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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() |