Upload App.py

App.py

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+import pandas as pd
+import streamlit as st
+import numpy as np
+from scipy.integrate import odeint
+import matplotlib.pyplot as plt
+
+#dowload file
+!wget https://raw.githubusercontent.com/owid/monkeypox/main/owid-monkeypox-data.csv
+
+#read files
+data = pd.read_csv('owid-monkeypox-data.csv')
+data = data[['location','date','new_cases','total_cases','new_deaths','total_deaths']]
+
+#preprocessiong data
+all_location = {}
+for i in data['location'].unique():
+  all_location[i] = data[data['location'] == i].reset_index(drop=True)
+
+# SIR model differential equations.
+def deriv(x, t, beta, gamma):
+    s, i, r = x
+    dsdt = -beta * s * i
+    didt = beta * s * i - gamma * i
+    drdt =  gamma * i
+    return [dsdt, didt, drdt]
+
+#plot model
+def plotdata(t, s, i, e=None):
+    # plot the data
+    fig = plt.figure(figsize=(12,6))
+    ax = [fig.add_subplot(221, axisbelow=True), 
+          fig.add_subplot(223),
+          fig.add_subplot(122)]
+
+    ax[0].plot(t, s, lw=3, label='Fraction Susceptible')
+    ax[0].plot(t, i, lw=3, label='Fraction Infective')
+    ax[0].plot(t, r, lw=3, label='Recovered')
+    ax[0].set_title('Susceptible and Recovered Populations')
+    ax[0].set_xlabel('Time /days')
+    ax[0].set_ylabel('Fraction')
+
+    ax[1].plot(t, i, lw=3, label='Infective')
+    ax[1].set_title('Infectious Population')
+    if e is not None: ax[1].plot(t, e, lw=3, label='Exposed')
+    ax[1].set_ylim(0, 1.0)
+    ax[1].set_xlabel('Time /days')
+    ax[1].set_ylabel('Fraction')
+
+    ax[2].plot(s, i, lw=3, label='s, i trajectory')
+    ax[2].plot([1/R0, 1/R0], [0, 1], '--', lw=3, label='di/dt = 0')
+    ax[2].plot(s[0], i[0], '.', ms=20, label='Initial Condition')
+    ax[2].plot(s[-1], i[-1], '.', ms=20, label='Final Condition')
+    ax[2].set_title('State Trajectory')
+    ax[2].set_aspect('equal')
+    ax[2].set_ylim(0, 1.05)
+    ax[2].set_xlim(0, 1.05)
+    ax[2].set_xlabel('Susceptible')
+    ax[2].set_ylabel('Infectious')
+
+    for a in ax: 
+        a.grid(True)
+        a.legend()
+
+    plt.tight_layout()
+
+#final model
+def SIR(country,t_infective):
+  # parameter values
+  R0 = (all_location[country]['new_cases'].sum()/len(all_location[country]['date'].unique()))/t_infective
+  t_infective = t_infective
+
+  # initial number of infected and recovered individuals
+  i_initial = 1/20000
+  r_initial = 0.00
+  s_initial = 1 - i_initial - r_initial
+
+  gamma = 1/t_infective
+  beta = R0*gamma
+
+  t = np.linspace(0, 100, 1000)
+  x_initial = s_initial, i_initial, r_initial
+  soln = odeint(deriv, x_initial, t, args=(beta, gamma))
+  s, i, r = soln.T
+  e = None
+
+  plotdata(t, s, i)
+
+  return R0,t_infective,gamma,beta
+
+def main():
+    st.title("SIR Model for Monkeypox")
+
+    with st.form("questionaire"):
+        country = st.selectbox("Country")# user's input
+        recovery = st.slider("How long Monkeypox recover?", 21, 31, 21)# user's input
+
+        # clicked==True only when the button is clicked
+        clicked = st.form_submit_button("Show Graph")
+        if clicked:
+
+            #show total cases graph
+            all_location[country]['total_cases'].plot()
+
+            # Show SIR
+            SIR_param = SIR(country,recovery)
+
+            st.success("SIR model parameters for "+str(country)+" is")
+            st.success("R0 = "+str(SIR_param[0]))
+            st.success("Beta = "+str(SIR_param[3]))
+            st.success("Gamma = "+str(SIR_param[2]))
+
+# Run main()
+if __name__ == "__main__":
+    main()