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utils.py
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import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
def dataPrepare(total_population, confirmed, recovered, exposed_ratio):
days = len(confirmed)
X = []
y = []
for i in range(days-2):
I_0 = confirmed[i] - recovered[i]
R_0 = recovered[i]
E_0 = I_0 * exposed_ratio
S_0 = total_population - I_0 - R_0 - E_0
I_1 = confirmed[i+1] - recovered[i+1]
R_1 = recovered[i+1]
E_1 = I_1 * exposed_ratio
S_1 = total_population - I_1 - R_1 - E_1
X.append([S_0, E_0, I_0, R_0, S_1, E_1, I_1, R_1])
y.append(confirmed[i+2] - recovered[i+2])
return np.array(X), np.array(y).reshape((-1,1))
def dataPrepareWithTime(total_population, confirmed, recovered, exposed_ratio):
days = len(confirmed)
X = []
y = []
for i in range(days-2):
I_0 = confirmed[i] - recovered[i]
R_0 = recovered[i]
E_0 = I_0 * exposed_ratio
S_0 = total_population - I_0 - R_0 - E_0
I_1 = confirmed[i+1] - recovered[i+1]
R_1 = recovered[i+1]
E_1 = I_1 * exposed_ratio
S_1 = total_population - I_1 - R_1 - E_1
X.append([S_0, E_0, I_0, R_0, S_1, E_1, I_1, R_1, i])
y.append(confirmed[i+2] - recovered[i+2])
return np.array(X), np.array(y).reshape((-1,1))
def check_and_plot(X, y, date, params):
days = X.shape[0]
predict_infected = []
for day in range(days):
data = X[day]
S_1 = data[4]
E_1 = data[5]
I_1 = data[6]
R_1 = data[7]
N = S_1 + E_1 + I_1 + R_1
preS = (1 - params[0]*I_1/N) * S_1
preE = (1 - params[1])*E_1 + params[0]*I_1*S_1/N
preI = (1 - params[2])*I_1 + params[1]*E_1
preR = R_1 + params[2]*I_1
predict_infected.append(preI)
plt.figure(figsize=(12,6))
plt.plot(predict_infected, label="predicted")
plt.plot(y, label="ground truth")
plt.legend(loc='upper left')
date = [d[5:] for d in date]
plt.xticks(np.arange(days), date)
plt.show()
mape = np.mean(np.abs(np.array(predict_infected) - y.flatten()))
return predict_infected, mape
def predict_and_plot(X, y, date, start_predict, params):
days = X.shape[0]
predict_infected = []
for day in range(start_predict):
data = X[day]
S_1 = data[4]
E_1 = data[5]
I_1 = data[6]
R_1 = data[7]
N = S_1 + E_1 + I_1 + R_1
preS = (1 - params[0]*I_1/N) * S_1
preE = (1 - params[1])*E_1 + params[0]*I_1*S_1/N
preI = (1 - params[2])*I_1 + params[1]*E_1
preR = R_1 + params[2]*I_1
predict_infected.append(preI)
S_1 = X[start_predict][4]
E_1 = X[start_predict][5]
I_1 = X[start_predict][6]
R_1 = X[start_predict][7]
for day in range(start_predict, days):
N = S_1 + E_1 + I_1 + R_1
preS = (1 - params[0]*I_1/N) * S_1
preE = (1 - params[1])*E_1 + params[0]*I_1*S_1/N
preI = (1 - params[2])*I_1 + params[1]*E_1
preR = R_1 + params[2]*I_1
predict_infected.append(preI)
S_1 = preS
E_1 = preE
I_1 = preI
R_1 = preR
plt.figure(figsize=(10,6))
plt.plot(predict_infected, label="predicted")
plt.plot(y, label="ground truth")
plt.legend(loc='upper left')
plt.xlabel("Days")
plt.ylabel("Still Infected")
date = [d[5:] for d in date]
plt.xticks(np.arange(0, days, 5))
plt.show()
predict_error = (np.array(predict_infected) - y.flatten())[start_predict:]
mape = np.mean(np.abs(predict_error))
return predict_infected, mape
def computeBeta(params, t):
if t <= 4:
return max(0,params[0])
elif t <= 15:
return max(0,params[0]+(params[1]-params[0])*(t-4)/(15-4))
elif t <= 30:
return max(0,params[1]+(params[2]-params[1])*(t-15)/(30-15))
else:
return max(0,params[2]+(params[3]-params[2])*(t-30)/(50-30))
def computeGamma(params, t):
if t <= 4:
return max(0,params[4])
elif t <= 15:
return max(0,params[4]+(params[5]-params[4])*(t-4)/(15-4))
elif t <= 30:
return max(0,params[5]+(params[6]-params[5])*(t-15)/(30-15))
else:
return max(0,params[6]+(params[7]-params[6])*(t-30)/(50-30))
def check_and_plot_dyn(X, y, date, params, sigma=0.02531358, gamma=0.07680123):
days = X.shape[0]
predict_infected = []
for day in range(days):
data = X[day]
S_1 = data[4]
E_1 = data[5]
I_1 = data[6]
R_1 = data[7]
t = day
N = S_1 + E_1 + I_1 + R_1
preS = (1 - computeBeta(params, t+1)*I_1/N) * S_1
preE = (1 - sigma)*E_1 + computeBeta(params, t+1)*I_1*S_1/N
preI = (1 - computeGamma(params,t+1))*I_1 + sigma*E_1
preR = R_1 + computeGamma(params,t+1)*I_1
predict_infected.append(preI)
plt.figure(figsize=(12,6))
plt.plot(predict_infected, label="predicted")
plt.plot(y, label="ground truth")
plt.legend(loc='upper left')
date = [d[5:] for d in date]
plt.xticks(np.arange(days))
# plt.xticks(np.arange(days), date)
plt.show()
mae = np.mean(np.abs(np.array(predict_infected) - y.flatten()))
return predict_infected, mae
def predict_and_plot_dyn(X, y, date, start_predict, params, sigma=0.02531358, gamma=0.07680123):
days = X.shape[0]
predict_infected = []
for day in range(start_predict):
data = X[day]
S_1 = data[4]
E_1 = data[5]
I_1 = data[6]
R_1 = data[7]
N = S_1 + E_1 + I_1 + R_1
t = day
preS = (1 - computeBeta(params, t+1)*I_1/N) * S_1
preE = (1 - sigma)*E_1 + computeBeta(params, t+1)*I_1*S_1/N
preI = (1 - computeGamma(params,t+1))*I_1 + sigma*E_1
preR = R_1 + computeGamma(params,t+1)*I_1
predict_infected.append(preI)
S_1 = X[start_predict][4]
E_1 = X[start_predict][5]
I_1 = X[start_predict][6]
R_1 = X[start_predict][7]
for day in range(start_predict, days):
N = S_1 + E_1 + I_1 + R_1
t = day
preS = (1 - computeBeta(params, t+1)*I_1/N) * S_1
preE = (1 - sigma)*E_1 + computeBeta(params, t+1)*I_1*S_1/N
preI = (1 - computeGamma(params,t+1))*I_1 + sigma*E_1
preR = R_1 + computeGamma(params,t+1)*I_1
predict_infected.append(preI)
S_1 = preS
E_1 = preE
I_1 = preI
R_1 = preR
plt.figure(figsize=(10,6))
plt.plot(predict_infected, label="predicted")
plt.plot(y, label="ground truth")
plt.legend(loc='upper left')
plt.xlabel("Days")
plt.ylabel("Still Infected")
date = [d[5:] for d in date]
plt.xticks(np.arange(0, days, 5))
plt.show()
predict_error = (np.array(predict_infected) - y.flatten())[start_predict:]
mae = np.mean(np.abs(predict_error))
return predict_infected, mae
def predict_and_plot_dyn_for_other_region(X, y, days, start_predict, params, sigma=0.02531358, gamma=0.07680123):
predict_infected = []
for day in range(start_predict):
data = X[day]
S_1 = data[4]
E_1 = data[5]
I_1 = data[6]
R_1 = data[7]
N = S_1 + E_1 + I_1 + R_1
t = day
preS = (1 - computeBeta(params, t+1)*I_1/N) * S_1
preE = (1 - sigma)*E_1 + computeBeta(params, t+1)*I_1*S_1/N
preI = (1 - computeGamma(params,t+1))*I_1 + sigma*E_1
preR = R_1 + computeGamma(params,t+1)*I_1
predict_infected.append(preI)
S_1 = X[start_predict][4]
E_1 = X[start_predict][5]
I_1 = X[start_predict][6]
R_1 = X[start_predict][7]
for day in range(start_predict, days):
N = S_1 + E_1 + I_1 + R_1
t = day
preS = (1 - computeBeta(params, t+1)*I_1/N) * S_1
preE = (1 - sigma)*E_1 + computeBeta(params, t+1)*I_1*S_1/N
preI = (1 - computeGamma(params,t+1))*I_1 + sigma*E_1
preR = R_1 + computeGamma(params,t+1)*I_1
predict_infected.append(preI)
S_1 = preS
E_1 = preE
I_1 = preI
R_1 = preR
plt.figure(figsize=(10,6))
plt.plot(predict_infected, label="predicted number using parameters from Zhejiang")
plt.plot(y, label="ground truth number in the Italy")
plt.legend(loc='upper left')
plt.xlabel("Days")
plt.ylabel("Still Infected")
plt.xticks(np.arange(0, days, 5))
plt.show()
return predict_infected