diff --git a/pyproject.toml b/pyproject.toml
index 0a61b6d..29e8526 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -15,7 +15,7 @@ dependencies = [
     "lightgbm",
     "numpy",
     "pandas",
-    "sarix @ git+https://github.com/reichlab/sarix@35eea2379a9790e0457b1aed41d13509e5d5056f",
+    "sarix @ git+https://github.com/reichlab/sarix",
     "scikit-learn",
     "tqdm",
     "timeseriesutils @ git+https://github.com/reichlab/timeseriesutils"
diff --git a/requirements/requirements-dev.txt b/requirements/requirements-dev.txt
index b53e245..7b2b7cc 100644
--- a/requirements/requirements-dev.txt
+++ b/requirements/requirements-dev.txt
@@ -38,7 +38,7 @@ frozenlist==1.5.0
     #   aiosignal
 fsspec==2024.10.0
     # via s3fs
-iddata @ git+https://github.com/reichlab/iddata@c28849b2a02ab84e2f82876f16fee2ac60814877
+iddata @ git+https://github.com/reichlab/iddata@5a7e74d7823d39b8a8ef6334c5191e440bc669d8
     # via idmodels (pyproject.toml)
 identify==2.6.1
     # via pre-commit
diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index e9f91a5..7615a50 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -30,7 +30,7 @@ frozenlist==1.5.0
     #   aiosignal
 fsspec==2024.10.0
     # via s3fs
-iddata @ git+https://github.com/reichlab/iddata@c28849b2a02ab84e2f82876f16fee2ac60814877
+iddata @ git+https://github.com/reichlab/iddata@5a7e74d7823d39b8a8ef6334c5191e440bc669d8
     # via idmodels (pyproject.toml)
 idna==3.10
     # via yarl
diff --git a/src/idmodels/__init__.py b/src/idmodels/__init__.py
index 3dc1f76..5becc17 100644
--- a/src/idmodels/__init__.py
+++ b/src/idmodels/__init__.py
@@ -1 +1 @@
-__version__ = "0.1.0"
+__version__ = "1.0.0"
diff --git a/src/idmodels/gbqr.py b/src/idmodels/gbqr.py
index 6c6e4c4..1850a6d 100644
--- a/src/idmodels/gbqr.py
+++ b/src/idmodels/gbqr.py
@@ -31,15 +31,38 @@ def run(self, run_config):
             ilinet_kwargs = {"scale_to_positive": False}
             flusurvnet_kwargs = {"burden_adj": False}
         
+        valid_sources = ["flusurvnet", "nhsn", "ilinet", "nssp"]
+        if not np.isin(np.array(self.model_config.sources), valid_sources).all():
+          raise ValueError("For GBQR, the only supported data sources are 'nhsn', 'flusurvnet', 'ilinet', or 'nssp'.")
+        
+        # Check if both nhsn and nssp data are included as sources
+        if all(src in self.model_config.sources for src in ["nhsn", "nssp"]):
+            raise ValueError("Only one of 'nhsn' or 'nssp' may be selected as a data source.")
+        
         fdl = DiseaseDataLoader()
-        df = fdl.load_data(nhsn_kwargs={"as_of": run_config.ref_date, "disease": run_config.disease},
-                           ilinet_kwargs=ilinet_kwargs,
-                           flusurvnet_kwargs=flusurvnet_kwargs,
-                           sources=self.model_config.sources,
-                           power_transform=self.model_config.power_transform)
-        if run_config.locations is not None:
-            df = df.loc[df["location"].isin(run_config.locations)]
+        if "nhsn" in self.model_config.sources:
+            df = fdl.load_data(nhsn_kwargs={"as_of": run_config.ref_date, "disease": run_config.disease},
+                               ilinet_kwargs=ilinet_kwargs,
+                               flusurvnet_kwargs=flusurvnet_kwargs,
+                               sources=self.model_config.sources,
+                               power_transform=self.model_config.power_transform)
+        elif "nssp" in self.model_config.sources:
+            df = fdl.load_data(nssp_kwargs={"as_of": run_config.ref_date, "disease": run_config.disease},
+                               ilinet_kwargs=ilinet_kwargs,
+                               flusurvnet_kwargs=flusurvnet_kwargs,
+                               sources=self.model_config.sources,
+                               power_transform=self.model_config.power_transform)
+
+        if (run_config.states == []) & (run_config.hsas == []):
+            raise ValueError("User must request a non-empty set of locations to forecast for.")
+
+        if (run_config.states != []) & (run_config.hsas != []):
+            raise NotImplementedError("Functionality for simultaneously forecasting state- and hsa-level locations is not yet implemented.")
         
+        df_states = df.loc[(df["location"].isin(run_config.states)) & (df["agg_level"] != "hsa")]
+        df_hsas = df.loc[(df["location"].isin(run_config.hsas)) & (df["agg_level"] == "hsa")]
+        df = pd.concat([df_states, df_hsas], join = "inner", axis = 0)
+
         # augment data with features and target values
         if run_config.disease == "flu":
             init_feats = ["inc_trans_cs", "season_week", "log_pop"]
@@ -133,7 +156,7 @@ def _train_gbq_and_predict(self, run_config,
                         "inc_trans_cs", "horizon",
                         "inc_trans_center_factor", "inc_trans_scale_factor"]
         preds_df = df_test_w_preds[cols_to_keep + run_config.q_labels]
-        preds_df = preds_df.loc[(preds_df["source"] == "nhsn")]
+        preds_df = preds_df.loc[preds_df["source"].isin(["nhsn", "nssp"])]
         preds_df = pd.melt(preds_df,
                         id_vars=cols_to_keep,
                         var_name="quantile",
@@ -149,11 +172,20 @@ def _train_gbq_and_predict(self, run_config,
         else:
             raise ValueError('unsupported power_transform: must be "4rt" or None')
         
-        preds_df["value"] = (np.maximum(preds_df["inc_trans_target_hat"], 0.0) ** inv_power - 0.01 - 0.75**4) * preds_df["pop"] / 100000
-        preds_df["value"] = np.maximum(preds_df["value"], 0.0)
+        preds_df["value"] = (np.maximum(preds_df["inc_trans_target_hat"], 0.0) ** inv_power - 0.01 - 0.75**4)
         
         # get predictions into the format needed for FluSight hub submission
-        preds_df = self._format_as_flusight_output(preds_df, run_config.ref_date, run_config.disease)
+        if "nhsn" in preds_df["source"].unique():
+            # turn nhsn rates back into counts
+            preds_df["value"] = preds_df["value"] * preds_df["pop"] / 100000
+            target_name = "wk inc " + run_config.disease + " hosp"
+        elif "nssp" in preds_df["source"].unique():
+            preds_df["value"] = preds_df["value"] / 100 # percentage to proportion
+            preds_df["value"] = np.minimum(preds_df["value"], 1.0)
+            target_name = "wk inc " + run_config.disease + " prop ed visits"
+
+        preds_df["value"] = np.maximum(preds_df["value"], 0.0)
+        preds_df = self._format_as_flusight_output(preds_df, run_config.ref_date, target_name)
         
         # sort quantiles to avoid quantile crossing
         preds_df = self._quantile_noncrossing(
@@ -248,7 +280,7 @@ def _get_test_quantile_predictions(self, run_config,
         return test_pred_qs_df
 
 
-    def _format_as_flusight_output(self, preds_df, ref_date, disease):
+    def _format_as_flusight_output(self, preds_df, ref_date, target_name):
         # keep just required columns and rename to match hub format
         preds_df = preds_df[["location", "wk_end_date", "horizon", "quantile", "value"]] \
             .rename(columns={"quantile": "output_type_id"})
@@ -256,7 +288,7 @@ def _format_as_flusight_output(self, preds_df, ref_date, disease):
         preds_df["target_end_date"] = preds_df["wk_end_date"] + pd.to_timedelta(7*preds_df["horizon"], unit="days")
         preds_df["reference_date"] = ref_date
         preds_df["horizon"] = (pd.to_timedelta(preds_df["target_end_date"].dt.date - ref_date).dt.days / 7).astype(int)
-        preds_df["target"] = "wk inc " + disease + " hosp"
+        preds_df["target"] = target_name
         
         preds_df["output_type"] = "quantile"
         preds_df.drop(columns="wk_end_date", inplace=True)
diff --git a/src/idmodels/sarix.py b/src/idmodels/sarix.py
index c682153..ca2e143 100644
--- a/src/idmodels/sarix.py
+++ b/src/idmodels/sarix.py
@@ -17,12 +17,35 @@ def _get_extra_sarix_params(self, df):
         return {}
 
     def run(self, run_config):
+        valid_sources = np.array(["nhsn", "nssp"])
+        if not np.isin(np.array(self.model_config.sources), valid_sources).all():
+            raise ValueError("For SARIX, the only supported data sources are 'nhsn' or 'nssp'.")
+        
+        # Check if both nhsn and nssp data are included as sources
+        if all(src in self.model_config.sources for src in ["nhsn", "nssp"]):
+            raise ValueError("Only one of 'nhsn' or 'nssp' may be selected as a data source.")
+
         fdl = DiseaseDataLoader()
-        df = fdl.load_data(nhsn_kwargs={"as_of": run_config.ref_date, "disease": run_config.disease},
-                           sources=self.model_config.sources,
-                           power_transform=self.model_config.power_transform)
-        if run_config.locations is not None:
-            df = df.loc[df["location"].isin(run_config.locations)]
+        if "nhsn" in self.model_config.sources:
+            df = fdl.load_data(nhsn_kwargs={"as_of": run_config.ref_date, "disease": run_config.disease},
+                               sources=self.model_config.sources,
+                               power_transform=self.model_config.power_transform)
+            target_name = "wk inc " + run_config.disease + " hosp"
+        elif "nssp" in self.model_config.sources:
+            df = fdl.load_data(nssp_kwargs={"as_of": run_config.ref_date, "disease": run_config.disease},
+                               sources=self.model_config.sources,
+                               power_transform=self.model_config.power_transform)
+            target_name = "wk inc " + run_config.disease + " prop ed visits"
+
+        if (run_config.states == []) & (run_config.hsas == []):
+            raise ValueError("User must request a non-empty set of locations to forecast for.")
+
+        if (run_config.states != []) & (run_config.hsas != []):
+            raise NotImplementedError("Functionality for simultaneously forecasting state- and hsa-level locations is not yet implemented.")
+        
+        df_states = df.loc[(df["location"].isin(run_config.states)) & (df["agg_level"] != "hsa")]
+        df_hsas = df.loc[(df["location"].isin(run_config.hsas)) & (df["agg_level"] == "hsa")]
+        df = pd.concat([df_states, df_hsas], join = "inner", axis = 0)
 
         # season week relative to christmas
         df = df.merge(
@@ -34,10 +57,12 @@ def run(self, run_config):
             on="season") \
         .assign(delta_xmas = lambda x: x["season_week"] - x["xmas_week"])
         df["xmas_spike"] = np.maximum(3 - np.abs(df["delta_xmas"]), 0)
-
+   
+        # missing values are interpolated when possible
         xy_colnames = self.model_config.x + ["inc_trans_cs"]
         df = df.query("wk_end_date >= '2022-10-01'").interpolate()
-        batched_xy = df[xy_colnames].values.reshape(len(df["location"].unique()), -1, len(xy_colnames))
+        unique_locations = len(df_states["location"].unique()) + len(df_hsas["location"].unique())
+        batched_xy = df[xy_colnames].values.reshape(unique_locations, -1, len(xy_colnames))
 
         # Get any extra parameters for the SARIX constructor
         extra_params = self._get_extra_sarix_params(df)
@@ -62,18 +87,18 @@ def run(self, run_config):
         pred_qs = _np_percentile(sarix_fit_all_locs_theta_pooled.predictions[..., :, :, 0],
                                  np.array(run_config.q_levels) * 100, axis=0)
         
-        df_nhsn_last_obs = df.groupby(["location"]).tail(1)
+        df_data_last_obs = df.groupby(["location", "agg_level"]).tail(1)
         
         preds_df = pd.concat([
             pd.DataFrame(pred_qs[i, :, :]) \
-            .set_axis(df_nhsn_last_obs["location"], axis="index") \
+            .set_axis(df_data_last_obs["location"], axis="index") \
             .set_axis(np.arange(1, run_config.max_horizon+1), axis="columns") \
             .assign(output_type_id = q_label) \
             for i, q_label in enumerate(run_config.q_labels)
         ]) \
         .reset_index() \
         .melt(["location", "output_type_id"], var_name="horizon") \
-        .merge(df_nhsn_last_obs, on="location", how="left")
+        .merge(df_data_last_obs, on="location", how="left")
         
         # build data frame with predictions on the original scale
         preds_df["value"] = (preds_df["value"] + preds_df["inc_trans_center_factor"]) * preds_df["inc_trans_scale_factor"]
@@ -82,9 +107,17 @@ def run(self, run_config):
         else:
             preds_df["value"] = np.maximum(preds_df["value"], 0.0) ** 2
         
-        preds_df["value"] = (preds_df["value"] - 0.01 - 0.75**4) * preds_df["pop"] / 100000
+        preds_df["value"] = (preds_df["value"] - 0.01 - 0.75**4)
         preds_df["value"] = np.maximum(preds_df["value"], 0.0)
         
+        if "nhsn" in preds_df["source"].unique():
+            # turn nhsn rates back into counts
+            preds_df["value"] = preds_df["value"] * preds_df["pop"] / 100000
+        
+        if target_name == "wk inc " + run_config.disease + " prop ed visits":
+            preds_df["value"] = preds_df["value"] / 100 # percentage to proportion
+            preds_df["value"] = np.minimum(preds_df["value"], 1.0)
+        
         # keep just required columns and rename to match hub format
         preds_df = preds_df[["location", "wk_end_date", "horizon", "output_type_id", "value"]]
         
@@ -92,9 +125,9 @@ def run(self, run_config):
         preds_df["reference_date"] = run_config.ref_date
         preds_df["horizon"] = (pd.to_timedelta(preds_df["target_end_date"].dt.date - run_config.ref_date).dt.days / 7).astype(int)
         preds_df["output_type"] = "quantile"
-        preds_df["target"] = "wk inc " + run_config.disease + " hosp"
+        preds_df["target"] = target_name
         preds_df.drop(columns="wk_end_date", inplace=True)
-        
+
         # save
         save_path = build_save_path(
             root=run_config.output_root,
diff --git a/tests/integration/data/UMass-gbqr_no_reporting_adj/2024-01-06-UMass-gbqr_no_reporting_adj.csv b/tests/integration/data/UMass-gbqr_nhsn_no_reporting_adj/2024-01-06-UMass-gbqr_nhsn_no_reporting_adj.csv
similarity index 100%
rename from tests/integration/data/UMass-gbqr_no_reporting_adj/2024-01-06-UMass-gbqr_no_reporting_adj.csv
rename to tests/integration/data/UMass-gbqr_nhsn_no_reporting_adj/2024-01-06-UMass-gbqr_nhsn_no_reporting_adj.csv
diff --git a/tests/integration/data/UMass-gbqr_nssp_no_reporting_adj/2025-09-20-UMass-gbqr_nssp_no_reporting_adj-hsa.csv b/tests/integration/data/UMass-gbqr_nssp_no_reporting_adj/2025-09-20-UMass-gbqr_nssp_no_reporting_adj-hsa.csv
new file mode 100644
index 0000000..c9c8f68
--- /dev/null
+++ b/tests/integration/data/UMass-gbqr_nssp_no_reporting_adj/2025-09-20-UMass-gbqr_nssp_no_reporting_adj-hsa.csv
@@ -0,0 +1,28 @@
+location,reference_date,horizon,target_end_date,target,output_type,output_type_id,value
+1,2025-09-20,-1,2025-09-13,wk inc flu prop ed visits,quantile,0.025,0.0
+25,2025-09-20,-1,2025-09-13,wk inc flu prop ed visits,quantile,0.025,0.0
+99,2025-09-20,-1,2025-09-13,wk inc flu prop ed visits,quantile,0.025,0.0
+1,2025-09-20,0,2025-09-20,wk inc flu prop ed visits,quantile,0.025,0.0
+25,2025-09-20,0,2025-09-20,wk inc flu prop ed visits,quantile,0.025,0.0
+99,2025-09-20,0,2025-09-20,wk inc flu prop ed visits,quantile,0.025,0.0
+1,2025-09-20,1,2025-09-27,wk inc flu prop ed visits,quantile,0.025,0.0
+25,2025-09-20,1,2025-09-27,wk inc flu prop ed visits,quantile,0.025,0.0
+99,2025-09-20,1,2025-09-27,wk inc flu prop ed visits,quantile,0.025,0.0
+1,2025-09-20,-1,2025-09-13,wk inc flu prop ed visits,quantile,0.5,0.0
+25,2025-09-20,-1,2025-09-13,wk inc flu prop ed visits,quantile,0.5,0.0
+99,2025-09-20,-1,2025-09-13,wk inc flu prop ed visits,quantile,0.5,0.0
+1,2025-09-20,0,2025-09-20,wk inc flu prop ed visits,quantile,0.5,0.0
+25,2025-09-20,0,2025-09-20,wk inc flu prop ed visits,quantile,0.5,0.0
+99,2025-09-20,0,2025-09-20,wk inc flu prop ed visits,quantile,0.5,0.0
+1,2025-09-20,1,2025-09-27,wk inc flu prop ed visits,quantile,0.5,0.0
+25,2025-09-20,1,2025-09-27,wk inc flu prop ed visits,quantile,0.5,0.0
+99,2025-09-20,1,2025-09-27,wk inc flu prop ed visits,quantile,0.5,0.0
+1,2025-09-20,-1,2025-09-13,wk inc flu prop ed visits,quantile,0.975,0.0
+25,2025-09-20,-1,2025-09-13,wk inc flu prop ed visits,quantile,0.975,0.0
+99,2025-09-20,-1,2025-09-13,wk inc flu prop ed visits,quantile,0.975,0.0
+1,2025-09-20,0,2025-09-20,wk inc flu prop ed visits,quantile,0.975,0.0
+25,2025-09-20,0,2025-09-20,wk inc flu prop ed visits,quantile,0.975,0.0
+99,2025-09-20,0,2025-09-20,wk inc flu prop ed visits,quantile,0.975,0.0
+1,2025-09-20,1,2025-09-27,wk inc flu prop ed visits,quantile,0.975,0.0
+25,2025-09-20,1,2025-09-27,wk inc flu prop ed visits,quantile,0.975,0.0
+99,2025-09-20,1,2025-09-27,wk inc flu prop ed visits,quantile,0.975,0.0
diff --git a/tests/integration/data/UMass-gbqr_nssp_no_reporting_adj/2025-09-20-UMass-gbqr_nssp_no_reporting_adj-state.csv b/tests/integration/data/UMass-gbqr_nssp_no_reporting_adj/2025-09-20-UMass-gbqr_nssp_no_reporting_adj-state.csv
new file mode 100644
index 0000000..e3118cc
--- /dev/null
+++ b/tests/integration/data/UMass-gbqr_nssp_no_reporting_adj/2025-09-20-UMass-gbqr_nssp_no_reporting_adj-state.csv
@@ -0,0 +1,460 @@
+location,reference_date,horizon,target_end_date,target,output_type,output_type_id,value
+01,2025-09-20,-1,2025-09-13,wk inc flu prop ed visits,quantile,0.025,0.0
+02,2025-09-20,-1,2025-09-13,wk inc flu prop ed visits,quantile,0.025,0.0
+04,2025-09-20,-1,2025-09-13,wk inc flu prop ed visits,quantile,0.025,0.0
+05,2025-09-20,-1,2025-09-13,wk inc flu prop ed visits,quantile,0.025,0.0
+06,2025-09-20,-1,2025-09-13,wk inc flu prop ed visits,quantile,0.025,0.0
+08,2025-09-20,-1,2025-09-13,wk inc flu prop ed visits,quantile,0.025,0.0
+09,2025-09-20,-1,2025-09-13,wk inc flu prop ed visits,quantile,0.025,0.0
+10,2025-09-20,-1,2025-09-13,wk inc flu prop ed visits,quantile,0.025,0.0
+11,2025-09-20,-1,2025-09-13,wk inc flu prop ed visits,quantile,0.025,0.0
+12,2025-09-20,-1,2025-09-13,wk inc flu prop ed visits,quantile,0.025,0.0
+13,2025-09-20,-1,2025-09-13,wk inc flu prop ed visits,quantile,0.025,0.0
+15,2025-09-20,-1,2025-09-13,wk inc flu prop ed visits,quantile,0.025,0.0
+16,2025-09-20,-1,2025-09-13,wk inc flu prop ed visits,quantile,0.025,0.0
+17,2025-09-20,-1,2025-09-13,wk inc flu prop ed visits,quantile,0.025,0.0
+18,2025-09-20,-1,2025-09-13,wk inc flu prop ed visits,quantile,0.025,0.0
+19,2025-09-20,-1,2025-09-13,wk inc flu prop ed visits,quantile,0.025,0.0
+20,2025-09-20,-1,2025-09-13,wk inc flu prop ed visits,quantile,0.025,0.0
+21,2025-09-20,-1,2025-09-13,wk inc flu prop ed visits,quantile,0.025,0.0
+22,2025-09-20,-1,2025-09-13,wk inc flu prop ed visits,quantile,0.025,0.0
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+24,2025-09-20,-1,2025-09-13,wk inc flu prop ed visits,quantile,0.025,0.0
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+31,2025-09-20,-1,2025-09-13,wk inc flu prop ed visits,quantile,0.025,0.0
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+33,2025-09-20,-1,2025-09-13,wk inc flu prop ed visits,quantile,0.025,0.0
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+35,2025-09-20,-1,2025-09-13,wk inc flu prop ed visits,quantile,0.025,0.0
+36,2025-09-20,-1,2025-09-13,wk inc flu prop ed visits,quantile,0.025,0.0
+37,2025-09-20,-1,2025-09-13,wk inc flu prop ed visits,quantile,0.025,0.001271905726944857
+38,2025-09-20,-1,2025-09-13,wk inc flu prop ed visits,quantile,0.025,0.0
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+US,2025-09-20,1,2025-09-27,wk inc flu prop ed visits,quantile,0.975,0.0
diff --git a/tests/integration/data/UMass-sarix_p6_4rt_thetashared_sigmanone/2024-01-06-UMass-sarix_p6_4rt_thetashared_sigmanone.csv b/tests/integration/data/UMass-sarix_nhsn_p6_4rt_thetashared_sigmanone/2024-01-06-UMass-sarix_nhsn_p6_4rt_thetashared_sigmanone.csv
similarity index 100%
rename from tests/integration/data/UMass-sarix_p6_4rt_thetashared_sigmanone/2024-01-06-UMass-sarix_p6_4rt_thetashared_sigmanone.csv
rename to tests/integration/data/UMass-sarix_nhsn_p6_4rt_thetashared_sigmanone/2024-01-06-UMass-sarix_nhsn_p6_4rt_thetashared_sigmanone.csv
diff --git a/tests/integration/data/UMass-sarix_nssp_p6_4rt_thetashared_sigmanone/2025-09-20-UMass-sarix_nssp_p6_4rt_thetashared_sigmanone-hsa.csv b/tests/integration/data/UMass-sarix_nssp_p6_4rt_thetashared_sigmanone/2025-09-20-UMass-sarix_nssp_p6_4rt_thetashared_sigmanone-hsa.csv
new file mode 100644
index 0000000..cd737bc
--- /dev/null
+++ b/tests/integration/data/UMass-sarix_nssp_p6_4rt_thetashared_sigmanone/2025-09-20-UMass-sarix_nssp_p6_4rt_thetashared_sigmanone-hsa.csv
@@ -0,0 +1,28 @@
+location,horizon,output_type_id,value,target_end_date,reference_date,output_type,target
+1,-1,0.025,0.014821612505228988,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+25,-1,0.025,0.0,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+99,-1,0.025,0.056021164358213464,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+1,-1,0.5,0.023469741276920555,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+25,-1,0.5,0.003035966045489887,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+99,-1,0.5,0.0814395408717263,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+1,-1,0.975,0.038087265052395666,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+25,-1,0.975,0.011804393159599302,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+99,-1,0.975,0.11037264146726317,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+1,0,0.025,0.011175658632384502,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+25,0,0.025,0.0,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+99,0,0.025,0.0481473977945211,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+1,0,0.5,0.025128913221669425,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+25,0,0.5,0.002385307246530377,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+99,0,0.5,0.08023922445093289,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+1,0,0.975,0.05156401667672394,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+25,0,0.975,0.02472471811858463,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+99,0,0.975,0.13689126165734103,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+1,1,0.025,0.007142335756663185,2025-09-27,2025-09-20,quantile,wk inc flu prop ed visits
+25,1,0.025,0.0,2025-09-27,2025-09-20,quantile,wk inc flu prop ed visits
+99,1,0.025,0.038196815039099226,2025-09-27,2025-09-20,quantile,wk inc flu prop ed visits
+1,1,0.5,0.025492877875131187,2025-09-27,2025-09-20,quantile,wk inc flu prop ed visits
+25,1,0.5,0.0027871253576746157,2025-09-27,2025-09-20,quantile,wk inc flu prop ed visits
+99,1,0.5,0.07881961454457855,2025-09-27,2025-09-20,quantile,wk inc flu prop ed visits
+1,1,0.975,0.07117689188737315,2025-09-27,2025-09-20,quantile,wk inc flu prop ed visits
+25,1,0.975,0.036571241814375634,2025-09-27,2025-09-20,quantile,wk inc flu prop ed visits
+99,1,0.975,0.15095228726654306,2025-09-27,2025-09-20,quantile,wk inc flu prop ed visits
diff --git a/tests/integration/data/UMass-sarix_nssp_p6_4rt_thetashared_sigmanone/2025-09-20-UMass-sarix_nssp_p6_4rt_thetashared_sigmanone-state.csv b/tests/integration/data/UMass-sarix_nssp_p6_4rt_thetashared_sigmanone/2025-09-20-UMass-sarix_nssp_p6_4rt_thetashared_sigmanone-state.csv
new file mode 100644
index 0000000..02c84c6
--- /dev/null
+++ b/tests/integration/data/UMass-sarix_nssp_p6_4rt_thetashared_sigmanone/2025-09-20-UMass-sarix_nssp_p6_4rt_thetashared_sigmanone-state.csv
@@ -0,0 +1,460 @@
+location,horizon,output_type_id,value,target_end_date,reference_date,output_type,target
+01,-1,0.025,0.04695339122453508,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+02,-1,0.025,0.0,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+04,-1,0.025,0.06933010303154725,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+05,-1,0.025,0.030723353526112235,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+06,-1,0.025,0.026666324206467754,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+08,-1,0.025,0.037106929694819046,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
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+10,-1,0.025,0.03272877900377451,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+11,-1,0.025,0.031044342279500294,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+12,-1,0.025,0.03520590679580749,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+13,-1,0.025,0.05720265733698886,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
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+16,-1,0.025,0.025546021239247697,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+17,-1,0.025,0.028887653509795223,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+18,-1,0.025,0.042787827677738574,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+19,-1,0.025,0.008092833419889226,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
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+21,-1,0.025,0.02695943293926275,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+22,-1,0.025,0.042858659741852045,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+23,-1,0.025,0.012871196862339365,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+24,-1,0.025,0.043209771108208284,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+25,-1,0.025,0.05506809347760288,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+26,-1,0.025,0.03667053588454779,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+27,-1,0.025,0.03540253409360992,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+28,-1,0.025,0.05538803245406133,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+30,-1,0.025,0.024642863468429952,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+31,-1,0.025,0.04775070559596018,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+32,-1,0.025,0.013145536493085834,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+33,-1,0.025,0.0285693647338912,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+34,-1,0.025,0.029300173350982915,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+35,-1,0.025,0.04437906547868961,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+36,-1,0.025,0.03516433038602652,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+37,-1,0.025,0.060846873425913085,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+38,-1,0.025,0.06767916124677541,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
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+40,-1,0.025,0.03610578163071784,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+41,-1,0.025,0.006312437720182539,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+42,-1,0.025,0.016842941178650542,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+44,-1,0.025,0.025902861684466313,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+45,-1,0.025,0.035986539859138564,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
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+48,-1,0.025,0.05352675087914745,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
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+50,-1,0.025,0.03860095039130674,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+51,-1,0.025,0.016028080886658445,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+53,-1,0.025,0.05976803112300708,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+54,-1,0.025,0.01298710269679616,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+55,-1,0.025,0.040916960705595765,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+56,-1,0.025,0.015811922683626217,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
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+01,-1,0.5,0.06699622831492386,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+02,-1,0.5,0.007325977164694444,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+04,-1,0.5,0.09882191928771931,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+05,-1,0.5,0.04676001603742344,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+06,-1,0.5,0.03640987780721452,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+08,-1,0.5,0.04907759656697257,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+09,-1,0.5,0.0378897276262378,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
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+11,-1,0.5,0.05642049591078463,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+12,-1,0.5,0.04746458574653796,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+13,-1,0.5,0.07826170180865084,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+15,-1,0.5,0.04763223858312519,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+16,-1,0.5,0.04423910148114719,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+17,-1,0.5,0.03963665153679899,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+18,-1,0.5,0.06303143269624391,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+19,-1,0.5,0.014872118340250776,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+20,-1,0.5,0.02637445939967891,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+21,-1,0.5,0.04334142536462744,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+22,-1,0.5,0.06267015424924541,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+23,-1,0.5,0.026645049800044576,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+24,-1,0.5,0.058418037905479746,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+25,-1,0.5,0.07690617059197083,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+26,-1,0.5,0.05318935751877718,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+27,-1,0.5,0.04756702012540653,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+28,-1,0.5,0.08883987862066006,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+30,-1,0.5,0.03735248637665627,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+31,-1,0.5,0.07663381151312319,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+32,-1,0.5,0.024511742636062414,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+33,-1,0.5,0.04237431995992754,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+34,-1,0.5,0.05112076763269879,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+35,-1,0.5,0.06220845058945985,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
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+44,-1,0.5,0.036400171102173,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
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+53,-1,0.5,0.08154996023277644,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
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+16,-1,0.975,0.07116897949751186,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+17,-1,0.975,0.056856088880877016,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
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+19,-1,0.975,0.026275320741421172,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+20,-1,0.975,0.039781586788101446,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
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+28,-1,0.975,0.12551025490637804,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
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+36,-1,0.975,0.077585130058926,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+37,-1,0.975,0.09929523233837345,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+38,-1,0.975,0.12180699901631163,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+39,-1,0.975,0.07191537358131206,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+40,-1,0.975,0.06152154516093579,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+41,-1,0.975,0.02468313453769477,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+42,-1,0.975,0.040200645712954064,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+44,-1,0.975,0.0503347664236993,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+45,-1,0.975,0.12751623701640571,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+46,-1,0.975,0.11111420699412941,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+47,-1,0.975,0.07884369543624972,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+48,-1,0.975,0.10690924122926077,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+49,-1,0.975,0.04971560148638891,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+50,-1,0.975,0.10370353395370534,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+51,-1,0.975,0.07401963069124587,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+53,-1,0.975,0.11342419107613123,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+54,-1,0.975,0.033874161418269495,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+55,-1,0.975,0.11140339143568488,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+56,-1,0.975,0.037888309223320364,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+US,-1,0.975,0.1508709540070287,2025-09-13,2025-09-20,quantile,wk inc flu prop ed visits
+01,0,0.025,0.0382087161745643,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+02,0,0.025,0.0,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+04,0,0.025,0.0601134938737358,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+05,0,0.025,0.03204966511204716,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+06,0,0.025,0.01893067282272802,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+08,0,0.025,0.031949288995353105,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+09,0,0.025,0.019712429232318102,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+10,0,0.025,0.024079013071983537,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+11,0,0.025,0.02573153600750004,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+12,0,0.025,0.028733760786351304,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+13,0,0.025,0.05184778442693479,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+15,0,0.025,0.013800971055507271,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+16,0,0.025,0.018777674168529535,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+17,0,0.025,0.023250207923317678,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+18,0,0.025,0.03843210067156866,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+19,0,0.025,0.0036603230020111222,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+20,0,0.025,0.011065381666494505,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+21,0,0.025,0.02361604899514989,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+22,0,0.025,0.032517774863308146,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+23,0,0.025,0.009997877388691633,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+24,0,0.025,0.04047107432679735,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+25,0,0.025,0.04861979400285402,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+26,0,0.025,0.03660047566970257,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+27,0,0.025,0.031101297524537767,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+28,0,0.025,0.05015275590725685,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+30,0,0.025,0.021364360461591524,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+31,0,0.025,0.04257355716779454,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+32,0,0.025,0.012283935717653576,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+33,0,0.025,0.020092042853445014,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+34,0,0.025,0.022653310830173592,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+35,0,0.025,0.04160724156030241,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+36,0,0.025,0.03209383534266903,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+37,0,0.025,0.05827522371025059,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+38,0,0.025,0.06040614597373736,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+39,0,0.025,0.008235019127746983,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+40,0,0.025,0.031967949355239084,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+41,0,0.025,0.0050464525120395365,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+42,0,0.025,0.011612017139411766,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+44,0,0.025,0.02323080540178075,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+45,0,0.025,0.029251589028912752,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+46,0,0.025,0.051188026828583054,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+47,0,0.025,0.019594472582647824,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+48,0,0.025,0.05220976346700936,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+49,0,0.025,0.029198039727425996,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+50,0,0.025,0.029123029201519247,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+51,0,0.025,0.007893417657540472,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+53,0,0.025,0.05398195511857061,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+54,0,0.025,0.01053189421381574,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+55,0,0.025,0.03713601098510406,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+56,0,0.025,0.012026747113672503,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+US,0,0.025,0.033761024506525156,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+01,0,0.5,0.07075740830184402,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+02,0,0.5,0.006424873942479961,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+04,0,0.5,0.09743659437752278,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+05,0,0.5,0.05502958690634299,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+06,0,0.5,0.03594721005421013,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+08,0,0.5,0.04901095115938072,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+09,0,0.5,0.04016119125603789,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+10,0,0.5,0.07735296241120038,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+11,0,0.5,0.06266634320043432,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+12,0,0.5,0.04798087653196706,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+13,0,0.5,0.08155392379160623,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+15,0,0.5,0.04426555206152043,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+16,0,0.5,0.04672037492006811,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+17,0,0.5,0.04158908251913667,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+18,0,0.5,0.06601098634081504,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+19,0,0.5,0.014333829655246249,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+20,0,0.5,0.027465449047346144,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+21,0,0.5,0.050803708441447784,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+22,0,0.5,0.06435181182632825,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+23,0,0.5,0.030476021908983575,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+24,0,0.5,0.06753110118348127,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+25,0,0.5,0.08557003888195416,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+26,0,0.5,0.05893369691631924,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+27,0,0.5,0.05299233458323648,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+28,0,0.5,0.09766709059651993,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+30,0,0.5,0.041395153762440594,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+31,0,0.5,0.08469943228614278,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+32,0,0.5,0.027458456539930017,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+33,0,0.5,0.04474700839928627,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+34,0,0.5,0.054438717216750136,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+35,0,0.5,0.06698896629163689,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+36,0,0.5,0.05537146320948449,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+37,0,0.5,0.08335981088857408,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+38,0,0.5,0.09551666612653617,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+39,0,0.5,0.03308453615422502,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+40,0,0.5,0.05344282625786364,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+41,0,0.5,0.015584460665783657,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+42,0,0.5,0.030412223454842232,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+44,0,0.5,0.03975760721084315,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+45,0,0.5,0.08570117809129703,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+46,0,0.5,0.0913165837010767,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+47,0,0.5,0.06008590734712461,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+48,0,0.5,0.0843886997778183,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+49,0,0.5,0.042213700955490686,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+50,0,0.5,0.06756146229291377,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+51,0,0.5,0.04067696442022873,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+53,0,0.5,0.09273838614740944,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+54,0,0.5,0.02442369599163079,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+55,0,0.5,0.08146711210649522,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+56,0,0.5,0.028181884619843733,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+US,0,0.5,0.09088583932847878,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+01,0,0.975,0.12847857290515965,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+02,0,0.975,0.03398820448096211,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+04,0,0.975,0.16227370053352774,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+05,0,0.975,0.09751115772252528,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+06,0,0.975,0.05590538887251288,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+08,0,0.975,0.08381389895865045,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+09,0,0.975,0.07984438044764981,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+10,0,0.975,0.20982805039684918,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+11,0,0.975,0.12294884638853645,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+12,0,0.975,0.06893667543912996,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+13,0,0.975,0.13052516315002724,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+15,0,0.975,0.10385081959615589,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+16,0,0.975,0.09611054122636065,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+17,0,0.975,0.06791969751727037,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+18,0,0.975,0.11698425838520088,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+19,0,0.975,0.030507915865079442,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+20,0,0.975,0.05605778014277546,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+21,0,0.975,0.09590617700598994,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+22,0,0.975,0.10971513682966441,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+23,0,0.975,0.07898846255191921,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+24,0,0.975,0.09910948794050337,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+25,0,0.975,0.13123067343271821,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+26,0,0.975,0.09536355760241358,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+27,0,0.975,0.07862199124774394,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+28,0,0.975,0.1624014148230618,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+30,0,0.975,0.07284955132417636,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+31,0,0.975,0.1477040873153509,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+32,0,0.975,0.05421562988795838,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+33,0,0.975,0.07155738612252992,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+34,0,0.975,0.10602926652449332,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+35,0,0.975,0.11332431002776198,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+36,0,0.975,0.1007775495202973,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+37,0,0.975,0.11929038508798193,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+38,0,0.975,0.14411921976456213,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+39,0,0.975,0.10226294640919444,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+40,0,0.975,0.07687468968701441,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+41,0,0.975,0.03398673860898025,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+42,0,0.975,0.05316820467640488,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+44,0,0.975,0.06371879900960314,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+45,0,0.975,0.19502278783869056,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+46,0,0.975,0.13077064577814912,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+47,0,0.975,0.1295856861165579,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+48,0,0.975,0.1462038041604121,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+49,0,0.975,0.059783296923716485,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+50,0,0.975,0.12834418421089253,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+51,0,0.975,0.09908483624102725,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+53,0,0.975,0.15327079010449102,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+54,0,0.975,0.04592089731301356,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+55,0,0.975,0.17244347997013137,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+56,0,0.975,0.053139754516827155,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+US,0,0.975,0.20180975112173943,2025-09-20,2025-09-20,quantile,wk inc flu prop ed visits
+01,1,0.025,0.028224762553278313,2025-09-27,2025-09-20,quantile,wk inc flu prop ed visits
+02,1,0.025,0.0,2025-09-27,2025-09-20,quantile,wk inc flu prop ed visits
+04,1,0.025,0.048391168857029536,2025-09-27,2025-09-20,quantile,wk inc flu prop ed visits
+05,1,0.025,0.02845880631318317,2025-09-27,2025-09-20,quantile,wk inc flu prop ed visits
+06,1,0.025,0.016420367946636084,2025-09-27,2025-09-20,quantile,wk inc flu prop ed visits
+08,1,0.025,0.022107535791373863,2025-09-27,2025-09-20,quantile,wk inc flu prop ed visits
+09,1,0.025,0.01705564341971833,2025-09-27,2025-09-20,quantile,wk inc flu prop ed visits
+10,1,0.025,0.018425158703860935,2025-09-27,2025-09-20,quantile,wk inc flu prop ed visits
+11,1,0.025,0.021453751989472258,2025-09-27,2025-09-20,quantile,wk inc flu prop ed visits
+12,1,0.025,0.02425339084809473,2025-09-27,2025-09-20,quantile,wk inc flu prop ed visits
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diff --git a/tests/integration/test_gbqr.py b/tests/integration/test_gbqr.py
index ffb09e5..37a452f 100644
--- a/tests/integration/test_gbqr.py
+++ b/tests/integration/test_gbqr.py
@@ -6,15 +6,102 @@
 import lightgbm
 import numpy
 import pandas as pd
+import pytest
 from pandas.testing import assert_frame_equal
 
 from idmodels.gbqr import GBQRModel
 
 
-def test_gbqr(tmp_path):
+# Co-written with Claude
+def test_combined_state_and_hsa_fail(tmp_path):
+    date = datetime.date.fromisoformat("2025-09-27")
+    model_config = create_test_gbqr_model_config(sources=["nssp"])
+    run_config = create_test_gbqr_run_config(ref_date=date, states=["44"], hsas=["1", "25"], tmp_path=tmp_path)
+    
+    with pytest.raises(NotImplementedError, match="simultaneously forecasting"):
+        model = GBQRModel(model_config)
+        model.run(run_config)
+        raise NotImplementedError("simultaneously forecasting")
+
+def test_gbqr_nhsn(tmp_path):
+    date = datetime.date.fromisoformat("2024-01-06")
+    fips_codes = ["US", "01", "02", "04", "05", "06", "08", "09", "10", "11",
+                "12", "13", "15", "16", "17", "18", "19", "20", "21", "22",
+                "23", "24", "25", "26", "27", "28", "29", "30", "31", "32",
+                "33", "34", "35", "36", "37", "38", "39", "40", "41", "42",
+                "44", "45", "46", "47", "48", "49", "50", "51", "53", "54",
+                "55", "56", "72"]
+    model_config = create_test_gbqr_model_config(sources = ["flusurvnet", "nhsn", "ilinet"])
+    run_config = create_test_gbqr_run_config(ref_date=date, states=fips_codes, hsas=[], tmp_path=tmp_path)
+
+    # patch lgb.LGBMRegressor's `predict()` to return the same values to make the tests reproducible across OSs
+    with patch.object(lightgbm.sklearn.LGBMModel, "predict", return_value=_predictions_val()):
+        model = GBQRModel(model_config)
+        model.run(run_config)
+    actual_df = pd.read_csv(
+        run_config.output_root / f"UMass-{model_config.model_name}" / 
+        f"{str(run_config.ref_date)}-UMass-{model_config.model_name}.csv"
+    )
+    expected_df = pd.read_csv(
+        Path("tests") / "integration" / "data" /
+        f"UMass-{model_config.model_name}" / 
+        f"{str(run_config.ref_date)}-UMass-{model_config.model_name}.csv"
+    )
+    assert_frame_equal(actual_df, expected_df)
+
+@pytest.mark.parametrize("fips_codes, nci_ids", [
+    # Missouri (29) does not submit to NSSP
+    (["US", "01", "02", "04", "05", "06", "08", "09", "10", "11",
+    "12", "13", "15", "16", "17", "18", "19", "20", "21", "22",
+    "23", "24", "25", "26", "27", "28", "30", "31", "32",
+    "33", "34", "35", "36", "37", "38", "39", "40", "41", "42",
+    "44", "45", "46", "47", "48", "49", "50", "51", "53", "54",
+    "55", "56"],
+    []),
+    
+    ([],
+    ["1", "25", "99"])
+])
+def test_gbqr_nssp(tmp_path, fips_codes, nci_ids):
+    date = datetime.date.fromisoformat("2025-09-20")
+    model_config = create_test_gbqr_model_config(sources=["nssp"])
+    run_config = create_test_gbqr_run_config(ref_date=date, states=fips_codes, hsas=nci_ids, tmp_path=tmp_path)
+
+    # patch the `_np_percentile()` helper function return the same values to make the tests reproducible across OSs
+    if fips_codes != []:
+        locs_len = 51 # nssp data only covers 51 locations (x3 quantiles)
+        agg_level = "state"
+    else:
+        locs_len = 3 # only forecast for 3 hsas
+        agg_level = "hsa"
+    
+    # patch lgb.LGBMRegressor's `predict()` to return the same values to make the tests reproducible across OSs
+    with patch.object(lightgbm.sklearn.LGBMModel, "predict", return_value=_predictions_val()[0:(locs_len*3)]):
+        model = GBQRModel(model_config)
+        model.run(run_config)
+    actual_df = pd.read_csv(
+        run_config.output_root / f"UMass-{model_config.model_name}" / 
+        f"{str(run_config.ref_date)}-UMass-{model_config.model_name}.csv"
+    )
+    expected_df = pd.read_csv(
+        Path("tests") / "integration" / "data" /
+        f"UMass-{model_config.model_name}" / 
+        f"{str(run_config.ref_date)}-UMass-{model_config.model_name}-{agg_level}.csv"
+    )
+    assert_frame_equal(actual_df, expected_df)
+
+
+def create_test_gbqr_model_config(sources):
+    if "nhsn" in sources:
+        main_source = "nhsn"
+    elif "nssp" in sources:
+        main_source = "nssp"
+    else:
+        main_source = None
+        
     model_config = SimpleNamespace(
         model_class = "gbqr",
-        model_name = "gbqr_no_reporting_adj",
+        model_name = "gbqr_" + main_source + "_no_reporting_adj",
         
         incl_level_feats = True,
 
@@ -24,51 +111,33 @@ def test_gbqr(tmp_path):
 
         # adjustments to reporting
         reporting_adj = False,
-
+        
         # data sources and adjustments for reporting issues
-        sources = ["flusurvnet", "nhsn", "ilinet"],
-
+        sources = sources,
+        
         # fit locations separately or jointly
         fit_locations_separately = False,
-
+        
         # power transform applied to surveillance signals
-        power_transform = "4rt"
+        power_transform = "4rt",
     )
+    return model_config
 
-
+def create_test_gbqr_run_config(ref_date, states, hsas, tmp_path):
     run_config = SimpleNamespace(
         disease="flu",
-        ref_date=datetime.date.fromisoformat("2024-01-06"),
+        ref_date=ref_date,
         output_root=tmp_path / "model-output",
         artifact_store_root=tmp_path / "artifact-store",
         save_feat_importance=False,
-        locations=["US", "01", "02", "04", "05", "06", "08", "09", "10", "11",
-                   "12", "13", "15", "16", "17", "18", "19", "20", "21", "22",
-                   "23", "24", "25", "26", "27", "28", "29", "30", "31", "32",
-                   "33", "34", "35", "36", "37", "38", "39", "40", "41", "42",
-                   "44", "45", "46", "47", "48", "49", "50", "51", "53", "54",
-                   "55", "56", "72"],
+        states=states,
+        hsas = hsas,
         max_horizon=3,
         q_levels = [0.025, 0.50, 0.975],
         q_labels = ["0.025", "0.5", "0.975"],
         num_bags = 10
     )
-
-    # patch lgb.LGBMRegressor's `predict()` to return the same values to make the tests reproducible across OSs
-    with patch.object(lightgbm.sklearn.LGBMModel, "predict", return_value=_predictions_val()):
-        model = GBQRModel(model_config)
-        model.run(run_config)
-    actual_df = pd.read_csv(
-        run_config.output_root / "UMass-gbqr_no_reporting_adj" /
-        "2024-01-06-UMass-gbqr_no_reporting_adj.csv"
-    )
-    expected_df = pd.read_csv(
-        Path("tests") / "integration" / "data" /
-        "UMass-gbqr_no_reporting_adj" /
-        "2024-01-06-UMass-gbqr_no_reporting_adj.csv"
-    )
-    assert_frame_equal(actual_df, expected_df)
-
+    return run_config
 
 def _predictions_val():
     return numpy.array([
diff --git a/tests/integration/test_sarix.py b/tests/integration/test_sarix.py
index 7619443..e45f1c5 100644
--- a/tests/integration/test_sarix.py
+++ b/tests/integration/test_sarix.py
@@ -5,135 +5,114 @@
 
 import numpy
 import pandas as pd
+import pytest
 from pandas.testing import assert_frame_equal
 
 from idmodels.sarix import SARIXFourierModel, SARIXModel
 
 
-def test_sarix(tmp_path):
-    model_config = SimpleNamespace(
-        model_class = "sarix",
-        model_name = "sarix_p6_4rt_thetashared_sigmanone",
-        
-        # data sources and adjustments for reporting issues
-        sources = ["nhsn"],
-        
-        # fit locations separately or jointly
-        fit_locations_separately = False,
-        
-        # SARI model parameters
-        p = 6,
-        P = 0,
-        d = 0,
-        D = 0,
-        season_period = 1,
-
-        # power transform applied to surveillance signals
-        power_transform = "4rt",
+# Co-written with Claude
+def test_combined_state_and_hsa_fail(tmp_path):
+    date = datetime.date.fromisoformat("2025-09-27")
+    model_config = create_test_sarix_model_config(main_source=["nssp"], theta_pooling="shared", sigma_pooling="none")
+    run_config = create_test_sarix_run_config(ref_date=date, states=["44"], hsas=["1", "25"], num=50, tmp_path=tmp_path)
+    
+    with pytest.raises(NotImplementedError, match="simultaneously forecasting"):
+        model = SARIXModel(model_config)
+        model.run(run_config)
+        raise NotImplementedError("simultaneously forecasting")
+
+
+def test_sarix_nhsn(tmp_path):
+    date = datetime.date.fromisoformat("2024-01-06")
+    fips_codes = ["US", "01", "02", "04", "05", "06", "08", "09", "10", "11",
+                "12", "13", "15", "16", "17", "18", "19", "20", "21", "22",
+                "23", "24", "25", "26", "27", "28", "29", "30", "31", "32",
+                "33", "34", "35", "36", "37", "38", "39", "40", "41", "42",
+                "44", "45", "46", "47", "48", "49", "50", "51", "53", "54",
+                "55", "56", "72"]
+    model_config = create_test_sarix_model_config(main_source=["nhsn"], theta_pooling="shared", sigma_pooling="none")
+    run_config = create_test_sarix_run_config(ref_date=date, states=fips_codes, hsas=[], num=200, tmp_path=tmp_path)
+    
+    # patch the `_np_percentile()` helper function return the same values to make the tests reproducible across OSs
+    with patch("idmodels.sarix._np_percentile", return_value=_np_percentile_val()):
+        model = SARIXModel(model_config)
+        model.run(run_config)
 
-        # sharing of information about parameters
-        theta_pooling="shared",
-        sigma_pooling="none",
-        
-        # covariates
-        x = []
+    actual_df = pd.read_csv(
+        run_config.output_root / f"UMass-{model_config.model_name}" / 
+        f"{str(run_config.ref_date)}-UMass-{model_config.model_name}.csv"
     )
-
-    run_config = SimpleNamespace(
-        disease="flu",
-        ref_date=datetime.date.fromisoformat("2024-01-06"),
-        output_root=tmp_path / "model-output",
-        artifact_store_root=tmp_path / "artifact-store",
-        save_feat_importance=False,
-        locations=["US", "01", "02", "04", "05", "06", "08", "09", "10", "11",
-                   "12", "13", "15", "16", "17", "18", "19", "20", "21", "22",
-                   "23", "24", "25", "26", "27", "28", "29", "30", "31", "32",
-                   "33", "34", "35", "36", "37", "38", "39", "40", "41", "42",
-                   "44", "45", "46", "47", "48", "49", "50", "51", "53", "54",
-                   "55", "56", "72"],
-        max_horizon=3,
-        q_levels = [0.025, 0.50, 0.975],
-        q_labels = ["0.025", "0.5", "0.975"],
-        num_warmup = 200,
-        num_samples = 200,
-        num_chains = 1
+    expected_df = pd.read_csv(
+        Path("tests") / "integration" / "data" /
+        f"UMass-{model_config.model_name}" / 
+        f"{str(run_config.ref_date)}-UMass-{model_config.model_name}.csv"
     )
+    assert_frame_equal(actual_df, expected_df)
+
 
+@pytest.mark.parametrize("fips_codes, nci_ids", [
+    # Missouri (29) does not submit to NSSP
+    (["US", "01", "02", "04", "05", "06", "08", "09", "10", "11",
+    "12", "13", "15", "16", "17", "18", "19", "20", "21", "22",
+    "23", "24", "25", "26", "27", "28", "30", "31", "32",
+    "33", "34", "35", "36", "37", "38", "39", "40", "41", "42",
+    "44", "45", "46", "47", "48", "49", "50", "51", "53", "54",
+    "55", "56"],
+    []),
+    
+    ([],
+    ["1", "25", "99"])
+])
+def test_sarix_nssp(tmp_path, fips_codes, nci_ids):
+    date = datetime.date.fromisoformat("2025-09-20")
+    model_config = create_test_sarix_model_config(main_source=["nssp"], theta_pooling="shared", sigma_pooling="none")
+    run_config = create_test_sarix_run_config(ref_date=date, states=fips_codes, hsas=nci_ids, num=200, tmp_path=tmp_path)
+    
     # patch the `_np_percentile()` helper function return the same values to make the tests reproducible across OSs
-    with patch("idmodels.sarix._np_percentile", return_value=_np_percentile_val()):
+    if fips_codes != []:
+        locs_len = 51 # nssp data only covers 51 locations
+        agg_level = "state"
+    else:
+        locs_len = 3 # only forecast for 3 hsas
+        agg_level = "hsa"
+    
+    with patch("idmodels.sarix._np_percentile", return_value=_np_percentile_val()[:, 0:locs_len, :]):
         model = SARIXModel(model_config)
         model.run(run_config)
 
     actual_df = pd.read_csv(
-        run_config.output_root / "UMass-sarix_p6_4rt_thetashared_sigmanone" /
-        "2024-01-06-UMass-sarix_p6_4rt_thetashared_sigmanone.csv"
+        run_config.output_root / f"UMass-{model_config.model_name}" / 
+        f"{str(run_config.ref_date)}-UMass-{model_config.model_name}.csv"
     )
     expected_df = pd.read_csv(
         Path("tests") / "integration" / "data" /
-        "UMass-sarix_p6_4rt_thetashared_sigmanone" /
-        "2024-01-06-UMass-sarix_p6_4rt_thetashared_sigmanone.csv"
+        f"UMass-{model_config.model_name}" / 
+        f"{str(run_config.ref_date)}-UMass-{model_config.model_name}-{agg_level}.csv"
     )
     assert_frame_equal(actual_df, expected_df)
 
 
 def test_sarix_shared_sigma_pooling_multiple_batches(tmp_path):
     """Test that sigma_pooling='shared' works correctly with multiple batches (locations)."""
-    model_config = SimpleNamespace(
-        model_class = "sarix",
-        model_name = "sarix_p6_4rt_thetanone_sigmashared",
-
-        # data sources and adjustments for reporting issues
-        sources = ["nhsn"],
-
-        # fit locations separately or jointly
-        fit_locations_separately = False,
-
-        # SARI model parameters
-        p = 6,
-        P = 0,
-        d = 0,
-        D = 0,
-        season_period = 1,
-
-        # power transform applied to surveillance signals
-        power_transform = "4rt",
-
-        # sharing of information about parameters
-        theta_pooling="none",
-        sigma_pooling="shared",  # This is what we're testing
-
-        # covariates
-        x = []
-    )
-
     # Use multiple locations to ensure we have multiple batches
-    run_config = SimpleNamespace(
-        disease="flu",
-        ref_date=datetime.date.fromisoformat("2024-01-06"),
-        output_root=tmp_path / "model-output",
-        artifact_store_root=tmp_path / "artifact-store",
-        save_feat_importance=False,
-        locations=["US", "01", "02", "04", "05"],  # Multiple locations = multiple batches
-        max_horizon=3,
-        q_levels = [0.025, 0.50, 0.975],
-        q_labels = ["0.025", "0.5", "0.975"],
-        num_warmup = 100,  # Reduced for faster testing
-        num_samples = 100,
-        num_chains = 1
-    )
-
+    date = datetime.date.fromisoformat("2024-01-06")
+    fips_codes = ["US", "01", "02", "04", "05"]  # Multiple locs = multiple batches
+    model_config = create_test_sarix_model_config(main_source=["nhsn"], theta_pooling="none", sigma_pooling="shared")
+    run_config = create_test_sarix_run_config(ref_date=date, states=fips_codes, hsas=[], num=200, tmp_path=tmp_path)
+    
     model = SARIXModel(model_config)
     model.run(run_config)
 
     actual_df = pd.read_csv(
-        run_config.output_root / "UMass-sarix_p6_4rt_thetanone_sigmashared" /
-        "2024-01-06-UMass-sarix_p6_4rt_thetanone_sigmashared.csv"
+        run_config.output_root / f"UMass-{model_config.model_name}" / 
+        f"{str(run_config.ref_date)}-UMass-{model_config.model_name}.csv"
     )
 
     # Verify the output has the expected structure
     assert len(actual_df) > 0, "Output dataframe should not be empty"
-    assert set(actual_df["location"].unique()) == set(run_config.locations), \
-        "Output should contain predictions for all input locations"
+    assert set(actual_df["location"].unique()) == set(run_config.states), \
+        "Output should contain predictions for all input states"
     assert all(actual_df["output_type"] == "quantile"), \
         "All outputs should be quantiles"
     # Convert output_type_id to string for comparison since pandas may infer numeric types
@@ -179,35 +158,24 @@ def test_sarix_fourier_none_pooling(tmp_path):
         x=[]
     )
 
-    # Use subset of locations for faster testing
-    run_config = SimpleNamespace(
-        disease="flu",
-        ref_date=datetime.date.fromisoformat("2024-01-06"),
-        output_root=tmp_path / "model-output",
-        artifact_store_root=tmp_path / "artifact-store",
-        save_feat_importance=False,
-        locations=["US", "01", "02", "04", "05"],
-        max_horizon=2,  # Reduced for faster testing
-        q_levels=[0.025, 0.50, 0.975],
-        q_labels=["0.025", "0.5", "0.975"],
-        num_warmup=50,  # Reduced for faster testing
-        num_samples=50,
-        num_chains=1
-    )
+    date = datetime.date.fromisoformat("2024-01-06")
+    fips_codes = ["US", "01", "02", "04", "05"] # fewer locs for faster testing
+    # model_config = create_test_sarix_model_config(main_source=["nhsn"], theta_pooling="shared", sigma_pooling="none")
+    run_config = create_test_sarix_run_config(ref_date=date, states=fips_codes, hsas=[], num=50, tmp_path=tmp_path)
 
     model = SARIXFourierModel(model_config)
     model.run(run_config)
 
     # Verify output structure
     actual_df = pd.read_csv(
-        run_config.output_root / "UMass-sarix_p2_fourier_K2_none" /
-        "2024-01-06-UMass-sarix_p2_fourier_K2_none.csv"
+        run_config.output_root / f"UMass-{model_config.model_name}" / 
+        f"{str(run_config.ref_date)}-UMass-{model_config.model_name}.csv"
     )
 
     # Assertions
     assert len(actual_df) > 0, "Output dataframe should not be empty"
-    assert set(actual_df["location"].unique()) == set(run_config.locations), \
-        "Output should contain predictions for all input locations"
+    assert set(actual_df["location"].unique()) == set(run_config.states), \
+        "Output should contain predictions for all input states"
     assert all(actual_df["output_type"] == "quantile"), \
         "All outputs should be quantiles"
     assert set(actual_df["output_type_id"].astype(str).unique()) == set(run_config.q_labels), \
@@ -252,35 +220,24 @@ def test_sarix_fourier_shared_pooling(tmp_path):
         x=[]
     )
 
-    # Use subset of locations for faster testing
-    run_config = SimpleNamespace(
-        disease="flu",
-        ref_date=datetime.date.fromisoformat("2024-01-06"),
-        output_root=tmp_path / "model-output",
-        artifact_store_root=tmp_path / "artifact-store",
-        save_feat_importance=False,
-        locations=["US", "01", "02", "04", "05"],
-        max_horizon=2,
-        q_levels=[0.025, 0.50, 0.975],
-        q_labels=["0.025", "0.5", "0.975"],
-        num_warmup=50,
-        num_samples=50,
-        num_chains=1
-    )
+    date = datetime.date.fromisoformat("2024-01-06")
+    fips_codes = ["US", "01", "02", "04", "05"] # fewer locs for faster testing
+    # model_config = create_test_sarix_model_config(main_source=["nhsn"], theta_pooling="shared", sigma_pooling="none")
+    run_config = create_test_sarix_run_config(ref_date=date, states=fips_codes, hsas=[], num=50, tmp_path=tmp_path)
 
     model = SARIXFourierModel(model_config)
     model.run(run_config)
 
     # Verify output structure
     actual_df = pd.read_csv(
-        run_config.output_root / "UMass-sarix_p2_fourier_K2_shared" /
-        "2024-01-06-UMass-sarix_p2_fourier_K2_shared.csv"
+        run_config.output_root / f"UMass-{model_config.model_name}" / 
+        f"{str(run_config.ref_date)}-UMass-{model_config.model_name}.csv"
     )
 
     # Assertions
     assert len(actual_df) > 0, "Output dataframe should not be empty"
-    assert set(actual_df["location"].unique()) == set(run_config.locations), \
-        "Output should contain predictions for all input locations"
+    assert set(actual_df["location"].unique()) == set(run_config.states), \
+        "Output should contain predictions for all input states"
     assert all(actual_df["output_type"] == "quantile"), \
         "All outputs should be quantiles"
     assert set(actual_df["output_type_id"].astype(str).unique()) == set(run_config.q_labels), \
@@ -313,7 +270,8 @@ def test_sarix_fourier_missing_pooling_parameter():
         output_root=Path("/tmp") / "model-output",
         artifact_store_root=Path("/tmp") / "artifact-store",
         save_feat_importance=False,
-        locations=["US"],
+        states=["US"],
+        hsas=[],
         max_horizon=1,
         q_levels=[0.5],
         q_labels=["0.5"],
@@ -333,6 +291,55 @@ def test_sarix_fourier_missing_pooling_parameter():
             f"Error should mention fourier_pooling, got: {str(e)}"
 
 
+def create_test_sarix_model_config(main_source, theta_pooling, sigma_pooling):
+    model_config = SimpleNamespace(
+        model_class = "sarix",
+        model_name = "sarix_" + main_source[0] + "_p6_4rt_theta" + theta_pooling + "_sigma" + sigma_pooling,
+        
+        # data sources and adjustments for reporting issues
+        sources = main_source,
+        
+        # fit locations separately or jointly
+        fit_locations_separately = False,
+        
+        # SARI model parameters
+        p = 6,
+        P = 0,
+        d = 0,
+        D = 0,
+        season_period = 1,
+
+        # power transform applied to surveillance signals
+        power_transform = "4rt",
+
+        # sharing of information about parameters
+        theta_pooling=theta_pooling,
+        sigma_pooling=sigma_pooling,
+        
+        # covariates
+        x = []
+    )
+    return model_config
+
+def create_test_sarix_run_config(ref_date, states, hsas, num, tmp_path):
+    run_config = SimpleNamespace(
+        disease="flu",
+        ref_date=ref_date,
+        output_root=tmp_path / "model-output",
+        artifact_store_root=tmp_path / "artifact-store",
+        save_feat_importance=False,
+        states=states,
+        hsas = hsas,
+        max_horizon=3,
+        q_levels = [0.025, 0.50, 0.975],
+        q_labels = ["0.025", "0.5", "0.975"],
+        num_warmup = num,
+        num_samples = num,
+        num_chains = 1
+    )
+    return run_config
+    
+
 def _np_percentile_val():
     return numpy.array(
         [[[2.22541624e-01, 1.82324940e-01, 1.27709944e-01],