data age groups
-    auto model          = make_model(num_age_groups);
-    // Vector assignment necessary as read_input_data_county changes model
-    auto model_vector = std::vector<mio::osecirts::Model<double>>{model};
+    std::vector<mio::Node<ModelT>> model_vector{mio::Node<ModelT>(0, make_model(num_age_groups))};
 
     const std::vector<std::vector<double>> immunity_population = {{0.04, 0.04, 0.075, 0.08, 0.035, 0.01},
                                                                   {0.61, 0.61, 0.62, 0.62, 0.58, 0.41},
                                                                   {0.35, 0.35, 0.305, 0.3, 0.385, 0.58}};
 
-    ASSERT_THAT(mio::osecirts::read_input_data_county(model_vector, {2020, 12, 01}, {0},
-                                                      std::vector<double>(size_t(num_age_groups), 1.0), 1.0,
-                                                      TEST_DATA_DIR, 2, immunity_population, false),
+    std::string pydata_test_dir = TEST_DATA_DIR;
+    ASSERT_THAT(mio::osecirts::read_input_data(mio::make_range(model_vector.begin(), model_vector.end()), {2020, 12, 01},
+                                               std::vector<double>(size_t(num_age_groups), 1.0), 1.0, 2, immunity_population, 
+                                               mio::regions::de::EpidataFilenames::county(pydata_test_dir)),
                 IsSuccess());
 
     // Values from data/export_time_series_init_osecirts.h5, for reading in comparison
@@ -1066,7 +1076,7 @@ TEST(TestOdeSECIRTS, model_initialization)
          0.146922, 4.75954, 3.5, 4, 3.58574e+06, 0, 0, 0, 0.530478, 0.155246)
             .finished();
 
-    ASSERT_THAT(print_wrap(model_vector[0].populations.array().cast<double>()),
+    ASSERT_THAT(print_wrap(model_vector[0].property.populations.array().cast<double>()),
                 MatrixNear(print_wrap(expected_values), 1e-5, 1e-5));
 }
 
@@ -1074,11 +1084,11 @@ TEST(TestOdeSECIRTS, set_vaccination_data_not_avail)
 {
     const auto num_age_groups = 2;
     const auto num_days       = 5;
-    mio::osecirts::Model<double> model(num_age_groups);
-    std::vector<mio::osecirts::Model<double>> model_vector = {model};
+    ModelT model(num_age_groups);
+    std::vector<mio::Node<ModelT>> model_vector{mio::Node<ModelT>(1001, model)};
 
     // Setup initial non-zero vaccination data
-    auto& params = model_vector[0].parameters;
+    auto& params = model_vector[0].property.parameters;
     params.get<mio::osecirts::DailyPartialVaccinations<double>>().resize(mio::SimulationDay(num_days + 1));
     params.get<mio::osecirts::DailyFullVaccinations<double>>().resize(mio::SimulationDay(num_days + 1));
     params.get<mio::osecirts::DailyBoosterVaccinations<double>>().resize(mio::SimulationDay(num_days + 1));
@@ -1097,11 +1107,11 @@ TEST(TestOdeSECIRTS, set_vaccination_data_not_avail)
 
     // Call set_vaccination_data with an unavailable date
     mio::Date unavailable_date(2019, 1, 1); // Date before vaccinations started
-    std::vector<int> region = {1001};
     std::string any_path    = "dummy_vacc_path.json";
 
     auto result =
-        mio::osecirts::details::set_vaccination_data(model_vector, any_path, unavailable_date, region, num_days);
+        mio::osecirts::details::set_vaccination_data<double>(mio::make_range(model_vector.begin(), model_vector.end()), 
+                                                             any_path, unavailable_date, num_days);
 
     ASSERT_THAT(result, IsSuccess());
 
@@ -1125,9 +1135,24 @@ TEST(TestOdeSECIRTS, set_vaccination_data_min_date_not_avail)
     // create model
     const auto num_age_groups = 1;
     const auto num_days       = 5;
-    mio::osecirts::Model<double> model(num_age_groups);
-    std::vector<mio::osecirts::Model<double>> model_vector = {model};
-    auto& params                                           = model_vector[0].parameters;
+    ModelT model(num_age_groups);
+    auto& params                                           = model.parameters;
+
+    params.template get<mio::osecirts::DaysUntilEffectivePartialVaccination<double>>()[mio::AgeGroup(0)]  = 1;
+    params.template get<mio::osecirts::DaysUntilEffectiveImprovedVaccination<double>>()[mio::AgeGroup(0)] = 1;
+    params.template get<mio::osecirts::DaysUntilEffectiveBoosterImmunity<double>>()[mio::AgeGroup(0)]     = 1;
+
+    // Set vaccination data to 0 for all models
+    params.template get<mio::osecirts::DailyPartialVaccinations<double>>().resize(mio::SimulationDay(num_days + 1));
+    params.template get<mio::osecirts::DailyFullVaccinations<double>>().resize(mio::SimulationDay(num_days + 1));
+    params.template get<mio::osecirts::DailyBoosterVaccinations<double>>().resize(mio::SimulationDay(num_days + 1));
+    for (auto d = mio::SimulationDay(0); d < mio::SimulationDay(num_days + 1); ++d) {
+        for (auto a = mio::AgeGroup(0); a < params.get_num_groups(); ++a) {
+            params.template get<mio::osecirts::DailyPartialVaccinations<double>>()[{a, d}] = 0.0;
+            params.template get<mio::osecirts::DailyFullVaccinations<double>>()[{a, d}]    = 0.0;
+            params.template get<mio::osecirts::DailyBoosterVaccinations<double>>()[{a, d}] = 0.0;
+        }
+    }
 
     // create simple vaccination data
     std::vector<mio::VaccinationDataEntry> vacc_data = {
@@ -1135,9 +1160,8 @@ TEST(TestOdeSECIRTS, set_vaccination_data_min_date_not_avail)
 
     // simulation date before vaccination data
     mio::Date earlier_date(2021, 1, 1);
-    std::vector<int> region = {0};
 
-    auto result = mio::osecirts::details::set_vaccination_data(model_vector, vacc_data, earlier_date, region, num_days);
+    auto result = mio::osecirts::details::set_vaccination_data<double>(model, vacc_data, earlier_date, num_days);
     ASSERT_THAT(result, IsSuccess());
 
     // check that vaccinations are set to zero for all days and age groups
@@ -1163,19 +1187,19 @@ TEST(TestOdeSECIRTS, parameter_percentiles)
 
     //build small graph
     auto model = make_model(5);
-    auto graph = mio::Graph<mio::osecirts::Model<double>, mio::MobilityParameters<double>>();
+    auto graph = mio::Graph<ModelT, mio::MobilityParameters<double>>();
     graph.add_node(0, model);
 
     //sample a few times
-    auto sampled_graphs = std::vector<mio::Graph<mio::osecirts::Model<double>, mio::MobilityParameters<double>>>();
+    auto sampled_graphs = std::vector<mio::Graph<ModelT, mio::MobilityParameters<double>>>();
     std::generate_n(std::back_inserter(sampled_graphs), 10, [&graph]() {
         return mio::osecirts::draw_sample(graph);
     });
 
     //extract nodes from graph
-    auto sampled_nodes = std::vector<std::vector<mio::osecirts::Model<double>>>();
+    auto sampled_nodes = std::vector<std::vector<ModelT>>();
     std::transform(sampled_graphs.begin(), sampled_graphs.end(), std::back_inserter(sampled_nodes), [](auto&& g) {
-        auto models = std::vector<mio::osecirts::Model<double>>();
+        auto models = std::vector<ModelT>();
         std::transform(g.nodes().begin(), g.nodes().end(), std::back_inserter(models), [](auto&& n) {
             return n.property;
         });
@@ -1189,7 +1213,7 @@ TEST(TestOdeSECIRTS, parameter_percentiles)
     auto p       = double(percentile_params.get<mio::osecirts::ReducTimeInfectedMild<double>>()[mio::AgeGroup(2)]);
     auto samples = std::vector<double>();
     std::transform(sampled_nodes.begin(), sampled_nodes.end(), std::back_inserter(samples),
-                   [](const std::vector<mio::osecirts::Model<double>>& nodes) {
+                   [](const std::vector<ModelT>& nodes) {
                        return nodes[0].parameters.get<mio::osecirts::ReducTimeInfectedMild<double>>()[mio::AgeGroup(2)];
                    });
 
@@ -1199,7 +1223,7 @@ TEST(TestOdeSECIRTS, parameter_percentiles)
     p       = double(percentile_params.get<mio::osecirts::TimeExposed<double>>()[mio::AgeGroup(2)]);
     samples = std::vector<double>();
     std::transform(sampled_nodes.begin(), sampled_nodes.end(), std::back_inserter(samples),
-                   [](const std::vector<mio::osecirts::Model<double>>& nodes) {
+                   [](const std::vector<ModelT>& nodes) {
                        return nodes[0].parameters.get<mio::osecirts::TimeExposed<double>>()[mio::AgeGroup(2)];
                    });
     std::nth_element(samples.begin(), samples.begin() + 6, samples.end());
@@ -1315,7 +1339,7 @@ TEST(TestOdeSECIRTS, test_commuters)
 
 TEST(TestOdeSECIRTS, check_constraints_parameters)
 {
-    auto model = mio::osecirts::Model<double>(1);
+    auto model = ModelT(1);
     ASSERT_EQ(model.parameters.check_constraints(), 0);
 
     mio::set_log_level(mio::LogLevel::off);
@@ -1454,7 +1478,7 @@ TEST(TestOdeSECIRTS, check_constraints_parameters)
 TEST(TestOdeSECIRTS, apply_constraints_parameters)
 {
     const double tol_times = 1e-1;
-    auto model             = mio::osecirts::Model<double>(1);
+    auto model             = ModelT(1);
     auto indx_agegroup     = mio::AgeGroup(0);
     EXPECT_EQ(model.parameters.apply_constraints(), 0);
 
@@ -1598,7 +1622,7 @@ TEST(TestOdeSECIRTS, apply_constraints_parameters)
 
 TEST(TestOdeSECIRTS, apply_variant_function)
 {
-    auto model = mio::osecirts::Model<double>(1);
+    auto model = ModelT(1);
     model.parameters.set<mio::osecirts::TransmissionProbabilityOnContact<double>>(0.2);
 
     model.parameters.set<mio::osecirts::StartDay<double>>(0);
diff --git a/cpp/tests/test_odesecirvvs.cpp b/cpp/tests/test_odesecirvvs.cpp
index 666e9132fb..b4c494e453 100755
--- a/cpp/tests/test_odesecirvvs.cpp
+++ b/cpp/tests/test_odesecirvvs.cpp
@@ -51,13 +51,15 @@
 #include <iterator>
 #include <limits>
 
+using ModelT = mio::osecirvvs::Model<double>;
+
 TEST(TestOdeSECIRVVS, simulateDefault)
 {
     double t0   = 0;
     double tmax = 1;
     double dt   = 0.1;
 
-    mio::osecirvvs::Model<double> model(1);
+    ModelT model(1);
     model.populations.set_total(10);
     model.populations.set_difference_from_total({(mio::AgeGroup)0, mio::osecirvvs::InfectionState::SusceptibleNaive},
                                                 10);
@@ -75,7 +77,7 @@ TEST(TestOdeSECIRVVS, reduceToSecirAndCompareWithPreviousRun)
     // double t0   = 0;
     // double tmax = 50;
 
-    mio::osecirvvs::Model<double> model(1);
+    ModelT model(1);
 
     double nb_total_t0 = 10000, nb_exp_t0 = 100, nb_inf_t0 = 50, nb_car_t0 = 50, nb_hosp_t0 = 20, nb_icu_t0 = 10,
            nb_rec_t0 = 10;
@@ -204,7 +206,7 @@ void array_assign_uniform_distribution(mio::CustomIndexArray<mio::UncertainValue
     }
 }
 
-void set_synthetic_population_data(mio::osecirvvs::Model<double>::Populations& populations,
+void set_synthetic_population_data(ModelT::Populations& populations,
                                    bool set_invalid_initial_value)
 {
     for (mio::AgeGroup i = 0; i < mio::get<mio::AgeGroup>(populations.size()); i++) {
@@ -266,7 +268,7 @@ void set_synthetic_population_data(mio::osecirvvs::Model<double>::Populations& p
     }
 }
 
-void set_demographic_parameters(mio::osecirvvs::Model<double>::ParameterSet& parameters, bool set_invalid_initial_value)
+void set_demographic_parameters(ModelT::ParameterSet& parameters, bool set_invalid_initial_value)
 {
     assign_uniform_distribution(parameters.get<mio::osecirvvs::ICUCapacity<double>>(), 20, 50,
                                 set_invalid_initial_value);
@@ -276,7 +278,7 @@ void set_demographic_parameters(mio::osecirvvs::Model<double>::ParameterSet& par
     parameters.get<mio::osecirvvs::DailyFullVaccinations<double>>().array().setConstant(3);
 }
 
-void set_contact_parameters(mio::osecirvvs::Model<double>::ParameterSet& parameters, bool set_invalid_initial_value)
+void set_contact_parameters(ModelT::ParameterSet& parameters, bool set_invalid_initial_value)
 {
     auto& contacts       = parameters.get<mio::osecirvvs::ContactPatterns<double>>();
     auto& contact_matrix = contacts.get_cont_freq_mat();
@@ -297,7 +299,7 @@ void set_contact_parameters(mio::osecirvvs::Model<double>::ParameterSet& paramet
     parameters.template get<mio::osecirvvs::DynamicNPIsImplementationDelay<double>>() = 7;
 }
 
-void set_covid_parameters(mio::osecirvvs::Model<double>::ParameterSet& params, bool set_invalid_initial_value)
+void set_covid_parameters(ModelT::ParameterSet& params, bool set_invalid_initial_value)
 {
     //times
     const double timeExposedMin            = 2.67;
@@ -415,10 +417,10 @@ void set_covid_parameters(mio::osecirvvs::Model<double>::ParameterSet& params, b
                                 set_invalid_initial_value);
 }
 
-mio::osecirvvs::Model<double> make_model(int num_age_groups, bool set_invalid_initial_value = false)
+ModelT make_model(int num_age_groups, bool set_invalid_initial_value = false)
 {
     assert(num_age_groups <= 6 && "Provide more values in functions above to test more age groups.");
-    mio::osecirvvs::Model<double> model(num_age_groups);
+    ModelT model(num_age_groups);
     set_covid_parameters(model.parameters, set_invalid_initial_value);
     set_synthetic_population_data(model.populations, set_invalid_initial_value);
     set_demographic_parameters(model.parameters, set_invalid_initial_value);
@@ -431,7 +433,7 @@ TEST(TestOdeSECIRVVS, draw_sample)
 {
     mio::log_thread_local_rng_seeds(mio::LogLevel::warn);
 
-    mio::Graph<mio::osecirvvs::Model<double>, mio::MobilityParameters<double>> graph;
+    mio::Graph<ModelT, mio::MobilityParameters<double>> graph;
 
     auto num_age_groups = 6;
     //create model with invalid initials so the test fails if no sampling is done
@@ -513,58 +515,53 @@ TEST(TestOdeSECIRVVS, checkPopulationConservation)
 TEST(TestOdeSECIRVVS, read_confirmed_cases)
 {
     auto num_age_groups = 6; //reading data requires RKI data age groups
-    auto model          = std::vector<mio::osecirvvs::Model<double>>({make_model(num_age_groups)});
-    std::vector<int> region{1002};
+    ModelT model = make_model(num_age_groups);
+    const int region = 1002;
     auto path = mio::path_join(TEST_DATA_DIR, "Germany/pydata/cases_all_county_age_ma7.json");
-    std::vector<std::vector<int>> t_Exposed(1);
-    std::vector<std::vector<int>> t_InfectedNoSymptoms(1);
-    std::vector<std::vector<int>> t_InfectedSymptoms(1);
-    std::vector<std::vector<int>> t_InfectedSevere(1);
-    std::vector<std::vector<int>> t_InfectedCritical(1);
-
-    std::vector<std::vector<double>> mu_C_R(1);
-    std::vector<std::vector<double>> mu_I_H(1);
-    std::vector<std::vector<double>> mu_H_U(1);
-
-    std::vector<std::vector<double>> num_InfectedSymptoms(1);
-    std::vector<std::vector<double>> num_death(1);
-    std::vector<std::vector<double>> num_rec(1);
-    std::vector<std::vector<double>> num_Exposed(1);
-    std::vector<std::vector<double>> num_InfectedNoSymptoms(1);
-    std::vector<std::vector<double>> num_InfectedSevere(1);
-    std::vector<std::vector<double>> num_icu(1);
-
-    num_InfectedSymptoms[0]   = std::vector<double>(num_age_groups, 0.0);
-    num_death[0]              = std::vector<double>(num_age_groups, 0.0);
-    num_rec[0]                = std::vector<double>(num_age_groups, 0.0);
-    num_Exposed[0]            = std::vector<double>(num_age_groups, 0.0);
-    num_InfectedNoSymptoms[0] = std::vector<double>(num_age_groups, 0.0);
-    num_InfectedSevere[0]     = std::vector<double>(num_age_groups, 0.0);
-    num_icu[0]                = std::vector<double>(num_age_groups, 0.0);
+    std::vector<int> t_Exposed;
+    std::vector<int> t_InfectedNoSymptoms;
+    std::vector<int> t_InfectedSymptoms;
+    std::vector<int> t_InfectedSevere;
+    std::vector<int> t_InfectedCritical;
+
+    std::vector<double> mu_C_R;
+    std::vector<double> mu_I_H;
+    std::vector<double> mu_H_U;
+
+    std::vector<double> num_InfectedSymptoms(num_age_groups, 0.0);
+    std::vector<double> num_death(num_age_groups, 0.0);
+    std::vector<double> num_rec(num_age_groups, 0.0);
+    std::vector<double> num_Exposed(num_age_groups, 0.0);
+    std::vector<double> num_InfectedNoSymptoms(num_age_groups, 0.0);
+    std::vector<double> num_InfectedSevere(num_age_groups, 0.0);
+    std::vector<double> num_icu(num_age_groups, 0.0);
+
     for (size_t group = 0; group < static_cast<size_t>(num_age_groups); group++) {
 
-        t_Exposed[0].push_back(static_cast<int>(
-            std::round(model[0].parameters.template get<mio::osecirvvs::TimeExposed<double>>()[(mio::AgeGroup)group])));
-        t_InfectedNoSymptoms[0].push_back(static_cast<int>(std::round(
-            model[0].parameters.template get<mio::osecirvvs::TimeInfectedNoSymptoms<double>>()[(mio::AgeGroup)group])));
-        t_InfectedSymptoms[0].push_back(static_cast<int>(std::round(
-            model[0].parameters.template get<mio::osecirvvs::TimeInfectedSymptoms<double>>()[(mio::AgeGroup)group])));
-        t_InfectedSevere[0].push_back(static_cast<int>(std::round(
-            model[0].parameters.template get<mio::osecirvvs::TimeInfectedSevere<double>>()[(mio::AgeGroup)group])));
-        t_InfectedCritical[0].push_back(static_cast<int>(std::round(
-            model[0].parameters.template get<mio::osecirvvs::TimeInfectedCritical<double>>()[(mio::AgeGroup)group])));
-
-        mu_C_R[0].push_back(model[0].parameters.template get<mio::osecirvvs::RecoveredPerInfectedNoSymptoms<double>>()[(
+        t_Exposed.push_back(static_cast<int>(
+            std::round(model.parameters.template get<mio::osecirvvs::TimeExposed<double>>()[(mio::AgeGroup)group])));
+        t_InfectedNoSymptoms.push_back(static_cast<int>(std::round(
+            model.parameters.template get<mio::osecirvvs::TimeInfectedNoSymptoms<double>>()[(mio::AgeGroup)group])));
+        t_InfectedSymptoms.push_back(static_cast<int>(std::round(
+            model.parameters.template get<mio::osecirvvs::TimeInfectedSymptoms<double>>()[(mio::AgeGroup)group])));
+        t_InfectedSevere.push_back(static_cast<int>(std::round(
+            model.parameters.template get<mio::osecirvvs::TimeInfectedSevere<double>>()[(mio::AgeGroup)group])));
+        t_InfectedCritical.push_back(static_cast<int>(std::round(
+            model.parameters.template get<mio::osecirvvs::TimeInfectedCritical<double>>()[(mio::AgeGroup)group])));
+
+        mu_C_R.push_back(model.parameters.template get<mio::osecirvvs::RecoveredPerInfectedNoSymptoms<double>>()[(
             mio::AgeGroup)group]);
-        mu_I_H[0].push_back(
-            model[0]
-                .parameters.template get<mio::osecirvvs::SeverePerInfectedSymptoms<double>>()[(mio::AgeGroup)group]);
-        mu_H_U[0].push_back(
-            model[0].parameters.template get<mio::osecirvvs::CriticalPerSevere<double>>()[(mio::AgeGroup)group]);
+        mu_I_H.push_back(
+            model.parameters.template get<mio::osecirvvs::SeverePerInfectedSymptoms<double>>()[(mio::AgeGroup)group]);
+        mu_H_U.push_back(
+            model.parameters.template get<mio::osecirvvs::CriticalPerSevere<double>>()[(mio::AgeGroup)group]);
     }
 
-    auto read = mio::osecirvvs::details::read_confirmed_cases_data(
-        path, region, {2020, 12, 01}, num_Exposed, num_InfectedNoSymptoms, num_InfectedSymptoms, num_InfectedSevere,
+    auto case_data =
+        mio::read_confirmed_cases_data(path).value();
+
+    auto read = mio::osecirvvs::details::compute_confirmed_cases_data(
+        case_data, region, {2020, 12, 01}, num_Exposed, num_InfectedNoSymptoms, num_InfectedSymptoms, num_InfectedSevere,
         num_icu, num_death, num_rec, t_Exposed, t_InfectedNoSymptoms, t_InfectedSymptoms, t_InfectedSevere,
         t_InfectedCritical, mu_C_R, mu_I_H, mu_H_U, std::vector<double>(size_t(num_age_groups), 1.0));
 
@@ -574,14 +571,17 @@ TEST(TestOdeSECIRVVS, read_confirmed_cases)
 TEST(TestOdeSECIRVVS, set_divi_data_invalid_dates)
 {
     mio::set_log_level(mio::LogLevel::off);
-    auto model = mio::osecirvvs::Model<double>(1);
+    auto model = ModelT(1);
     model.populations.array().setConstant(1);
-    auto model_vector = std::vector<mio::osecirvvs::Model<double>>{model};
+    auto model_vector = std::vector<mio::Node<ModelT>>{mio::Node<ModelT>(1001, model)};
 
     // Test with date before DIVI dataset was available.
-    EXPECT_THAT(mio::osecirvvs::details::set_divi_data(model_vector, "", {1001}, {2019, 12, 01}, 1.0), IsSuccess());
+    EXPECT_THAT(mio::osecirvvs::details::set_divi_data(mio::make_range(model_vector.begin(), model_vector.end()), 
+                                                    "", {2019, 12, 01}, 1.0), 
+                IsSuccess());
+
     // Assure that populations is the same as before.
-    EXPECT_THAT(print_wrap(model_vector[0].populations.array().cast<double>()),
+    EXPECT_THAT(print_wrap(model_vector[0].property.populations.array().cast<double>()),
                 MatrixNear(print_wrap(model.populations.array().cast<double>()), 1e-10, 1e-10));
 
     mio::set_log_level(mio::LogLevel::warn);
@@ -590,7 +590,7 @@ TEST(TestOdeSECIRVVS, set_divi_data_invalid_dates)
 TEST(TestOdeSECIRVVS, set_confirmed_cases_data_with_ICU)
 {
     const auto num_age_groups = 6;
-    auto model                = mio::osecirvvs::Model<double>(num_age_groups);
+    auto model                = ModelT(num_age_groups);
     model.populations.array().setConstant(1);
 
     // set params
@@ -620,10 +620,9 @@ TEST(TestOdeSECIRVVS, set_confirmed_cases_data_with_ICU)
     auto mid_day = case_data[(size_t)case_data.size() / 2].date;
 
     // calculate ICU values using set_confirmed_cases_data
-    auto model_vector       = std::vector<mio::osecirvvs::Model<double>>{model};
     auto scaling_factor_inf = std::vector<double>(size_t(model.parameters.get_num_groups()), 1.0);
     EXPECT_THAT(
-        mio::osecirvvs::details::set_confirmed_cases_data(model_vector, case_data, {1002}, mid_day, scaling_factor_inf),
+        mio::osecirvvs::details::set_confirmed_cases_data(model, case_data, 1002, mid_day, scaling_factor_inf),
         IsSuccess());
 
     // Since, TimeInfectedCritical is 1, the number of ICU cases is the difference of confirmed cases between two days, which is 1.
@@ -632,19 +631,19 @@ TEST(TestOdeSECIRVVS, set_confirmed_cases_data_with_ICU)
         const auto expected_value = (i == 2) ? 1.0 : 0.0;
 
         auto actual_value_naive =
-            model_vector[0]
+            model
                 .populations[{mio::AgeGroup(i), mio::osecirvvs::InfectionState::InfectedCriticalNaive}]
                 .value();
         EXPECT_NEAR(actual_value_naive, expected_value, 1e-10);
 
         auto actual_value_pi =
-            model_vector[0]
+            model
                 .populations[{mio::AgeGroup(i), mio::osecirvvs::InfectionState::InfectedCriticalPartialImmunity}]
                 .value();
         EXPECT_NEAR(actual_value_pi, expected_value, 1e-10);
 
         auto actual_value_ii =
-            model_vector[0]
+            model
                 .populations[{mio::AgeGroup(i), mio::osecirvvs::InfectionState::InfectedCriticalImprovedImmunity}]
                 .value();
         EXPECT_NEAR(actual_value_ii, expected_value, 1e-10);
@@ -654,25 +653,23 @@ TEST(TestOdeSECIRVVS, set_confirmed_cases_data_with_ICU)
 TEST(TestOdeSECIRVVS, read_data)
 {
     auto num_age_groups = 6; //reading data requires RKI data age groups
-    auto model1         = std::vector<mio::osecirvvs::Model<double>>({make_model(num_age_groups)});
-    auto model2         = std::vector<mio::osecirvvs::Model<double>>({make_model(num_age_groups)});
-    auto model3         = std::vector<mio::osecirvvs::Model<double>>({make_model(num_age_groups)});
-
-    const auto pydata_dir_District = mio::path_join(TEST_DATA_DIR, "District", "pydata");
-
-    auto read_result1 = mio::osecirvvs::read_input_data_county(model1, {2020, 12, 01}, {1002},
-                                                               std::vector<double>(size_t(num_age_groups), 1.0), 1.0,
-                                                               TEST_GERMANY_PYDATA_DIR, 30);
-
-    auto read_result2 = mio::osecirvvs::read_input_data(model2, {2020, 12, 01}, {1002},
-                                                        std::vector<double>(size_t(num_age_groups), 1.0), 1.0,
-                                                        TEST_GERMANY_PYDATA_DIR, 30);
-
-    auto read_result_district = mio::osecirvvs::read_input_data(
-        model3, {2020, 12, 01}, {1002}, std::vector<double>(size_t(num_age_groups), 1.0), 1.0, pydata_dir_District, 30);
+    auto model = make_model(num_age_groups);
+    auto model1 = std::vector<mio::Node<ModelT>>{mio::Node<ModelT>(1002, model)};
+    auto model2 = std::vector<mio::Node<ModelT>>{mio::Node<ModelT>(1002, model)};
+
+    auto pydata_germany_path = std::string(TEST_GERMANY_PYDATA_DIR);
+    auto read_result1 = 
+        mio::osecirvvs::read_input_data(mio::make_range(model1.begin(), model1.end()), {2020, 12, 01}, 
+                                     std::vector<double>(size_t(num_age_groups), 1.0), 1.0, 30,
+                                     mio::regions::de::EpidataFilenames::county(pydata_germany_path));
+    
+    auto pydata_district_path = mio::path_join(TEST_DATA_DIR, "District", "pydata");
+    auto read_result_district = 
+        mio::osecirvvs::read_input_data(mio::make_range(model2.begin(), model2.end()), {2020, 12, 01}, 
+                                     std::vector<double>(size_t(num_age_groups), 1.0), 1.0, 30,
+                                     mio::regions::de::EpidataFilenames::county(pydata_district_path));
 
     ASSERT_THAT(read_result1, IsSuccess());
-    ASSERT_THAT(read_result2, IsSuccess());
     ASSERT_THAT(read_result_district, IsSuccess());
 
     // values were generated by the tested function; can only check stability of the implementation, not correctness
@@ -692,122 +689,84 @@ TEST(TestOdeSECIRVVS, read_data)
          0.00442784, 4.67895, 0.00764769, 0.0729502, 2253.25, 0, 0, 0)
             .finished();
 
-    ASSERT_THAT(print_wrap(model1[0].populations.array().cast<double>()),
-                MatrixNear(print_wrap(model2[0].populations.array().cast<double>()), 1e-5, 1e-5));
+    ASSERT_THAT(print_wrap(model1[0].property.populations.array().cast<double>()),
+                MatrixNear(print_wrap(model2[0].property.populations.array().cast<double>()), 1e-5, 1e-5));
 
-    ASSERT_THAT(print_wrap(model1[0].populations.array().cast<double>()),
-                MatrixNear(print_wrap(model3[0].populations.array().cast<double>()), 1e-5, 1e-5));
-
-    ASSERT_THAT(print_wrap(model1[0].populations.array().cast<double>()),
-                MatrixNear(print_wrap(expected_values), 1e-5, 1e-5));
-    ASSERT_THAT(print_wrap(model2[0].populations.array().cast<double>()),
+    ASSERT_THAT(print_wrap(model1[0].property.populations.array().cast<double>()),
                 MatrixNear(print_wrap(expected_values), 1e-5, 1e-5));
-    ASSERT_THAT(print_wrap(model3[0].populations.array().cast<double>()),
+    ASSERT_THAT(print_wrap(model2[0].property.populations.array().cast<double>()),
                 MatrixNear(print_wrap(expected_values), 1e-5, 1e-5));
 
     // some more tests which are actually not necessary but can help if the previous tests fails or needs to get changed
-    for (mio::AgeGroup i = 0; i < model1[0].parameters.get_num_groups(); i++) {
-        EXPECT_GE(double(model1[0].populations[{i, mio::osecirvvs::InfectionState::InfectedNoSymptomsNaive}]), 0);
-        EXPECT_GE(double(model2[0].populations[{i, mio::osecirvvs::InfectionState::InfectedNoSymptomsNaive}]), 0);
-        EXPECT_GE(double(model3[0].populations[{i, mio::osecirvvs::InfectionState::InfectedNoSymptomsNaive}]), 0);
-        EXPECT_GE(double(model1[0].populations[{i, mio::osecirvvs::InfectionState::InfectedSymptomsNaive}]), 0);
-        EXPECT_GE(double(model2[0].populations[{i, mio::osecirvvs::InfectionState::InfectedSymptomsNaive}]), 0);
-        EXPECT_GE(double(model3[0].populations[{i, mio::osecirvvs::InfectionState::InfectedSymptomsNaive}]), 0);
-        EXPECT_GE(double(model1[0].populations[{i, mio::osecirvvs::InfectionState::InfectedNoSymptomsPartialImmunity}]),
-                  0);
-        EXPECT_GE(double(model2[0].populations[{i, mio::osecirvvs::InfectionState::InfectedNoSymptomsPartialImmunity}]),
+    for (mio::AgeGroup i = 0; i < model1[0].property.parameters.get_num_groups(); i++) {
+        EXPECT_GE(double(model1[0].property.populations[{i, mio::osecirvvs::InfectionState::InfectedNoSymptomsNaive}]), 0);
+        EXPECT_GE(double(model2[0].property.populations[{i, mio::osecirvvs::InfectionState::InfectedNoSymptomsNaive}]), 0);
+        EXPECT_GE(double(model1[0].property.populations[{i, mio::osecirvvs::InfectionState::InfectedSymptomsNaive}]), 0);
+        EXPECT_GE(double(model2[0].property.populations[{i, mio::osecirvvs::InfectionState::InfectedSymptomsNaive}]), 0);
+        EXPECT_GE(double(model1[0].property.populations[{i, mio::osecirvvs::InfectionState::InfectedNoSymptomsPartialImmunity}]),
                   0);
-        EXPECT_GE(double(model3[0].populations[{i, mio::osecirvvs::InfectionState::InfectedNoSymptomsPartialImmunity}]),
+        EXPECT_GE(double(model2[0].property.populations[{i, mio::osecirvvs::InfectionState::InfectedNoSymptomsPartialImmunity}]),
                   0);
-        EXPECT_GE(double(model1[0].populations[{i, mio::osecirvvs::InfectionState::InfectedSymptomsPartialImmunity}]),
+        EXPECT_GE(double(model1[0].property.populations[{i, mio::osecirvvs::InfectionState::InfectedSymptomsPartialImmunity}]),
                   0);
-        EXPECT_GE(double(model2[0].populations[{i, mio::osecirvvs::InfectionState::InfectedSymptomsPartialImmunity}]),
+        EXPECT_GE(double(model2[0].property.populations[{i, mio::osecirvvs::InfectionState::InfectedSymptomsPartialImmunity}]),
                   0);
-        EXPECT_GE(double(model3[0].populations[{i, mio::osecirvvs::InfectionState::InfectedSymptomsPartialImmunity}]),
-                  0);
-        EXPECT_GE(
-            double(model1[0].populations[{i, mio::osecirvvs::InfectionState::InfectedNoSymptomsImprovedImmunity}]), 0);
         EXPECT_GE(
-            double(model2[0].populations[{i, mio::osecirvvs::InfectionState::InfectedNoSymptomsImprovedImmunity}]), 0);
+            double(model1[0].property.populations[{i, mio::osecirvvs::InfectionState::InfectedNoSymptomsImprovedImmunity}]), 0);
         EXPECT_GE(
-            double(model3[0].populations[{i, mio::osecirvvs::InfectionState::InfectedNoSymptomsImprovedImmunity}]), 0);
-        EXPECT_GE(double(model1[0].populations[{i, mio::osecirvvs::InfectionState::InfectedSymptomsImprovedImmunity}]),
+            double(model2[0].property.populations[{i, mio::osecirvvs::InfectionState::InfectedNoSymptomsImprovedImmunity}]), 0);
+        EXPECT_GE(double(model1[0].property.populations[{i, mio::osecirvvs::InfectionState::InfectedSymptomsImprovedImmunity}]),
                   0);
-        EXPECT_GE(double(model2[0].populations[{i, mio::osecirvvs::InfectionState::InfectedSymptomsImprovedImmunity}]),
-                  0);
-        EXPECT_GE(double(model3[0].populations[{i, mio::osecirvvs::InfectionState::InfectedSymptomsImprovedImmunity}]),
+        EXPECT_GE(double(model2[0].property.populations[{i, mio::osecirvvs::InfectionState::InfectedSymptomsImprovedImmunity}]),
                   0);
 
         // currently dead and confirmed after commuting compartments are initialized as zero
-        EXPECT_EQ(double(model1[0].populations[{i, mio::osecirvvs::InfectionState::InfectedNoSymptomsNaiveConfirmed}]),
-                  0);
-        EXPECT_EQ(double(model2[0].populations[{i, mio::osecirvvs::InfectionState::InfectedNoSymptomsNaiveConfirmed}]),
+        EXPECT_EQ(double(model1[0].property.populations[{i, mio::osecirvvs::InfectionState::InfectedNoSymptomsNaiveConfirmed}]),
                   0);
-        EXPECT_EQ(double(model3[0].populations[{i, mio::osecirvvs::InfectionState::InfectedNoSymptomsNaiveConfirmed}]),
+        EXPECT_EQ(double(model2[0].property.populations[{i, mio::osecirvvs::InfectionState::InfectedNoSymptomsNaiveConfirmed}]),
                   0);
-        EXPECT_EQ(double(model1[0].populations[{i, mio::osecirvvs::InfectionState::InfectedSymptomsNaiveConfirmed}]),
+        EXPECT_EQ(double(model1[0].property.populations[{i, mio::osecirvvs::InfectionState::InfectedSymptomsNaiveConfirmed}]),
                   0);
-        EXPECT_EQ(double(model2[0].populations[{i, mio::osecirvvs::InfectionState::InfectedSymptomsNaiveConfirmed}]),
+        EXPECT_EQ(double(model2[0].property.populations[{i, mio::osecirvvs::InfectionState::InfectedSymptomsNaiveConfirmed}]),
                   0);
-        EXPECT_EQ(double(model3[0].populations[{i, mio::osecirvvs::InfectionState::InfectedSymptomsNaiveConfirmed}]),
-                  0);
-        EXPECT_EQ(
-            double(
-                model1[0].populations[{i, mio::osecirvvs::InfectionState::InfectedNoSymptomsPartialImmunityConfirmed}]),
-            0);
-        EXPECT_EQ(
-            double(
-                model2[0].populations[{i, mio::osecirvvs::InfectionState::InfectedNoSymptomsPartialImmunityConfirmed}]),
-            0);
         EXPECT_EQ(
             double(
-                model3[0].populations[{i, mio::osecirvvs::InfectionState::InfectedNoSymptomsPartialImmunityConfirmed}]),
+                model1[0].property.populations[{i, mio::osecirvvs::InfectionState::InfectedNoSymptomsPartialImmunityConfirmed}]),
             0);
         EXPECT_EQ(
             double(
-                model1[0].populations[{i, mio::osecirvvs::InfectionState::InfectedSymptomsPartialImmunityConfirmed}]),
+                model2[0].property.populations[{i, mio::osecirvvs::InfectionState::InfectedNoSymptomsPartialImmunityConfirmed}]),
             0);
         EXPECT_EQ(
             double(
-                model2[0].populations[{i, mio::osecirvvs::InfectionState::InfectedSymptomsPartialImmunityConfirmed}]),
+                model1[0].property.populations[{i, mio::osecirvvs::InfectionState::InfectedSymptomsPartialImmunityConfirmed}]),
             0);
         EXPECT_EQ(
             double(
-                model3[0].populations[{i, mio::osecirvvs::InfectionState::InfectedSymptomsPartialImmunityConfirmed}]),
+                model2[0].property.populations[{i, mio::osecirvvs::InfectionState::InfectedSymptomsPartialImmunityConfirmed}]),
             0);
         EXPECT_EQ(
             double(model1[0]
-                       .populations[{i, mio::osecirvvs::InfectionState::InfectedNoSymptomsImprovedImmunityConfirmed}]),
+                       .property.populations[{i, mio::osecirvvs::InfectionState::InfectedNoSymptomsImprovedImmunityConfirmed}]),
             0);
         EXPECT_EQ(
             double(model2[0]
-                       .populations[{i, mio::osecirvvs::InfectionState::InfectedNoSymptomsImprovedImmunityConfirmed}]),
-            0);
-        EXPECT_EQ(
-            double(model3[0]
-                       .populations[{i, mio::osecirvvs::InfectionState::InfectedNoSymptomsImprovedImmunityConfirmed}]),
-            0);
-        EXPECT_EQ(
-            double(
-                model1[0].populations[{i, mio::osecirvvs::InfectionState::InfectedSymptomsImprovedImmunityConfirmed}]),
+                       .property.populations[{i, mio::osecirvvs::InfectionState::InfectedNoSymptomsImprovedImmunityConfirmed}]),
             0);
         EXPECT_EQ(
             double(
-                model2[0].populations[{i, mio::osecirvvs::InfectionState::InfectedSymptomsImprovedImmunityConfirmed}]),
+                model1[0].property.populations[{i, mio::osecirvvs::InfectionState::InfectedSymptomsImprovedImmunityConfirmed}]),
             0);
         EXPECT_EQ(
             double(
-                model3[0].populations[{i, mio::osecirvvs::InfectionState::InfectedSymptomsImprovedImmunityConfirmed}]),
+                model2[0].property.populations[{i, mio::osecirvvs::InfectionState::InfectedSymptomsImprovedImmunityConfirmed}]),
             0);
-        EXPECT_EQ(double(model1[0].populations[{i, mio::osecirvvs::InfectionState::DeadNaive}]), 0);
-        EXPECT_EQ(double(model2[0].populations[{i, mio::osecirvvs::InfectionState::DeadNaive}]), 0);
-        EXPECT_EQ(double(model3[0].populations[{i, mio::osecirvvs::InfectionState::DeadNaive}]), 0);
-        EXPECT_EQ(double(model1[0].populations[{i, mio::osecirvvs::InfectionState::DeadPartialImmunity}]), 0);
-        EXPECT_EQ(double(model2[0].populations[{i, mio::osecirvvs::InfectionState::DeadPartialImmunity}]), 0);
-        EXPECT_EQ(double(model3[0].populations[{i, mio::osecirvvs::InfectionState::DeadPartialImmunity}]), 0);
-        EXPECT_EQ(double(model1[0].populations[{i, mio::osecirvvs::InfectionState::DeadImprovedImmunity}]), 0);
-        EXPECT_EQ(double(model2[0].populations[{i, mio::osecirvvs::InfectionState::DeadImprovedImmunity}]), 0);
-        EXPECT_EQ(double(model3[0].populations[{i, mio::osecirvvs::InfectionState::DeadImprovedImmunity}]), 0);
+        EXPECT_EQ(double(model1[0].property.populations[{i, mio::osecirvvs::InfectionState::DeadNaive}]), 0);
+        EXPECT_EQ(double(model2[0].property.populations[{i, mio::osecirvvs::InfectionState::DeadNaive}]), 0);
+        EXPECT_EQ(double(model1[0].property.populations[{i, mio::osecirvvs::InfectionState::DeadPartialImmunity}]), 0);
+        EXPECT_EQ(double(model2[0].property.populations[{i, mio::osecirvvs::InfectionState::DeadPartialImmunity}]), 0);
+        EXPECT_EQ(double(model1[0].property.populations[{i, mio::osecirvvs::InfectionState::DeadImprovedImmunity}]), 0);
+        EXPECT_EQ(double(model2[0].property.populations[{i, mio::osecirvvs::InfectionState::DeadImprovedImmunity}]), 0);
     }
 }
 
@@ -818,16 +777,14 @@ TEST(TestOdeSECIRVVS, export_time_series_init)
     ASSERT_THAT(mio::create_directory(tmp_results_dir), IsSuccess());
 
     auto num_age_groups = 6; // Data to be read requires RKI confirmed cases data age groups
-    auto model          = make_model(num_age_groups);
+    std::vector<mio::Node<ModelT>> model{mio::Node<ModelT>(0, make_model(num_age_groups))};
 
+    std::string pydata_test_dir = TEST_DATA_DIR;
     // Test exporting time series
-    ASSERT_THAT(mio::osecirvvs::export_input_data_county_timeseries(
-                    std::vector<mio::osecirvvs::Model<double>>{model}, tmp_results_dir, {0}, {2020, 12, 01},
-                    std::vector<double>(size_t(num_age_groups), 1.0), 1.0, 2,
-                    mio::path_join(TEST_DATA_DIR, "county_divi_ma7.json"),
-                    mio::path_join(TEST_DATA_DIR, "cases_all_county_age_ma7.json"),
-                    mio::path_join(TEST_DATA_DIR, "county_current_population.json"),
-                    mio::path_join(TEST_DATA_DIR, "vacc_county_ageinf_ma7.json")),
+    ASSERT_THAT(mio::osecirvvs::export_input_data_timeseries(
+                    mio::make_range(model.begin(), model.end()), tmp_results_dir, {2020, 12, 01}, 
+                    std::vector<double>(size_t(num_age_groups), 1.0), 1.0, 2, 
+                    mio::regions::de::EpidataFilenames::county(pydata_test_dir)),
                 IsSuccess());
 
     auto data_extrapolated = mio::read_result(mio::path_join(tmp_results_dir, "Results_rki.h5"));
@@ -850,22 +807,20 @@ TEST(TestOdeSECIRVVS, export_time_series_init_old_date)
     ASSERT_THAT(mio::create_directory(tmp_results_dir), IsSuccess());
 
     auto num_age_groups = 6; // Data to be read requires RKI confirmed cases data age groups
-    auto model          = make_model(num_age_groups);
+    std::vector<mio::Node<ModelT>> model{mio::Node<ModelT>(0, make_model(num_age_groups))};
 
     // set vaccinations to zero
-    model.parameters.get<mio::osecirvvs::DailyPartialVaccinations<double>>().array().setConstant(0);
-    model.parameters.get<mio::osecirvvs::DailyFullVaccinations<double>>().array().setConstant(0);
+    model[0].property.parameters.get<mio::osecirvvs::DailyPartialVaccinations<double>>().array().setConstant(0);
+    model[0].property.parameters.get<mio::osecirvvs::DailyFullVaccinations<double>>().array().setConstant(0);
     // set all compartments to zero
-    model.populations.array().setConstant(0.0);
+    model[0].property.populations.array().setConstant(0.0);
 
+    std::string pydata_test_dir = TEST_DATA_DIR;
     // Test exporting time series
-    ASSERT_THAT(mio::osecirvvs::export_input_data_county_timeseries(
-                    std::vector<mio::osecirvvs::Model<double>>{model}, tmp_results_dir, {0}, {20, 12, 01},
-                    std::vector<double>(size_t(num_age_groups), 1.0), 1.0, 0,
-                    mio::path_join(TEST_DATA_DIR, "county_divi_ma7.json"),
-                    mio::path_join(TEST_DATA_DIR, "cases_all_county_age_ma7.json"),
-                    mio::path_join(TEST_DATA_DIR, "county_current_population.json"),
-                    mio::path_join(TEST_DATA_DIR, "vacc_county_ageinf_ma7.json")),
+    ASSERT_THAT(mio::osecirvvs::export_input_data_timeseries(
+                    mio::make_range(model.begin(), model.end()), tmp_results_dir, {1000, 12, 01}, 
+                    std::vector<double>(size_t(num_age_groups), 1.0), 1.0, 2, 
+                    mio::regions::de::EpidataFilenames::county(pydata_test_dir)),
                 IsSuccess());
 
     auto data_extrapolated = mio::read_result(mio::path_join(tmp_results_dir, "Results_rki.h5"));
@@ -893,17 +848,15 @@ TEST(TestOdeSECIRVVS, export_time_series_init_old_date)
 TEST(TestOdeSECIRVVS, model_initialization)
 {
     auto num_age_groups = 6; // Data to be read requires RKI confirmed cases data age groups
-    auto model          = make_model(num_age_groups);
-    // Vector assignment necessary as read_input_data_county changes model
-    auto model_vector = std::vector<mio::osecirvvs::Model<double>>{model};
+    std::vector<mio::Node<ModelT>> model_vector{mio::Node<ModelT>(0, make_model(num_age_groups))};
 
-    ASSERT_THAT(mio::osecirvvs::read_input_data_county(model_vector, {2020, 12, 01}, {0},
-                                                       std::vector<double>(size_t(num_age_groups), 1.0), 1.0,
-                                                       TEST_GERMANY_PYDATA_DIR, 30, false),
+    std::string pydata_germany_dir = TEST_GERMANY_PYDATA_DIR;
+    ASSERT_THAT(mio::osecirvvs::read_input_data(mio::make_range(model_vector.begin(), model_vector.end()), {2020, 12, 01}, 
+                                                std::vector<double>(size_t(num_age_groups), 1.0), 1.0, 30,
+                                                mio::regions::de::EpidataFilenames::county(pydata_germany_dir)),
                 IsSuccess());
 
-    // Values from data/export_time_series_init_osecirvvs.h5, for reading in comparison
-    // operator for return of mio::read_result and model population needed.
+    // comparison for return of mio::read_result and model population needed.
     auto expected_values =
         (Eigen::ArrayXd(num_age_groups * Eigen::Index(mio::osecirvvs::InfectionState::Count)) << 3.46722e+06, 176.06,
          3.42799, 0.00017139, 0.00059842, 1.52355, 9.52168e-05, 0.000803518, 0, 0, 0, 7.28479, 0.000193296, 0.000551002,
@@ -921,7 +874,7 @@ TEST(TestOdeSECIRVVS, model_initialization)
          9.64602e-06, 4.75937, 1.66604e-05, 0.000158922, 2253.23, 0, 0, 0)
             .finished();
 
-    ASSERT_THAT(print_wrap(model_vector[0].populations.array().cast<double>()),
+    ASSERT_THAT(print_wrap(model_vector[0].property.populations.array().cast<double>()),
                 MatrixNear(print_wrap(expected_values), 1e-5, 1e-5));
 }
 
@@ -929,55 +882,18 @@ TEST(TestOdeSECIRVVS, model_initialization_old_date)
 {
     mio::set_log_level(mio::LogLevel::off);
     constexpr auto num_age_groups = 6; // Data to be read requires RKI confirmed cases data age groups
-    auto model                    = make_model(num_age_groups);
-    // set vaccinations to zero
-    model.parameters.get<mio::osecirvvs::DailyPartialVaccinations<double>>().array().setConstant(0);
-    model.parameters.get<mio::osecirvvs::DailyFullVaccinations<double>>().array().setConstant(0);
-    // set all compartments to zero
-    model.populations.array().setConstant(0.0);
-
-    auto model_vector = std::vector<mio::osecirvvs::Model<double>>{model};
-
-    ASSERT_THAT(mio::osecirvvs::read_input_data(model_vector, {100, 12, 01}, {0},
-                                                std::vector<double>(size_t(num_age_groups), 1.0), 1.0,
-                                                TEST_GERMANY_PYDATA_DIR, 0, false),
-                IsSuccess());
-
-    // if we enter an old date, the model only should be initialized with the population data.
-    // read population data
-    std::string path = mio::path_join(TEST_DATA_DIR, "county_current_population.json");
-    const std::vector<int> region{0};
-    auto population_data = mio::read_population_data(path, region).value();
-
-    // So, the expected values are the population data in the susceptible compartments and zeros in the other compartments.
-    for (auto i = 0; i < num_age_groups; i++) {
-        EXPECT_NEAR(
-            model_vector[0].populations.array().cast<double>()(i * Eigen::Index(mio::osecirvvs::InfectionState::Count)),
-            population_data[0][i], 1e-5);
-    }
-
-    // sum of all compartments should be equal to the population
-    EXPECT_NEAR(model_vector[0].populations.array().cast<double>().sum(),
-                std::accumulate(population_data[0].begin(), population_data[0].end(), 0.0), 1e-5);
-    mio::set_log_level(mio::LogLevel::warn);
-}
+    std::vector<mio::Node<ModelT>> model_vector{mio::Node<ModelT>(0, make_model(num_age_groups))};
 
-TEST(TestOdeSECIRVVS, model_initialization_old_date_county)
-{
-    mio::set_log_level(mio::LogLevel::off);
-    constexpr auto num_age_groups = 6; // Data to be read requires RKI confirmed cases data age groups
-    auto model                    = make_model(num_age_groups);
     // set vaccinations to zero
-    model.parameters.get<mio::osecirvvs::DailyPartialVaccinations<double>>().array().setConstant(0);
-    model.parameters.get<mio::osecirvvs::DailyFullVaccinations<double>>().array().setConstant(0);
+    model_vector[0].property.parameters.get<mio::osecirvvs::DailyPartialVaccinations<double>>().array().setConstant(0);
+    model_vector[0].property.parameters.get<mio::osecirvvs::DailyFullVaccinations<double>>().array().setConstant(0);
     // set all compartments to zero
-    model.populations.array().setConstant(0.0);
-
-    auto model_vector = std::vector<mio::osecirvvs::Model<double>>{model};
+    model_vector[0].property.populations.array().setConstant(0.0);
 
-    ASSERT_THAT(mio::osecirvvs::read_input_data_county(model_vector, {100, 12, 01}, {0},
-                                                       std::vector<double>(size_t(num_age_groups), 1.0), 1.0,
-                                                       TEST_GERMANY_PYDATA_DIR, 0, false),
+    std::string pydata_germany_dir = TEST_GERMANY_PYDATA_DIR;
+    ASSERT_THAT(mio::osecirvvs::read_input_data(mio::make_range(model_vector.begin(), model_vector.end()), {1000, 12, 01}, 
+                                                std::vector<double>(size_t(num_age_groups), 1.0), 1.0, 30,
+                                                mio::regions::de::EpidataFilenames::county(pydata_germany_dir)),
                 IsSuccess());
 
     // if we enter an old date, the model only should be initialized with the population data.
@@ -989,12 +905,12 @@ TEST(TestOdeSECIRVVS, model_initialization_old_date_county)
     // So, the expected values are the population data in the susceptible compartments and zeros in the other compartments.
     for (auto i = 0; i < num_age_groups; i++) {
         EXPECT_NEAR(
-            model_vector[0].populations.array().cast<double>()(i * Eigen::Index(mio::osecirvvs::InfectionState::Count)),
+            model_vector[0].property.populations.array().cast<double>()(i * Eigen::Index(mio::osecirvvs::InfectionState::Count)),
             population_data[0][i], 1e-5);
     }
 
     // sum of all compartments should be equal to the population
-    EXPECT_NEAR(model_vector[0].populations.array().cast<double>().sum(),
+    EXPECT_NEAR(model_vector[0].property.populations.array().cast<double>().sum(),
                 std::accumulate(population_data[0].begin(), population_data[0].end(), 0.0), 1e-5);
     mio::set_log_level(mio::LogLevel::warn);
 }
@@ -1013,29 +929,29 @@ TEST(TestOdeSECIRVVS, set_population_data_overflow_vacc)
     model.parameters
         .template get<mio::osecirvvs::DailyFullVaccinations<double>>()[{mio::AgeGroup(0), mio::SimulationDay(0)}] = 1e7;
 
-    auto model_vector = std::vector<mio::osecirvvs::Model<double>>{model};
+    std::vector<mio::Node<ModelT>> model_vector{mio::Node<ModelT>(1002, model)};
 
     std::string path_pop_data = mio::path_join(TEST_DATA_DIR, "county_current_population.json");
-    const std::vector<int> region{0};
+    const std::vector<int> region{1002};
     auto population_data = mio::read_population_data(path_pop_data, region).value();
 
     // we choose the date so that no case data is available
     ASSERT_THAT(mio::osecirvvs::details::set_population_data(
-                    model_vector, path_pop_data, mio::path_join(TEST_DATA_DIR, "cases_all_county_age_ma7.json"), {0},
-                    {1000, 12, 01}),
+                    mio::make_range(model_vector.begin(), model_vector.end()), path_pop_data, 
+                    mio::path_join(TEST_DATA_DIR, "cases_all_county_age_ma7.json"), {1000, 12, 01}),
                 IsSuccess());
 
     EXPECT_NEAR(
-        double(model_vector[0].populations[{mio::AgeGroup(0), mio::osecirvvs::InfectionState::SusceptibleNaive}]), 0.0,
+        double(model_vector[0].property.populations[{mio::AgeGroup(0), mio::osecirvvs::InfectionState::SusceptibleNaive}]), 0.0,
         1e-10);
-    EXPECT_NEAR(double(model_vector[0].populations[{mio::AgeGroup(0),
+    EXPECT_NEAR(double(model_vector[0].property.populations[{mio::AgeGroup(0),
                                                     mio::osecirvvs::InfectionState::SusceptiblePartialImmunity}]),
                 1, 1e-10);
-    EXPECT_NEAR(double(model_vector[0].populations[{mio::AgeGroup(0),
+    EXPECT_NEAR(double(model_vector[0].property.populations[{mio::AgeGroup(0),
                                                     mio::osecirvvs::InfectionState::SusceptibleImprovedImmunity}]),
                 population_data[0][0] - 1, 1e-10);
 
-    EXPECT_NEAR(model_vector[0].populations.get_group_total(mio::AgeGroup(0)), population_data[0][0], 1e-9);
+    EXPECT_NEAR(model_vector[0].property.populations.get_group_total(mio::AgeGroup(0)), population_data[0][0], 1e-9);
 }
 
 TEST(TestOdeSECIRVVS, set_population_data_no_data_avail)
@@ -1053,19 +969,17 @@ TEST(TestOdeSECIRVVS, set_population_data_no_data_avail)
     model.parameters
         .template get<mio::osecirvvs::DailyFullVaccinations<double>>()[{mio::AgeGroup(0), mio::SimulationDay(0)}] = 0;
 
-    auto model_vector = std::vector<mio::osecirvvs::Model<double>>{model};
+    std::vector<mio::Node<ModelT>> model_vector{mio::Node<ModelT>(200, model)};
 
     std::string path_pop_data = mio::path_join(TEST_DATA_DIR, "county_current_population.json");
-    const std::vector<int> region{0};
-    auto population_data = mio::read_population_data(path_pop_data, region).value();
 
     // we choose the date so that no case data is available
     ASSERT_THAT(mio::osecirvvs::details::set_population_data(
-                    model_vector, path_pop_data, mio::path_join(TEST_DATA_DIR, "cases_all_county_age_ma7.json"), {200},
-                    {1000, 12, 01}),
-                IsSuccess());
+                    mio::make_range(model_vector.begin(), model_vector.end()), path_pop_data, 
+                    mio::path_join(TEST_DATA_DIR, "cases_all_county_age_ma7.json"), {1000, 12, 01}),
+                IsFailure(mio::StatusCode::InvalidValue));
 
-    EXPECT_NEAR(model.populations.get_total(), 0.0, 1e-10);
+    EXPECT_NEAR(model_vector[0].property.populations.get_total(), 0.0, 1e-10);
 }
 
 TEST(TestOdeSECIRVVS, run_simulation)
@@ -1088,11 +1002,11 @@ TEST(TestOdeSECIRVVS, set_vaccination_data_not_avail)
 {
     const auto num_age_groups = 2;
     const auto num_days       = 5;
-    mio::osecirvvs::Model<double> model(num_age_groups);
-    std::vector<mio::osecirvvs::Model<double>> model_vector = {model};
+    ModelT model(num_age_groups);
+    std::vector<mio::Node<ModelT>> model_vector{mio::Node<ModelT>(1001, model)};
 
     // Setup initial non-zero vaccination data
-    auto& params = model_vector[0].parameters;
+    auto& params = model_vector[0].property.parameters;
     params.get<mio::osecirvvs::DailyPartialVaccinations<double>>().resize(mio::SimulationDay(num_days + 1));
     params.get<mio::osecirvvs::DailyFullVaccinations<double>>().resize(mio::SimulationDay(num_days + 1));
 
@@ -1108,10 +1022,10 @@ TEST(TestOdeSECIRVVS, set_vaccination_data_not_avail)
 
     // Call set_vaccination_data with an unavailable date
     mio::Date unavailable_date(2019, 1, 1); // Date before vaccinations started
-    std::vector<int> region = {1001};
     std::string any_path    = "dummy_vacc_path.json";
     auto result =
-        mio::osecirvvs::details::set_vaccination_data(model_vector, any_path, unavailable_date, region, num_days);
+        mio::osecirvvs::details::set_vaccination_data<double>(mio::make_range(model_vector.begin(), model_vector.end()), 
+                                                              any_path, unavailable_date, num_days);
 
     ASSERT_THAT(result, IsSuccess());
 
@@ -1133,23 +1047,32 @@ TEST(TestOdeSECIRVVS, set_vaccination_data_min_date_not_avail)
     // create model
     const auto num_age_groups = 1;
     const auto num_days       = 5;
-    mio::osecirvvs::Model<double> model(num_age_groups);
-    std::vector<mio::osecirvvs::Model<double>> model_vector = {model};
-    auto& params                                            = model_vector[0].parameters;
+    ModelT model(num_age_groups);
+
+    auto& params                                            = model.parameters;
     params.template get<mio::osecirvvs::DaysUntilEffectivePartialImmunity<double>>()[mio::AgeGroup(0)]  = 1;
     params.template get<mio::osecirvvs::DaysUntilEffectiveImprovedImmunity<double>>()[mio::AgeGroup(0)] = 1;
     params.template get<mio::osecirvvs::VaccinationGap<double>>()[mio::AgeGroup(0)]                     = 1;
 
+    // Set vaccination data to 0 for all models
+    params.template get<mio::osecirvvs::DailyPartialVaccinations<double>>().resize(mio::SimulationDay(num_days + 1));
+    params.template get<mio::osecirvvs::DailyFullVaccinations<double>>().resize(mio::SimulationDay(num_days + 1));
+    for (auto d = mio::SimulationDay(0); d < mio::SimulationDay(num_days + 1); ++d) {
+        for (auto a = mio::AgeGroup(0); a < params.get_num_groups(); ++a) {
+            params.template get<mio::osecirvvs::DailyPartialVaccinations<double>>()[{a, d}] = 0.0;
+            params.template get<mio::osecirvvs::DailyFullVaccinations<double>>()[{a, d}]    = 0.0;
+        }
+    }
+
     // create simple vaccination data
     std::vector<mio::VaccinationDataEntry> vacc_data = {
         mio::VaccinationDataEntry{10.0, 5.0, 0.0, 0.0, mio::Date(2021, 1, 15), mio::AgeGroup(0), {}, {}, {}}};
 
     // simulation date before vaccination data
     mio::Date earlier_date(2021, 1, 1);
-    std::vector<int> region = {0};
 
     auto result =
-        mio::osecirvvs::details::set_vaccination_data(model_vector, vacc_data, earlier_date, region, num_days);
+        mio::osecirvvs::details::set_vaccination_data<double>(model, vacc_data, earlier_date, num_days);
     ASSERT_THAT(result, IsSuccess());
 
     // check that vaccinations are set to zero for all days and age groups
@@ -1173,19 +1096,19 @@ TEST(TestOdeSECIRVVS, parameter_percentiles)
 
     //build small graph
     auto model = make_model(5);
-    auto graph = mio::Graph<mio::osecirvvs::Model<double>, mio::MobilityParameters<double>>();
+    auto graph = mio::Graph<ModelT, mio::MobilityParameters<double>>();
     graph.add_node(0, model);
 
     //sample a few times
-    auto sampled_graphs = std::vector<mio::Graph<mio::osecirvvs::Model<double>, mio::MobilityParameters<double>>>();
+    auto sampled_graphs = std::vector<mio::Graph<ModelT, mio::MobilityParameters<double>>>();
     std::generate_n(std::back_inserter(sampled_graphs), 10, [&graph]() {
         return mio::osecirvvs::draw_sample(graph);
     });
 
     //extract nodes from graph
-    auto sampled_nodes = std::vector<std::vector<mio::osecirvvs::Model<double>>>();
+    auto sampled_nodes = std::vector<std::vector<ModelT>>();
     std::transform(sampled_graphs.begin(), sampled_graphs.end(), std::back_inserter(sampled_nodes), [](auto&& g) {
-        auto models = std::vector<mio::osecirvvs::Model<double>>();
+        auto models = std::vector<ModelT>();
         std::transform(g.nodes().begin(), g.nodes().end(), std::back_inserter(models), [](auto&& n) {
             return n.property;
         });
@@ -1200,7 +1123,7 @@ TEST(TestOdeSECIRVVS, parameter_percentiles)
     auto samples = std::vector<double>();
     std::transform(
         sampled_nodes.begin(), sampled_nodes.end(), std::back_inserter(samples),
-        [](const std::vector<mio::osecirvvs::Model<double>>& nodes) {
+        [](const std::vector<ModelT>& nodes) {
             return nodes[0].parameters.get<mio::osecirvvs::ReducTimeInfectedMild<double>>()[mio::AgeGroup(2)];
         });
 
@@ -1210,7 +1133,7 @@ TEST(TestOdeSECIRVVS, parameter_percentiles)
     p       = double(percentile_params.get<mio::osecirvvs::TimeExposed<double>>()[mio::AgeGroup(2)]);
     samples = std::vector<double>();
     std::transform(sampled_nodes.begin(), sampled_nodes.end(), std::back_inserter(samples),
-                   [](const std::vector<mio::osecirvvs::Model<double>>& nodes) {
+                   [](const std::vector<ModelT>& nodes) {
                        return nodes[0].parameters.get<mio::osecirvvs::TimeExposed<double>>()[mio::AgeGroup(2)];
                    });
     std::nth_element(samples.begin(), samples.begin() + 6, samples.end());
@@ -1334,7 +1257,7 @@ TEST(TestOdeSECIRVVS, test_commuters)
 TEST(TestOdeSECIRVVS, population_zero_no_nan)
 {
     // initialize simple model with total population 0
-    mio::osecirvvs::Model<double> model(1);
+    ModelT model(1);
     model.populations.set_total(0.0);
 
     // call the get_flows function
@@ -1351,7 +1274,7 @@ TEST(TestOdeSECIRVVS, population_zero_no_nan)
 
 TEST(TestOdeSECIRVVS, check_constraints_parameters)
 {
-    auto model = mio::osecirvvs::Model<double>(1);
+    auto model = ModelT(1);
     EXPECT_EQ(model.parameters.check_constraints(), 0);
 
     mio::set_log_level(mio::LogLevel::off);
@@ -1479,7 +1402,7 @@ TEST(TestOdeSECIRVVS, check_constraints_parameters)
 TEST(TestOdeSECIRVVS, apply_constraints_parameters)
 {
     const double tol_times = 1e-1;
-    auto model             = mio::osecirvvs::Model<double>(1);
+    auto model             = ModelT(1);
     auto indx_agegroup     = mio::AgeGroup(0);
     EXPECT_EQ(model.parameters.apply_constraints(), 0);
 
@@ -1612,7 +1535,7 @@ TEST(TestOdeSECIRVVS, apply_constraints_parameters)
 
 TEST(TestOdeSECIRVVS, apply_variant_function)
 {
-    auto model = mio::osecirvvs::Model<double>(1);
+    auto model = ModelT(1);
     model.parameters.set<mio::osecirvvs::TransmissionProbabilityOnContact<double>>(0.2);
 
     model.parameters.set<mio::osecirvvs::StartDay<double>>(0);
diff --git a/cpp/tests/test_save_parameters.cpp b/cpp/tests/test_save_parameters.cpp
index acdd16c546..ba98b13546 100644
--- a/cpp/tests/test_save_parameters.cpp
+++ b/cpp/tests/test_save_parameters.cpp
@@ -625,269 +625,6 @@ TEST(TestSaveParameters, json_graphs_write_read_compare)
     }
 }
 
-TEST(TestSaveParameters, ReadPopulationDataRKIAges)
-{
-    std::vector<mio::osecir::Model<double>> model(1, {6});
-    model[0].apply_constraints();
-    std::vector<double> scaling_factor_inf(6, 1.0);
-    double scaling_factor_icu = 1.0;
-    mio::Date date(2020, 12, 10);
-
-    std::string path = TEST_DATA_DIR;
-
-    for (auto group = mio::AgeGroup(0); group < mio::AgeGroup(6); group++) {
-        model[0].parameters.get<mio::osecir::RecoveredPerInfectedNoSymptoms<double>>()[group] =
-            0.1 * ((size_t)group + 1);
-        model[0].parameters.get<mio::osecir::SeverePerInfectedSymptoms<double>>()[group] = 0.11 * ((size_t)group + 1);
-        model[0].parameters.get<mio::osecir::CriticalPerSevere<double>>()[group]         = 0.12 * ((size_t)group + 1);
-    }
-
-    auto read_result = mio::osecir::read_input_data_germany(model, date, scaling_factor_inf, scaling_factor_icu, path);
-    ASSERT_THAT(print_wrap(read_result), IsSuccess());
-
-    std::vector<double> sus   = {3444279.56, 7666866.07, 18801541.51, 29518513.27, 16321649.37, 6072737.25};
-    std::vector<double> exp   = {433.015, 1771.61, 8856.33, 14757.62, 7222.86, 6626.07};
-    std::vector<double> car   = {434.444, 1772.14, 8724.49, 14386.90, 6995.14, 6307.14};
-    std::vector<double> inf   = {375.429, 1393.43, 6007.14, 8438.71, 3377.57, 2421.57};
-    std::vector<double> hosp  = {39.9614, 303.191, 1934.84, 3621.2, 1793.39, 1557.03};
-    std::vector<double> icu   = {47.6813, 190.725, 429.132, 762.901, 1192.03, 1716.53};
-    std::vector<double> rec   = {23135.58, 77174.17, 389914.56, 472390.20, 164273.23, 72435.15};
-    std::vector<double> death = {2, 4, 48, 1137.86, 8174.14, 18528.9};
-
-    for (size_t i = 0; i < 6; i++) {
-        EXPECT_NEAR((model[0].populations[{mio::AgeGroup(i), mio::osecir::InfectionState::Susceptible}]), sus[i], 1e-1);
-        EXPECT_NEAR((model[0].populations[{mio::AgeGroup(i), mio::osecir::InfectionState::Exposed}]), exp[i], 1e-1);
-        EXPECT_NEAR((model[0].populations[{mio::AgeGroup(i), mio::osecir::InfectionState::InfectedNoSymptoms}]), car[i],
-                    1e-1);
-        EXPECT_NEAR((model[0].populations[{mio::AgeGroup(i), mio::osecir::InfectionState::InfectedSymptoms}]), inf[i],
-                    1e-1);
-        EXPECT_NEAR((model[0].populations[{mio::AgeGroup(i), mio::osecir::InfectionState::InfectedSevere}]), hosp[i],
-                    1e-1);
-        EXPECT_NEAR((model[0].populations[{mio::AgeGroup(i), mio::osecir::InfectionState::InfectedCritical}]), icu[i],
-                    1e-1);
-        EXPECT_NEAR((model[0].populations[{mio::AgeGroup(i), mio::osecir::InfectionState::Recovered}]), rec[i], 1e-1);
-        EXPECT_NEAR((model[0].populations[{mio::AgeGroup(i), mio::osecir::InfectionState::Dead}]), death[i], 1e-1);
-    }
-
-    EXPECT_NEAR(model[0].populations.get_total(), 83166695, 1e-6);
-}
-
-TEST(TestSaveParameters, ReadPopulationDataStateAllAges)
-{
-    std::vector<mio::osecir::Model<double>> model(1, {6});
-    model[0].apply_constraints();
-    std::vector<double> scaling_factor_inf(6, 1.0);
-    double scaling_factor_icu = 1.0;
-    mio::Date date(2020, 12, 10);
-
-    std::vector<int> state = {1};
-
-    std::string path = TEST_DATA_DIR;
-
-    for (auto group = mio::AgeGroup(0); group < mio::AgeGroup(6); group++) {
-        model[0].parameters.get<mio::osecir::RecoveredPerInfectedNoSymptoms<double>>()[group] =
-            0.1 * ((size_t)group + 1);
-        model[0].parameters.get<mio::osecir::SeverePerInfectedSymptoms<double>>()[group] = 0.11 * ((size_t)group + 1);
-        model[0].parameters.get<mio::osecir::CriticalPerSevere<double>>()[group]         = 0.12 * ((size_t)group + 1);
-    }
-
-    auto read_result =
-        mio::osecir::read_input_data_state(model, date, state, scaling_factor_inf, scaling_factor_icu, path);
-    ASSERT_THAT(print_wrap(read_result), IsSuccess());
-
-    std::vector<double> sus   = {116699.98, 283936.40, 622935.07, 1042390.64, 606622.90, 213104.13};
-    std::vector<double> exp   = {8.57143, 30.5357, 149.388, 228.809, 87.1429, 99.2857};
-    std::vector<double> car   = {7.77778, 26.0714, 143.061, 217.143, 84.8571, 92.1429};
-    std::vector<double> inf   = {7.00000, 18.7143, 97.7143, 122.000, 40.8571, 36.1429};
-    std::vector<double> hosp  = {0.707143, 3.92857, 30.6429, 50.5371, 20.35, 19.9886};
-    std::vector<double> icu   = {0.274725, 1.0989, 2.47253, 4.3956, 6.86813, 9.89011};
-    std::vector<double> rec   = {385.36, 1192.59, 5328.66, 6331.18, 2109.05, 778.53};
-    std::vector<double> death = {0, 0, 0, 16.2857, 99.5714, 198.286};
-
-    for (size_t i = 0; i < 6; i++) {
-        EXPECT_NEAR((model[0].populations[{mio::AgeGroup(i), mio::osecir::InfectionState::Susceptible}]), sus[i], 1e-1);
-        EXPECT_NEAR((model[0].populations[{mio::AgeGroup(i), mio::osecir::InfectionState::Exposed}]), exp[i], 1e-1);
-        EXPECT_NEAR((model[0].populations[{mio::AgeGroup(i), mio::osecir::InfectionState::InfectedNoSymptoms}]), car[i],
-                    1e-1);
-        EXPECT_NEAR((model[0].populations[{mio::AgeGroup(i), mio::osecir::InfectionState::InfectedSymptoms}]), inf[i],
-                    1e-1);
-        EXPECT_NEAR((model[0].populations[{mio::AgeGroup(i), mio::osecir::InfectionState::InfectedSevere}]), hosp[i],
-                    1e-1);
-        EXPECT_NEAR((model[0].populations[{mio::AgeGroup(i), mio::osecir::InfectionState::InfectedCritical}]), icu[i],
-                    1e-1);
-        EXPECT_NEAR((model[0].populations[{mio::AgeGroup(i), mio::osecir::InfectionState::Recovered}]), rec[i], 1e-1);
-        EXPECT_NEAR((model[0].populations[{mio::AgeGroup(i), mio::osecir::InfectionState::Dead}]), death[i], 1e-1);
-    }
-
-    EXPECT_NEAR(model[0].populations.get_total(), 2903777, 1e-6);
-}
-
-TEST(TestSaveParameters, ReadPopulationDataCountyAllAges)
-{
-
-    std::vector<mio::osecir::Model<double>> model1(1, {6});
-    model1[0].apply_constraints();
-    std::vector<mio::osecir::Model<double>> model2(1, {6});
-    model2[0].apply_constraints();
-    std::vector<mio::osecir::Model<double>> model3(1, {6});
-    model3[0].apply_constraints();
-    std::vector<double> scaling_factor_inf(6, 1.0);
-    double scaling_factor_icu = 1.0;
-    mio::Date date(2020, 12, 10);
-
-    std::vector<int> county = {1002};
-
-    for (auto group = mio::AgeGroup(0); group < mio::AgeGroup(6); group++) {
-        model1[0].parameters.get<mio::osecir::RecoveredPerInfectedNoSymptoms<double>>()[group] =
-            0.1 * ((size_t)group + 1);
-        model1[0].parameters.get<mio::osecir::SeverePerInfectedSymptoms<double>>()[group] = 0.11 * ((size_t)group + 1);
-        model1[0].parameters.get<mio::osecir::CriticalPerSevere<double>>()[group]         = 0.12 * ((size_t)group + 1);
-        model2[0].parameters.get<mio::osecir::RecoveredPerInfectedNoSymptoms<double>>()[group] =
-            0.1 * ((size_t)group + 1);
-        model2[0].parameters.get<mio::osecir::SeverePerInfectedSymptoms<double>>()[group] = 0.11 * ((size_t)group + 1);
-        model2[0].parameters.get<mio::osecir::CriticalPerSevere<double>>()[group]         = 0.12 * ((size_t)group + 1);
-        model3[0].parameters.get<mio::osecir::RecoveredPerInfectedNoSymptoms<double>>()[group] =
-            0.1 * ((size_t)group + 1);
-        model3[0].parameters.get<mio::osecir::SeverePerInfectedSymptoms<double>>()[group] = 0.11 * ((size_t)group + 1);
-        model3[0].parameters.get<mio::osecir::CriticalPerSevere<double>>()[group]         = 0.12 * ((size_t)group + 1);
-    }
-
-    auto read_result1 = mio::osecir::read_input_data_county(model1, date, county, scaling_factor_inf,
-                                                            scaling_factor_icu, TEST_GERMANY_PYDATA_DIR);
-    auto read_result2 = mio::osecir::read_input_data(model2, date, county, scaling_factor_inf, scaling_factor_icu,
-                                                     TEST_GERMANY_PYDATA_DIR);
-    auto read_result_district = mio::osecir::read_input_data(
-        model3, date, county, scaling_factor_inf, scaling_factor_icu, mio::path_join(TEST_DATA_DIR, "District/pydata"));
-    ASSERT_THAT(print_wrap(read_result1), IsSuccess());
-    ASSERT_THAT(print_wrap(read_result2), IsSuccess());
-    ASSERT_THAT(print_wrap(read_result_district), IsSuccess());
-
-    std::vector<double> sus   = {10284.66, 19086.96, 73805.00, 82515.81, 43739.68, 15633.34};
-    std::vector<double> exp   = {0.571429, 4.82143, 20.8163, 22.1429, 4.57143, 4.64286};
-    std::vector<double> car   = {0.557143, 4.46429, 22.0408, 20.7143, 4.28571, 4.64286};
-    std::vector<double> inf   = {0.42857, 3.285714, 15.2857, 13.0000, 2.42857, 2.00000};
-    std::vector<double> hosp  = {0.0942857, 0.691429, 4.90286, 5.34286, 1.41429, 2.45143};
-    std::vector<double> icu   = {0.0769231, 0.307692, 0.692308, 1.23077, 1.92308, 2.76923};
-    std::vector<double> rec   = {34.47, 104.47, 618.26, 552.43, 165.83, 54.93};
-    std::vector<double> death = {0, 0, 0, 0, 10, 14.4286};
-
-    for (size_t i = 0; i < 6; i++) {
-        EXPECT_NEAR((model1[0].populations[{mio::AgeGroup(i), mio::osecir::InfectionState::Susceptible}]), sus[i],
-                    1e-1);
-        EXPECT_NEAR((model2[0].populations[{mio::AgeGroup(i), mio::osecir::InfectionState::Susceptible}]), sus[i],
-                    1e-1);
-        EXPECT_NEAR((model3[0].populations[{mio::AgeGroup(i), mio::osecir::InfectionState::Susceptible}]), sus[i],
-                    1e-1);
-        EXPECT_NEAR((model1[0].populations[{mio::AgeGroup(i), mio::osecir::InfectionState::Exposed}]), exp[i], 1e-1);
-        EXPECT_NEAR((model2[0].populations[{mio::AgeGroup(i), mio::osecir::InfectionState::Exposed}]), exp[i], 1e-1);
-        EXPECT_NEAR((model3[0].populations[{mio::AgeGroup(i), mio::osecir::InfectionState::Exposed}]), exp[i], 1e-1);
-        EXPECT_NEAR((model1[0].populations[{mio::AgeGroup(i), mio::osecir::InfectionState::InfectedNoSymptoms}]),
-                    car[i], 1e-1);
-        EXPECT_NEAR((model2[0].populations[{mio::AgeGroup(i), mio::osecir::InfectionState::InfectedNoSymptoms}]),
-                    car[i], 1e-1);
-        EXPECT_NEAR((model3[0].populations[{mio::AgeGroup(i), mio::osecir::InfectionState::InfectedNoSymptoms}]),
-                    car[i], 1e-1);
-        EXPECT_NEAR((model1[0].populations[{mio::AgeGroup(i), mio::osecir::InfectionState::InfectedSymptoms}]), inf[i],
-                    1e-1);
-        EXPECT_NEAR((model2[0].populations[{mio::AgeGroup(i), mio::osecir::InfectionState::InfectedSymptoms}]), inf[i],
-                    1e-1);
-        EXPECT_NEAR((model3[0].populations[{mio::AgeGroup(i), mio::osecir::InfectionState::InfectedSymptoms}]), inf[i],
-                    1e-1);
-        EXPECT_NEAR((model1[0].populations[{mio::AgeGroup(i), mio::osecir::InfectionState::InfectedSevere}]), hosp[i],
-                    1e-1);
-        EXPECT_NEAR((model2[0].populations[{mio::AgeGroup(i), mio::osecir::InfectionState::InfectedSevere}]), hosp[i],
-                    1e-1);
-        EXPECT_NEAR((model3[0].populations[{mio::AgeGroup(i), mio::osecir::InfectionState::InfectedSevere}]), hosp[i],
-                    1e-1);
-        EXPECT_NEAR((model1[0].populations[{mio::AgeGroup(i), mio::osecir::InfectionState::InfectedCritical}]), icu[i],
-                    1e-1);
-        EXPECT_NEAR((model2[0].populations[{mio::AgeGroup(i), mio::osecir::InfectionState::InfectedCritical}]), icu[i],
-                    1e-1);
-        EXPECT_NEAR((model3[0].populations[{mio::AgeGroup(i), mio::osecir::InfectionState::InfectedCritical}]), icu[i],
-                    1e-1);
-        EXPECT_NEAR((model1[0].populations[{mio::AgeGroup(i), mio::osecir::InfectionState::Recovered}]), rec[i], 1e-1);
-        EXPECT_NEAR((model2[0].populations[{mio::AgeGroup(i), mio::osecir::InfectionState::Recovered}]), rec[i], 1e-1);
-        EXPECT_NEAR((model3[0].populations[{mio::AgeGroup(i), mio::osecir::InfectionState::Recovered}]), rec[i], 1e-1);
-        EXPECT_NEAR((model1[0].populations[{mio::AgeGroup(i), mio::osecir::InfectionState::Dead}]), death[i], 1e-1);
-        EXPECT_NEAR((model2[0].populations[{mio::AgeGroup(i), mio::osecir::InfectionState::Dead}]), death[i], 1e-1);
-        EXPECT_NEAR((model3[0].populations[{mio::AgeGroup(i), mio::osecir::InfectionState::Dead}]), death[i], 1e-1);
-    }
-
-    EXPECT_NEAR(model1[0].populations.get_total(), 246793, 1e-6);
-    EXPECT_NEAR(model2[0].populations.get_total(), 246793, 1e-6);
-    EXPECT_NEAR(model3[0].populations.get_total(), 246793, 1e-6);
-}
-
-TEST(TestSaveParameters, ExtrapolateRKI)
-{
-    std::vector<mio::osecir::Model<double>> model{mio::osecir::Model<double>(6)};
-
-    model[0].apply_constraints();
-    std::vector<double> scaling_factor_inf(6, 1.0);
-    double scaling_factor_icu = 1.0;
-    mio::Date date(2020, 12, 10);
-
-    std::vector<int> county = {1002};
-
-    for (auto group = mio::AgeGroup(0); group < mio::AgeGroup(6); group++) {
-        model[0].parameters.get<mio::osecir::RecoveredPerInfectedNoSymptoms<double>>()[group] =
-            0.1 * ((size_t)group + 1);
-        model[0].parameters.get<mio::osecir::SeverePerInfectedSymptoms<double>>()[group] = 0.11 * ((size_t)group + 1);
-        model[0].parameters.get<mio::osecir::CriticalPerSevere<double>>()[group]         = 0.12 * ((size_t)group + 1);
-    }
-
-    TempFileRegister file_register;
-    auto results_dir = file_register.get_unique_path("ExtrapolateRKI-%%%%-%%%%");
-    boost::filesystem::create_directory(results_dir);
-    auto extrapolate_result = mio::osecir::export_input_data_county_timeseries(
-        model, results_dir, county, date, scaling_factor_inf, scaling_factor_icu, 1,
-        mio::path_join(TEST_DATA_DIR, "county_divi_ma7.json"),
-        mio::path_join(TEST_DATA_DIR, "cases_all_county_age_ma7.json"),
-        mio::path_join(TEST_DATA_DIR, "county_current_population.json"));
-    ASSERT_THAT(print_wrap(extrapolate_result), IsSuccess());
-
-    auto read_result = mio::read_result(mio::path_join(results_dir, "Results_rki.h5"));
-    ASSERT_THAT(print_wrap(read_result), IsSuccess());
-    auto& file_results = read_result.value();
-    auto results       = file_results[0].get_groups();
-
-    std::vector<double> sus   = {10284.66, 19086.96, 73805.00, 82515.81, 43739.68, 15633.34};
-    std::vector<double> exp   = {0.571429, 4.82143, 20.8163, 22.1429, 4.57143, 4.64286};
-    std::vector<double> car   = {0.557143, 4.46429, 22.0408, 20.7143, 4.28571, 4.64286};
-    std::vector<double> inf   = {0.428571, 3.28571, 15.2857, 13.0000, 2.42857, 2.00000};
-    std::vector<double> hosp  = {0.0942857, 0.691429, 4.90286, 5.34286, 1.41429, 2.45143};
-    std::vector<double> icu   = {0.0769231, 0.307692, 0.692308, 1.23077, 1.92308, 2.76923};
-    std::vector<double> rec   = {34.47, 104.47, 618.26, 552.43, 165.83, 54.93};
-    std::vector<double> death = {0, 0, 0, 0, 10, 14.4286};
-
-    for (size_t i = 0; i < 6; i++) {
-        EXPECT_NEAR(results[0]((size_t)mio::osecir::InfectionState::Susceptible +
-                               (size_t)mio::osecir::InfectionState::Count * i),
-                    sus[i], 1e-1);
-        EXPECT_NEAR(
-            results[0]((size_t)mio::osecir::InfectionState::Exposed + (size_t)mio::osecir::InfectionState::Count * i),
-            exp[i], 1e-1);
-        EXPECT_NEAR(results[0]((size_t)mio::osecir::InfectionState::InfectedNoSymptoms +
-                               (size_t)mio::osecir::InfectionState::Count * i),
-                    car[i], 1e-1);
-        EXPECT_NEAR(results[0]((size_t)mio::osecir::InfectionState::InfectedSymptoms +
-                               (size_t)mio::osecir::InfectionState::Count * i),
-                    inf[i], 1e-1);
-        EXPECT_NEAR(results[0]((size_t)mio::osecir::InfectionState::InfectedSevere +
-                               (size_t)mio::osecir::InfectionState::Count * i),
-                    hosp[i], 1e-1);
-        EXPECT_NEAR(results[0]((size_t)mio::osecir::InfectionState::InfectedCritical +
-                               (size_t)mio::osecir::InfectionState::Count * i),
-                    icu[i], 1e-1);
-        EXPECT_NEAR(
-            results[0]((size_t)mio::osecir::InfectionState::Recovered + (size_t)mio::osecir::InfectionState::Count * i),
-            rec[i], 1e-1);
-        EXPECT_NEAR(
-            results[0]((size_t)mio::osecir::InfectionState::Dead + (size_t)mio::osecir::InfectionState::Count * i),
-            death[i], 1e-1);
-    }
-}
-
 TEST(TestSaveParameters, json_write_read_parameters_secirvvs)
 {
     mio::osecirvvs::Model<double> model(2);
diff --git a/pycode/examples/simulation/2020_npis_sarscov2_wildtype_germany.py b/pycode/examples/simulation/2020_npis_sarscov2_wildtype_germany.py
index ec122bd9e8..10ed55eee6 100644
--- a/pycode/examples/simulation/2020_npis_sarscov2_wildtype_germany.py
+++ b/pycode/examples/simulation/2020_npis_sarscov2_wildtype_germany.py
@@ -507,7 +507,7 @@ def senior_awareness(t, min, max):
             dynamic_npis.base_value = 100000
             dynamic_npis.set_threshold(200.0, local_npis)
 
-            # school holidays(holiday periods are set per node, see set_nodes)
+            # school holidays(holiday periods are set per node)
             contacts.school_holiday_damping = damping_helper(
                 0, 1.0, 1.0, lvl_holidays, typ_school, [loc_school])
             contacts.dampings = dampings
@@ -534,17 +534,12 @@ def get_graph(self, end_date):
             data_dir_Germany, "mobility", "commuter_mobility_2022.txt")
         pydata_dir = os.path.join(data_dir_Germany, "pydata")
 
-        path_population_data = os.path.join(pydata_dir,
-                                            "county_current_population.json")
-
-        mio.osecir.set_nodes(
+        graph = mio.osecir.create_graph_german_county(
             model.parameters,
             mio.Date(self.start_date.year,
                      self.start_date.month, self.start_date.day),
-            mio.Date(end_date.year,
-                     end_date.month, end_date.day), pydata_dir,
-            path_population_data, True, graph, scaling_factor_infected,
-            scaling_factor_icu, tnt_capacity_factor, 0, False)
+            mio.Date(end_date.year, end_date.month, end_date.day),
+            scaling_factor_infected, scaling_factor_icu, pydata_dir, tnt_capacity_factor)
 
         mio.osecir.set_edges(
             mobility_data_file, graph, len(Location))
diff --git a/pycode/examples/simulation/2021_vaccination_sarscov2_delta_germany.py b/pycode/examples/simulation/2021_vaccination_sarscov2_delta_germany.py
index 8a8766d252..0b5fd8c5b8 100644
--- a/pycode/examples/simulation/2021_vaccination_sarscov2_delta_germany.py
+++ b/pycode/examples/simulation/2021_vaccination_sarscov2_delta_germany.py
@@ -594,7 +594,7 @@ def senior_awareness(t, min, max):
 
         params.DynamicNPIsInfectedSymptoms = dynamic_npis
 
-        # school holidays (holiday periods are set per node, see set_nodes)
+        # school holidays (holiday periods are set per node)
         school_holiday_value = mio.UncertainValue(0.5 * (1.0 + 1.0))
         school_holiday_value.set_distribution(
             mio.ParameterDistributionUniform(1.0, 1.0))
@@ -624,15 +624,9 @@ def get_graph(self, end_date):
             data_dir_Germany, "mobility", "commuter_mobility_2022.txt")
         pydata_dir = os.path.join(data_dir_Germany, "pydata")
 
-        path_population_data = os.path.join(pydata_dir,
-                                            "county_current_population.json")
-
-        osecirvvs.set_nodes(
-            model.parameters,
-            self.start_date,
-            end_date, pydata_dir,
-            path_population_data, True, graph, scaling_factor_infected,
-            scaling_factor_icu, tnt_capacity_factor, end_date - self.start_date, False)
+        graph = mio.osecir.create_graph_german_county(
+            model.parameters, self.start_date, end_date,
+            scaling_factor_infected, scaling_factor_icu, pydata_dir, tnt_capacity_factor)
 
         osecirvvs.set_edges(
             mobility_data_file, graph, len(Location))
diff --git a/pycode/memilio-simulation/memilio/simulation/bindings/models/osecir.cpp b/pycode/memilio-simulation/memilio/simulation/bindings/models/osecir.cpp
index c20271fdc3..85eadff28e 100644
--- a/pycode/memilio-simulation/memilio/simulation/bindings/models/osecir.cpp
+++ b/pycode/memilio-simulation/memilio/simulation/bindings/models/osecir.cpp
@@ -166,26 +166,6 @@ PYBIND11_MODULE(_simulation_osecir, m)
         },
         py::arg("model"));
 
-    // These functions are in general not model dependent, only with the current config
-    m.def(
-        "set_nodes",
-        [](const mio::osecir::Parameters<double>& params, mio::Date start_date, mio::Date end_date,
-           const std::string& data_dir, const std::string& population_data_path, bool is_node_for_county,
-           mio::Graph<mio::osecir::Model<double>, mio::MobilityParameters<double>>& params_graph,
-           const std::vector<double>& scaling_factor_inf, double scaling_factor_icu, double tnt_capacity_factor,
-           int num_days = 0, bool export_time_series = false) {
-            auto result = mio::set_nodes<
-                double, // FP
-                mio::osecir::TestAndTraceCapacity<double>, mio::osecir::ContactPatterns<double>,
-                mio::osecir::Model<double>, mio::MobilityParameters<double>, mio::osecir::Parameters<double>,
-                decltype(mio::osecir::read_input_data_county<mio::osecir::Model<double>>), decltype(mio::get_node_ids)>(
-                params, start_date, end_date, data_dir, population_data_path, is_node_for_county, params_graph,
-                mio::osecir::read_input_data_county<mio::osecir::Model<double>>, mio::get_node_ids, scaling_factor_inf,
-                scaling_factor_icu, tnt_capacity_factor, num_days, export_time_series);
-            return pymio::check_and_throw(result);
-        },
-        py::return_value_policy::move);
-
     pymio::iterable_enum<ContactLocation>(m, "ContactLocation")
         .value("Home", ContactLocation::Home)
         .value("School", ContactLocation::School)
@@ -219,15 +199,45 @@ PYBIND11_MODULE(_simulation_osecir, m)
     pymio::bind_write_graph<mio::osecir::Model<double>>(m);
     pymio::bind_read_graph<mio::osecir::Model<double>>(m);
     m.def(
-        "read_input_data_county",
-        [](std::vector<mio::osecir::Model<double>>& model, mio::Date date, const std::vector<int>& county,
-           const std::vector<double>& scaling_factor_inf, double scaling_factor_icu, const std::string& dir,
-           int num_days = 0, bool export_time_series = false) {
-            auto result = mio::osecir::read_input_data_county<mio::osecir::Model<double>>(
-                model, date, county, scaling_factor_inf, scaling_factor_icu, dir, num_days, export_time_series);
+        "read_input_data_german_county",
+        [](mio::Graph<mio::osecir::Model<double>, mio::MobilityParameters<double>>& params_graph, mio::Date start_date,
+           const std::vector<double>& scaling_factor_inf, double scaling_factor_icu, std::string& pydata_path) {
+
+            auto result = mio::osecir::read_input_data(params_graph.nodes(), start_date, scaling_factor_inf, scaling_factor_icu, 
+                            mio::regions::de::EpidataFilenames::county(pydata_path));
             return pymio::check_and_throw(result);
         },
+        "Reads compartments for german counties at a specified date from data files.",
+        py::arg("params_graph"), py::arg("start_date"), py::arg("scaling_factor_inf"), py::arg("scaling_factor_icu"),
+        py::arg("pydata_path"), py::return_value_policy::move);
+
+    m.def(
+        "create_graph_german_county",
+        [](const mio::osecir::Parameters<double>& params, mio::Date start_date, mio::Date end_date,
+           const std::vector<double>& scaling_factor_inf, double scaling_factor_icu, std::string& pydata_path,
+           double tnt_capacity_factor) {
+
+            auto result_node_id = mio::get_node_ids(mio::path_join(pydata_path, "county_current_population.json"), true);
+            auto node_ids = pymio::check_and_throw(result_node_id);
+
+            mio::Graph<mio::osecir::Model<double>, mio::MobilityParameters<double>> params_graph(node_ids, 
+                mio::osecir::Model<double>::Populations({params.get_num_groups(), mio::osecir::InfectionState::Count}), params);
+            auto result_read_input_data = mio::osecir::read_input_data(params_graph.nodes(), start_date, 
+                                            scaling_factor_inf, scaling_factor_icu, 
+                                            mio::regions::de::EpidataFilenames::county(pydata_path));
+            pymio::check_and_throw(result_read_input_data);
+            
+            mio::set_test_and_trace_capacity<mio::osecir::Model<double>, mio::osecir::TestAndTraceCapacity<double>>(params_graph.nodes(), tnt_capacity_factor);
+            mio::set_german_holidays<double, mio::osecir::Model<double>, mio::osecir::ContactPatterns<double>>(params_graph.nodes(), start_date, end_date);
+            mio::set_uncertainty_on_population<mio::osecir::Model<double>>(params_graph.nodes());
+            
+            return params_graph;
+        },
+        "Creates graph of germany with compartments for geographic units at a specified date from data files.",
+        py::arg("params"), py::arg("start_date"), py::arg("end_date"), py::arg("scaling_factor_inf"), 
+        py::arg("scaling_factor_icu"), py::arg("pydata_path"), py::arg("tnt_capacity_factor"),
         py::return_value_policy::move);
+        
 #endif // MEMILIO_HAS_JSONCPP
 
     m.def("interpolate_simulation_result",
diff --git a/pycode/memilio-simulation/memilio/simulation/bindings/models/osecirvvs.cpp b/pycode/memilio-simulation/memilio/simulation/bindings/models/osecirvvs.cpp
index d6e5bc62a3..a4e821f25e 100755
--- a/pycode/memilio-simulation/memilio/simulation/bindings/models/osecirvvs.cpp
+++ b/pycode/memilio-simulation/memilio/simulation/bindings/models/osecirvvs.cpp
@@ -205,28 +205,6 @@ PYBIND11_MODULE(_simulation_osecirvvs, m)
         },
         py::arg("model"));
 
-    // These functions are in general not secirvvs dependent, only with the current config
-    m.def(
-        "set_nodes",
-        [](const mio::osecirvvs::Parameters<double>& params, mio::Date start_date, mio::Date end_date,
-           const std::string& data_dir, const std::string& population_data_path, bool is_node_for_county,
-           mio::Graph<mio::osecirvvs::Model<double>, mio::MobilityParameters<double>>& params_graph,
-           const std::vector<double>& scaling_factor_inf, double scaling_factor_icu, double tnt_capacity_factor,
-           int num_days = 0, bool export_time_series = false) {
-            auto result =
-                mio::set_nodes<double, // FP
-                               mio::osecirvvs::TestAndTraceCapacity<double>, mio::osecirvvs::ContactPatterns<double>,
-                               mio::osecirvvs::Model<double>, mio::MobilityParameters<double>,
-                               mio::osecirvvs::Parameters<double>,
-                               decltype(mio::osecirvvs::read_input_data_county<mio::osecirvvs::Model<ScalarType>>),
-                               decltype(mio::get_node_ids)>(
-                    params, start_date, end_date, data_dir, population_data_path, is_node_for_county, params_graph,
-                    mio::osecirvvs::read_input_data_county<mio::osecirvvs::Model<double>>, mio::get_node_ids,
-                    scaling_factor_inf, scaling_factor_icu, tnt_capacity_factor, num_days, export_time_series);
-            return pymio::check_and_throw(result);
-        },
-        py::return_value_policy::move);
-
     m.def(
         "set_edges",
         [](const std::string& mobility_data_file,
@@ -263,14 +241,44 @@ PYBIND11_MODULE(_simulation_osecirvvs, m)
     pymio::bind_write_graph<mio::osecirvvs::Model<double>>(m);
     pymio::bind_read_graph<mio::osecirvvs::Model<double>>(m);
     m.def(
-        "read_input_data_county",
-        [](std::vector<mio::osecirvvs::Model<double>>& model, mio::Date date, const std::vector<int>& county,
-           const std::vector<double>& scaling_factor_inf, double scaling_factor_icu, const std::string& dir,
-           int num_days = 0, bool export_time_series = false) {
-            auto result = mio::osecirvvs::read_input_data_county<mio::osecirvvs::Model<double>>(
-                model, date, county, scaling_factor_inf, scaling_factor_icu, dir, num_days, export_time_series);
+        "read_input_data_german_county",
+        [](mio::Graph<mio::osecirvvs::Model<double>, mio::MobilityParameters<double>>& params_graph, 
+           mio::Date start_date, const std::vector<double>& scaling_factor_inf, double scaling_factor_icu, 
+           int num_days, std::string& pydata_path) {
+
+            auto result = mio::osecirvvs::read_input_data(params_graph.nodes(), start_date, scaling_factor_inf, scaling_factor_icu, 
+                            num_days, mio::regions::de::EpidataFilenames::county(pydata_path));
             return pymio::check_and_throw(result);
         },
+        "Reads compartments for german counties at a specified date from data files.",
+        py::arg("params_graph"), py::arg("start_date"), py::arg("scaling_factor_inf"), py::arg("scaling_factor_icu"),
+        py::arg("num_days"), py::arg("pydata_path"), py::return_value_policy::move);
+
+    m.def(
+        "create_graph_german_county",
+        [](const mio::osecirvvs::Parameters<double>& params, mio::Date start_date, mio::Date end_date,
+           const std::vector<double>& scaling_factor_inf, double scaling_factor_icu, int num_days, 
+           std::string& pydata_path, double tnt_capacity_factor) {
+
+            auto result_node_id = mio::get_node_ids(mio::path_join(pydata_path, "county_current_population.json"), true);
+            auto node_ids = pymio::check_and_throw(result_node_id);
+
+            mio::Graph<mio::osecirvvs::Model<double>, mio::MobilityParameters<double>> params_graph(node_ids, 
+                mio::osecirvvs::Model<double>::Populations({params.get_num_groups(), mio::osecirvvs::InfectionState::Count}), params);
+            auto result_read_input_data = mio::osecirvvs::read_input_data(params_graph.nodes(), start_date, 
+                                            scaling_factor_inf, scaling_factor_icu, num_days,
+                                            mio::regions::de::EpidataFilenames::county(pydata_path));
+            pymio::check_and_throw(result_read_input_data);
+            
+            mio::set_test_and_trace_capacity<mio::osecirvvs::Model<double>, mio::osecirvvs::TestAndTraceCapacity<double>>(params_graph.nodes(), tnt_capacity_factor);
+            mio::set_german_holidays<double, mio::osecirvvs::Model<double>, mio::osecirvvs::ContactPatterns<double>>(params_graph.nodes(), start_date, end_date);
+            mio::set_uncertainty_on_population<mio::osecirvvs::Model<double>>(params_graph.nodes());
+            
+            return params_graph;
+        },
+        "Creates graph of germany with compartments for geographic units at a specified date from data files.",
+        py::arg("params"), py::arg("start_date"), py::arg("end_date"), py::arg("scaling_factor_inf"), 
+        py::arg("scaling_factor_icu"), py::arg("num_days"), py::arg("pydata_path"), py::arg("tnt_capacity_factor"),
         py::return_value_policy::move);
 #endif // MEMILIO_HAS_JSONCPP
 
diff --git a/pycode/memilio-simulation/memilio/simulation/bindings/simulation.cpp b/pycode/memilio-simulation/memilio/simulation/bindings/simulation.cpp
index a98012dce8..27ab2733d8 100755
--- a/pycode/memilio-simulation/memilio/simulation/bindings/simulation.cpp
+++ b/pycode/memilio-simulation/memilio/simulation/bindings/simulation.cpp
@@ -59,7 +59,7 @@ PYBIND11_MODULE(_simulation, m)
     pymio::bind_CustomIndexArray<mio::UncertainValue<double>, mio::AgeGroup>(m, "AgeGroupArray");
     pymio::bind_class<mio::AgeGroup, pymio::EnablePickling::Required, mio::Index<mio::AgeGroup>>(m, "AgeGroup")
         .def(py::init<size_t>());
-    
+
     pymio::bind_CustomIndexArray<double, mio::AgeGroup, mio::SimulationDay>(m, "AgeGroupSimulationDayArray");
     pymio::bind_class<mio::SimulationDay, pymio::EnablePickling::Never, mio::Index<mio::SimulationDay>>(m,
                                                                                                         "SimulationDay")
@@ -70,7 +70,8 @@ PYBIND11_MODULE(_simulation, m)
     auto damping_class = pymio::bind_class<mio::SquareDamping<double>, pymio::EnablePickling::Required>(m, "Damping");
     pymio::bind_damping_members(damping_class);
 
-    auto dampings_class = pymio::bind_class<mio::SquareDampings<double>, pymio::EnablePickling::Required>(m, "Dampings");
+    auto dampings_class =
+        pymio::bind_class<mio::SquareDampings<double>, pymio::EnablePickling::Required>(m, "Dampings");
     pymio::bind_dampings_members(dampings_class);
 
     pymio::bind_time_series(m, "TimeSeries");
@@ -99,12 +100,13 @@ PYBIND11_MODULE(_simulation, m)
         pymio::bind_class<mio::VectorDampings<double>, pymio::EnablePickling::Required>(m, "MobilityDampings");
     pymio::bind_dampings_members(mobility_dampings_class);
 
-    auto mobility_coeffs_class =
-        pymio::bind_class<mio::MobilityCoefficients<double>, pymio::EnablePickling::Required>(m, "MobilityCoefficients");
+    auto mobility_coeffs_class = pymio::bind_class<mio::MobilityCoefficients<double>, pymio::EnablePickling::Required>(
+        m, "MobilityCoefficients");
     pymio::bind_damping_expression_members(mobility_coeffs_class);
 
-    auto mobility_coeff_group_class = pymio::bind_class<mio::MobilityCoefficientGroup<double>, pymio::EnablePickling::Required>(
-        m, "MobilityCoefficientGroup");
+    auto mobility_coeff_group_class =
+        pymio::bind_class<mio::MobilityCoefficientGroup<double>, pymio::EnablePickling::Required>(
+            m, "MobilityCoefficientGroup");
     pymio::bind_damping_expression_group_members(mobility_coeff_group_class);
 
     pymio::bind_dynamicNPI_members(m, "DynamicNPIs");
@@ -127,7 +129,7 @@ PYBIND11_MODULE(_simulation, m)
             return std::vector<std::pair<mio::Date, mio::Date>>(h.begin(), h.end());
         },
         py::arg("state_id"), py::arg("start_date") = mio::Date(std::numeric_limits<int>::min(), 1, 1),
-        py::arg("end_date") = mio::Date(std::numeric_limits<int>::max(), 1, 1)); 
+        py::arg("end_date") = mio::Date(std::numeric_limits<int>::max(), 1, 1));
 
     m.def(
         "read_mobility_plain",