Delete app.py

app.py

@@ -1,165 +0,0 @@
-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()