basic widget with lintr

widget/widget.py

@@ -1,10 +1,151 @@
+# To-do: be very sure we know what rows vs columns mean
 import numpy as np
+import pandas as pd
 import streamlit as st
 
-# 2 groups as test
-# To-do: be very sure we know what rows vs columns mean
-r_mat = np.array([[1.0, 2.0], [2.0, 1.0]])
 
-st.sidebar.markdown("## Parameters")
-high_r0 = st.sidebar.slider("High R0", 0.1, 10.0, 1.0)  # min, max, default
-low_r0 = st.sidebar.slider("Low R0", 0.1, 10.0, 1.0)  # min, max, default
+def ngm_sir(N, V, K, VE, p_severe):
+    """
+    Function to calculate next-generation matrix (NGM) for a 4-group SIR model
+
+    Args:
+        N (array): Population sizes for each group
+        V (array): Number of vaccine doses administered to each group
+        K (2D array): Contact matrix (4x4); (flexible?)
+        VE (float): Vaccine efficacy, all or nothing
+        p_severe (array): Group spcfeic probability of severe infection
+
+    Returns:
+        dict: Contains R-effective, total infections, severe infections, adjusted K matrix
+    """
+    # check sizes of inputs? do we need this
+    assert np.all(N >= V), "Vaccinated cannot exceed population size"
+    assert len(N) == len(V) == len(p_severe), "Input dimensions must match"
+
+    # Calculate susceptibles for NGM
+    S = N - VE * V
+    K_adjusted = K * S / N
+
+    # Eigenvalue computation for NGM
+    eigenvalues, eigenvectors = np.linalg.eig(K_adjusted)
+    r_effective = max(eigenvalues)
+
+    # Calculate infections and severe infections
+    dominant_vector = eigenvectors[:, np.argmax(eigenvalues)]
+    infections = dominant_vector * N
+    severe_infections = infections * p_severe
+
+    return {
+        "R_effective": r_effective,
+        "Infections": infections.real,
+        "Severe_Infections": severe_infections.real,
+        "K_adjusted": K_adjusted,
+    }
+
+
+def app():
+    st.title("4-Group SIR Model NGM Calculator")
+
+    # Group names
+    group_names = ["Core", "Kids", "Travelers", "General Population"]
+
+    # Sidebar for inputs
+    st.sidebar.header("Model Inputs")
+
+    # Population size
+    st.sidebar.subheader("Population Sizes")
+    N = np.array(
+        [
+            st.sidebar.number_input(
+                f"Population ({group})", value=100, min_value=0
+            )
+            for group in group_names
+        ]
+    )
+
+    # Vaccine doses
+    st.sidebar.subheader("Vaccine Doses")
+    V = np.array(
+        [
+            st.sidebar.number_input(
+                f"Vaccine Doses ({group})",
+                value=0,
+                min_value=0,
+                max_value=N[i],
+            )
+            for i, group in enumerate(group_names)
+        ]
+    )
+
+    # Vaccine efficacy
+    VE = st.sidebar.slider("Vaccine Efficacy", 0.0, 1.0, value=0.7, step=0.01)
+
+    # Probability of severe infections
+    st.sidebar.subheader("Severe Infection Probabilities")
+    p_severe = np.array(
+        [
+            st.sidebar.slider(
+                f"Probability of Severe Infection ({group})",
+                0.0,
+                1.0,
+                value=0.03,
+                step=0.01,
+            )
+            for group in group_names
+        ]
+    )
+
+    # Contact matrix
+    st.sidebar.subheader("NGM (4x4)")
+    K = np.array(
+        [
+            [
+                st.sidebar.number_input(
+                    f"K[{group_names[i]} → {group_names[j]}]",
+                    value=1 if i != j else 3,
+                )
+                for j in range(4)
+            ]
+            for i in range(4)
+        ]
+    )
+
+    # Perform the NGM calculation
+    if st.sidebar.button("Calculate"):
+        result = ngm_sir(N, V, K, VE, p_severe)
+
+        # Display the adjusted contact matrix
+        st.subheader("NGM with vaccination")
+        st.write(
+            "This matrix reflects the impact of vaccine efficacy and susceptibility:"
+        )
+
+        K_adjusted_df = pd.DataFrame(
+            result["K_adjusted"],
+            columns=group_names,
+            index=group_names,
+        )
+
+        st.dataframe(K_adjusted_df)
+
+        # Display results
+        st.subheader("Results")
+        st.write(f"R-effective: {result['R_effective']:.2f}")
+        st.write("Infections by Group:")
+        st.json(
+            {
+                group: round(inf, 2)
+                for group, inf in zip(group_names, result["Infections"])
+            }
+        )
+        st.write("Severe Infections by Group:")
+        st.json(
+            {
+                group: round(sev, 2)
+                for group, sev in zip(group_names, result["Severe_Infections"])
+            }
+        )
+
+
+if __name__ == "__main__":
+    app()