Create 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
+from sklearn.metrics import mean_absolute_percentage_error
+import warnings
+warnings.filterwarnings("ignore")
+
+#read files
+data = pd.read_csv('owid-monkeypox-data.csv')
+data = data[['location','iso_code','date','new_cases','total_cases','new_deaths','total_deaths']]
+
+pop = pd.read_csv('API_SP.POP.TOTL_DS2_en_csv_v2_4578059.csv')
+#preprocessiong data
+all_location = {}
+for i in data['iso_code'].unique():
+  all_location[i] = data[data['iso_code'] == i].reset_index(drop=True)
+
+popu = pop[['Country Code','2021']].to_dict('index')
+pop_dict = {}
+for i in popu.values():
+  pop_dict[i['Country Code']] = i['2021']
+
+pop_dict['GLP'] = 400000
+pop_dict['MTQ'] = 376480
+pop_dict['OWID_WRL'] = 7836630792
+
+code = dict(data.groupby('location')['iso_code'].unique())
+
+# 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,r,R0, 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()
+    
+    return fig
+    
+def compare_plt(country,i,pop):
+    fig = plt.figure(figsize=(12,6))
+    ax = [fig.add_subplot(121, axisbelow=True),fig.add_subplot(122)]
+    ax[0].set_title('Monkeypox confirmed cases')
+    ax[0].plot(all_location[country]['total_cases'],lw=3,label='Infective')
+    ax[0].set_xlabel('Days')
+    ax[0].set_ylabel('Number of cases')
+    ax[0].legend()
+    
+    scaler = all_location[country]['total_cases'].apply(lambda x : x/pop)
+    ax[1].set_title('Monkeypox confirmed cases compare with model')
+    ax[1].plot(scaler,lw=3,label='Real Infective')
+    ax[1].plot(i,lw=3,label='SIR model Infective')
+    ax[1].set_ylim(0,0.00005)
+    ax[1].set_xlim(0,200)
+    ax[1].set_xlabel('Days')
+    ax[1].set_ylabel('Fraction Number of cases')
+    ax[1].legend()
+    plt.tight_layout()
+    
+    return fig
+    
+#final model
+def SIR(country,R0,t_infective,pop):
+  #R0 = 0.57 - 1.25
+
+  # parameter values
+  R0 = R0
+  t_infective = t_infective
+    
+  # initial number of infected and recovered individuals
+  i_initial = all_location[country]['total_cases'].iloc[0]/pop
+  r_initial = 0.00
+  s_initial = 1 - i_initial - r_initial
+    
+  gamma = 1/t_infective
+  beta = R0*gamma
+  
+  t = np.linspace(0, 3000, 3000)
+  x_initial = s_initial, i_initial, r_initial
+  soln = odeint(deriv, x_initial, t, args=(beta, gamma))
+  s, i, r = soln.T
+  e = None
+  
+  scaler = all_location[country]['total_cases'].apply(lambda x : x/pop)
+  rangee =  len(all_location[country]['total_cases'])
+  rmpe = mean_absolute_percentage_error(scaler,i[0:rangee])*100
+
+
+  return R0,t_infective,beta,gamma,rmpe,plotdata(t, s, i,r,R0),compare_plt(country,i,pop)
+  
+def main():
+    st.title("SIR Model for Monkeypox in Thailand")
+    st.subheader("Latest updated : 10/02/2023")
+    st.subheader("Reference : https://jckantor.github.io/CBE30338/03.09-COVID-19.html")
+    st.caption("Display graph of SIR model of monkeypox and comparison between the model and actual data. Try to find the best R0 that fit for the actual data (lowest MAPE).")
+
+    with st.form("questionaire"):
+        recovery = st.slider("How long Monkeypox last until recovery(days)? ", 14, 31, 21)
+        R0 = st.slider("Basic Reproduction Number (R0)", 0.57, 3.00, 0.57)# user's input
+        country_code = code["Thailand"][0]
+        pop = pop_dict[country_code]
+       
+        # clicked==True only when the button is clicked
+        clicked = st.form_submit_button("Show Graph")
+        if clicked:
+        
+            # Show SIR
+            SIR_param = SIR(country_code,R0,recovery,pop)
+            
+            if SIR_param[0] <= 1:
+                a = 'No epidemic.'
+            else:
+                a = 'Epidemic has began.'
+            
+            st.pyplot(SIR_param[-2])
+            st.pyplot(SIR_param[-1])
+            st.success("SIR model parameters of Thailand "+" is")
+            st.success("R0 (Basic Reproduction Number) = "+str(SIR_param[0])+' '+a)
+            st.success("Beta (Rate of transmission) = "+str(round(SIR_param[2],3)))
+            st.success("Gamma (Rate of Recovery) = "+str(round(SIR_param[3],3)))
+            st.success("MAPE = "+str(round(SIR_param[4],3))+"%")
+
+# Run main()
+if __name__ == "__main__":
+    main()