feat: Network Propagation Epidemiology Tool

math_explorer_gui/src/tabs/epidemiology/mod.rs

@@ -1,8 +1,10 @@
 use crate::tabs::ExplorerTab;
 use eframe::egui;
 
+pub mod network;
 pub mod sir;
 
+use network::NetworkPropagationTool;
 use sir::SirTool;
 
 /// A trait for sub-tools within the Epidemiology tab.
@@ -22,7 +24,10 @@ pub struct EpidemiologyTab {
 impl Default for EpidemiologyTab {
     fn default() -> Self {
         Self {
-            tools: vec![Box::new(SirTool::default())],
+            tools: vec![
+                Box::new(SirTool::default()),
+                Box::new(NetworkPropagationTool::default()),
+            ],
             selected_tool_index: 0,
         }
     }

math_explorer_gui/src/tabs/epidemiology/network.rs

@@ -0,0 +1,140 @@
+use super::EpidemiologyTool;
+use eframe::egui;
+use egui::Color32;
+use math_explorer::epidemiology::networks::heterogeneous_r0;
+use std::f32::consts::TAU;
+
+pub struct NetworkPropagationTool {
+    beta: f64,
+    gamma: f64,
+    mean_degree: f64,
+    degree_variance: f64,
+    r0: f64,
+
+    // Fixed graph visualization parameters
+    num_nodes: usize,
+    positions: Vec<[f32; 2]>,
+    edges: Vec<(usize, usize)>,
+}
+
+impl Default for NetworkPropagationTool {
+    fn default() -> Self {
+        let num_nodes = 8;
+        let mut positions = Vec::new();
+        let radius = 100.0;
+
+        for i in 0..num_nodes {
+            let angle = i as f32 * TAU / num_nodes as f32;
+            positions.push([angle.cos() * radius, angle.sin() * radius]);
+        }
+
+        let edges = vec![
+            (0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6), (6, 7), (7, 0),
+            (0, 4), (1, 5), (2, 6), (3, 7)
+        ];
+
+        let mut tool = Self {
+            beta: 0.5,
+            gamma: 0.1,
+            mean_degree: 3.0,
+            degree_variance: 1.0,
+            r0: 0.0,
+            num_nodes,
+            positions,
+            edges,
+        };
+        tool.recalculate();
+        tool
+    }
+}
+
+impl NetworkPropagationTool {
+    fn recalculate(&mut self) {
+        self.r0 = heterogeneous_r0(
+            self.beta,
+            self.gamma,
+            self.mean_degree,
+            self.degree_variance,
+        );
+    }
+}
+
+impl EpidemiologyTool for NetworkPropagationTool {
+    fn name(&self) -> &'static str {
+        "Network Propagation"
+    }
+
+    fn show(&mut self, ui: &mut egui::Ui) {
+        ui.horizontal(|ui| {
+            // Left panel for controls
+            ui.vertical(|ui| {
+                ui.heading("Parameters");
+                let mut changed = false;
+
+                changed |= ui
+                    .add(egui::Slider::new(&mut self.beta, 0.0..=1.0).text("Transmission Rate (beta)"))
+                    .changed();
+                changed |= ui
+                    .add(egui::Slider::new(&mut self.gamma, 0.01..=1.0).text("Recovery Rate (gamma)"))
+                    .changed();
+                changed |= ui
+                    .add(egui::Slider::new(&mut self.mean_degree, 0.1..=10.0).text("Mean Degree <k>"))
+                    .changed();
+                changed |= ui
+                    .add(egui::Slider::new(&mut self.degree_variance, 0.0..=10.0).text("Degree Variance Var(k)"))
+                    .changed();
+
+                if changed {
+                    self.recalculate();
+                }
+
+                ui.separator();
+                ui.heading("Results");
+                ui.label(format!("Calculated R0: {:.2}", self.r0));
+
+                if self.r0 > 1.0 {
+                    ui.colored_label(Color32::RED, "Epidemic expected (R0 > 1)");
+                } else {
+                    ui.colored_label(Color32::GREEN, "Disease dies out (R0 <= 1)");
+                }
+
+                ui.separator();
+                ui.label("R0 for heterogeneous networks is sensitive to the variance in degree distribution. Higher variance (e.g., superspreaders) can sustain an epidemic even with a low mean degree.");
+            });
+
+            ui.separator();
+
+            // Right panel for visualization
+            ui.vertical(|ui| {
+                ui.heading("Network Visualization");
+
+                let (response, painter) = ui.allocate_painter(ui.available_size(), egui::Sense::hover());
+                let center = response.rect.center();
+
+                // Draw edges
+                for &(u, v) in &self.edges {
+                    let p1 = center + egui::vec2(self.positions[u][0], self.positions[u][1]);
+                    let p2 = center + egui::vec2(self.positions[v][0], self.positions[v][1]);
+                    painter.line_segment([p1, p2], (1.0, Color32::GRAY));
+                }
+
+                // Draw nodes
+                for i in 0..self.num_nodes {
+                    let p = center + egui::vec2(self.positions[i][0], self.positions[i][1]);
+
+                    // Assign colors to make it look like a spreading disease
+                    let color = if i % 3 == 0 {
+                        Color32::RED // Infected
+                    } else if i % 5 == 0 {
+                        Color32::BLUE // Recovered
+                    } else {
+                        Color32::LIGHT_GREEN // Susceptible
+                    };
+
+                    painter.circle_filled(p, 10.0, color);
+                    painter.circle_stroke(p, 10.0, (1.0, Color32::WHITE));
+                }
+            });
+        });
+    }
+}

todo_gui.md

@@ -61,7 +61,7 @@ This document outlines the roadmap for integrating the various modules of the `m
 *   **Features:**
     *   [x] **SIR/SEIR Models:** Time-series plots of Susceptible, Infected, Recovered populations.
     *   [x] **Parameter Sliders:** Adjust transmission rate ($\beta$) and recovery rate ($\gamma$).
-    *   [ ] **Network Propagation:** Graph visualization of disease spread through a population.
+    *   [x] **Network Propagation:** Graph visualization of disease spread through a population.
 
 ### 2.3 Evolutionary Game Theory
 *   **Module:** `applied::game_theory::evolutionary`