Update app.py

app.py

@@ -6,8 +6,6 @@ import matplotlib.pyplot as plt
 import warnings
 warnings.filterwarnings("ignore")
 
-#dowload file
-
 #read files
 data = pd.read_csv('owid-monkeypox-data.csv')
 data = data[['location','iso_code','date','new_cases','total_cases','new_deaths','total_deaths']]
@@ -77,49 +75,87 @@ def plotdata(t, s, i,r,R0, e=None):
     plt.tight_layout()
     
     return fig
-     
-
+ 
 #final model
-def SIR(country,t_infective):
+def SIR(country,R0,t_infective):
+  #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_dict[country]
+  r_initial = 0.00
+  s_initial = 1 - i_initial - r_initial
+    
   gamma = 1/t_infective
-  beta = (all_location[country]['new_cases'].sum()/pop_dict[country])/len(all_location[country]['date'].unique())
-  R0 = beta/gamma
+  beta = R0*gamma
 
   # initial number of infected and recovered individuals
   i_initial = all_location[country]['new_cases'].sum()/pop_dict[country]
   r_initial = 0.00
   s_initial = 1 - i_initial - r_initial
   
-  t = np.linspace(0, 1000, 1000)
+  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_dict[country])
+  rangee =  len(all_location[country]['total_cases'])
+  rmpe = mean_absolute_percentage_error(scaler,i[0:rangee])
 
 
-  return R0,t_infective,beta,gamma,plotdata(t, s, i,r,R0)
+  return R0,t_infective,beta,gamma,rmpe,plotdata(t, s, i,r,R0)
+  
+def compare_plt(country):
+    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_dict[country])
+    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
 
 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
+        recovery = st.slider("How long Monkeypox recover?", 21, 31, 21)
+        R0 = st.slider("Basic Reproduction Number (R0)", 0.57, 3.00, 0.57)# user's input
         country_code = code[country][0]
+        range =  len(all_location['OWID_WRL']['total_cases'])
+        rmpe = mean_absolute_percentage_error(scaler,i[0:range])
         # clicked==True only when the button is clicked
         clicked = st.form_submit_button("Show Graph")
         if clicked:
         
             # Show SIR
-            SIR_param = SIR(country_code,recovery)
+            SIR_param = SIR(country_code,R0,recovery)
             
             st.pyplot(SIR_param[-1])
+            st.pyplot(compare_plt(country_code))
             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]))
+            st.success("RMPE = "+str(SIR_param[4]+"%"))
 
 # Run main()
 if __name__ == "__main__":