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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 | |
| #read files | |
| data = pd.read_csv('myfile.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,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() | |
| st.pyplot(fig) | |
| #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 | |
| return R0,t_infective,beta,gamma | |
| def main(): | |
| st.title("SIR Model for Monkeypox") | |
| with st.form("questionaire"): | |
| country = st.selectbox("Country",data['location'].unique())# 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(st.pyplot(all_location[country]['total_cases'])) | |
| st.success(st.pyplot(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[2])) | |
| st.success("Gamma = "+str(SIR_param[3])) | |
| # Run main() | |
| if __name__ == "__main__": | |
| main() |