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SEIR.py
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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from scipy.optimize import minimize
import math
from scipy.stats import binom as binom
from smoothing import dataframe_smoothing
class SEIR():
def __init__(self):
# ========================================== #
# Model parameters
# ========================================== #
self.beta = 0.3 # Contamination rate
self.sigma = 0.8 # Incubation rate
self.gamma = 0.15 # Recovery rate
self.hp = 0.05 # Hospit rate
self.hcr = 0.2 # Hospit recovery rate
self.pc = 0.1 # Critical rate
self.pd = 0.1 # Critical recovery rate
self.pcr = 0.3 # Critical mortality
# Learning set
self.dataframe = None
self.dataset = None
# Initial state
self.I_0 = 2 # Infected
self.E_0 = 3 # Exposed
self.R_0 = 0 # Recovered
self.S_0 = 1000000 - self.I_0 - self.E_0 # Sensible
self.H_0 = 0
self.C_0 = 0
self.D_0 = 0
self.CT_0 = self.I_0 # Contamined
# ========================================== #
# Hyperparameters dashboard:
# ========================================== #
# Testing protocol
self.s = 0.7 # Sensitivity
self.t = 0.5 # Testing rate in symptomatical
# Importance given to each curve during the fitting process
self.w_1 = 1 # Weight of cumulative positive data
self.w_2 = 0.5 # Weight of hopit data
self.w_3 = 0.5 # Weight of cumul hospit data
self.w_4 = 1 # Weight àf critical data
self.w_5 = 1 # Weight of fatalities data
# Value to return if log(binom.pmf(k,n,p)) = - infinity
self.overflow = - 1000
# Smoothing data or not
self.smoothing = True
# Binomial smoother: ex: if = 2: predicted value *= 2 and p /= 2 WARNING: only use integer
self.binom_smoother = 4
# Binomial smoother use for model scoring:
self.b_s_score = 2
# Optimizer step size
self.opti_step = 0.1
# Optimizer constraints
self.beta_min = 0.1
self.beta_max = 0.9
self.sigma_min = 1/5
self.sigma_max = 1
self.gamma_min = 1/10
self.gamma_max = 1/4
self.hp_min = 0.01
self.hp_max = 0.5
self.hcr_min = 0.01
self.hcr_max = 0.4
self.pc_min = 0.01
self.pc_max = 0.4
self.pd_min = 0.01
self.pd_max = 0.5
self.pcr_min = 0.01
self.pcr_max = 0.4
self.s_min = 0.7
self.s_max = 0.85
self.t_min = 0.5
self.t_max = 1
# Optimizer choise: COBYLA LBFGSB ou AUTO
self.optimizer = 'COBYLA'
# Fit type:
self.fit_type = 'type_1'
def get_parameters(self):
prm = (self.beta, self.sigma, self.gamma, self.hp, self.hcr, self.pc, self.pd, self.pcr)
return prm
def get_hyper_parameters(self):
hprm = (self.s, self.t, self.w_1, self.w_2, self.w_3, self.w_4, self.w_5, self.binom_smoother, self.opti_step, self.optimizer, self.smoothing)
return hprm
def get_initial_state(self, sensib=None, test_rate=None):
"""
Generate an initial state for the model from the dataset
according to the sensitivity and the testing rate to
estimate the true value of the initial state
:param sensib: Sensibility value to use. Use class value if None
:param test_rate: Testing rate value to use. Use class value if None
:return: An array
"""
if sensib is None:
s = self.s
else:
s = sensib
if test_rate is None:
t = self.t
else:
t = test_rate
I_0 = np.round(np.round(self.dataset[0][1] / (s * t)))
H_0 = self.dataset[0][3]
E_0 = I_0
D_0 = 0
C_0 = 0
S_0 = 1000000 - I_0 - H_0 - E_0
R_0 = 0
CT_0 = I_0
CH_0 = H_0
init = (S_0, E_0, I_0, R_0, H_0, C_0, D_0, CT_0, CH_0)
return init
def differential(self, state, time, beta, sigma, gamma, hp, hcr, pc, pd, pcr):
"""
ODE who describe the evolution of the model with the time
:param state: An initial state to use
:param time: A time vector
:return: the evolution of the number of person in each compartiment + cumulative testing rate
+ cumulative entry in hospital
"""
S, E, I, R, H, C, D, CT, CH = state
dS = -(beta * S * I) / (S + I + E + R + H + C + D)
dE = ((beta * S * I) / (S + I + E + R + H + C + D)) - (sigma * E)
dI = (sigma * E) - (gamma * I) - (hp * I)
dH = (hp * I) - (hcr * H) - (pc * H)
dC = (pc * H) - (pd * C) - (pcr * C)
dD = (pd * C)
dR = (gamma * I) + (hcr * H) + (pcr * C)
dCT = sigma * E
dCH = hp * I
return dS, dE, dI, dR, dH, dC, dD, dCT, dCH
def predict(self, duration, initial_state=None, parameters=None):
"""
Predict the evolution of the epidemic during the selected duration from a given initial state
and given parameters
:param duration: Use positive integer value
:param initial_state: Default = use self.get_initial_state()
:param parameters: Default = use self.get_parameters()
:return: a numpy array of 8 columns and t rows
"""
# Time vector:
time = np.arange(duration)
# Parameters to use
prm = parameters
if prm is None:
prm = self.get_parameters()
# Initial state to use:
init = initial_state
if init is None:
init = self.get_initial_state()
# Make prediction:
predict = odeint(func=self.differential,
y0=init,
t=time,
args=(tuple(prm)))
return predict
def fit(self, display=False, step_2=False):
"""
Compute best epidemic parameters values according to model's hyperparameters and the dataset
"""
if self.fit_type == 'type_1' and not step_2:
# Initial values of parameters:
init_prm = (self.beta, self.sigma, self.gamma, self.hp, self.hcr, self.pc, self.pd, self.pcr)
# Time vector:
time = self.dataset[:, 0]
# Bounds
bds = [(self.beta_min, self.beta_max), (self.sigma_min, self.sigma_max), (self.gamma_min, self.gamma_max),
(self.hp_min, self.hp_max), (self.hcr_min, self.hcr_max), (self.pc_min, self.pc_max),
(self.pd_min, self.pd_max), (self.pcr_min, self.pcr_max)]
# Constraint on parameters:
cons = ({'type': 'ineq', 'fun': lambda x: -x[0] + self.beta_max},
{'type': 'ineq', 'fun': lambda x: -x[1] + self.sigma_max},
{'type': 'ineq', 'fun': lambda x: -x[2] + self.gamma_max},
{'type': 'ineq', 'fun': lambda x: -x[3] + self.hp_max},
{'type': 'ineq', 'fun': lambda x: -x[4] + self.hcr_max},
{'type': 'ineq', 'fun': lambda x: -x[5] + self.pc_max},
{'type': 'ineq', 'fun': lambda x: -x[6] + self.pd_max},
{'type': 'ineq', 'fun': lambda x: -x[7] + self.pcr_max},
{'type': 'ineq', 'fun': lambda x: x[0] - self.beta_min},
{'type': 'ineq', 'fun': lambda x: x[1] - self.sigma_min},
{'type': 'ineq', 'fun': lambda x: x[2] - self.gamma_min},
{'type': 'ineq', 'fun': lambda x: x[3] - self.hp_min},
{'type': 'ineq', 'fun': lambda x: x[4] - self.hcr_min},
{'type': 'ineq', 'fun': lambda x: x[5] - self.pc_min},
{'type': 'ineq', 'fun': lambda x: x[6] - self.pd_min},
{'type': 'ineq', 'fun': lambda x: x[7] - self.pcr_min})
# Optimizer
res = None
if self.optimizer == 'LBFGSB':
res = minimize(self.objective, np.asarray(init_prm),
method='L-BFGS-B',
options={'eps': self.opti_step},
constraints=cons,
bounds=bds,
args=('method_1', False, display))
else:
if self.optimizer == 'COBYLA':
res = minimize(self.objective, np.asarray(init_prm),
method='COBYLA',
args=('method_1', False, display),
constraints=cons)
else: # Auto
res = minimize(self.objective, np.asarray(init_prm),
constraints=cons,
options={'eps': self.opti_step},
args=('method_1, False, display'))
if display:
# Print optimizer result
print(res)
# Update model parameters:
self.beta = res.x[0]
self.sigma = res.x[1]
self.gamma = res.x[2]
self.hp = res.x[3]
self.hcr = res.x[4]
self.pc = res.x[5]
self.pd = res.x[6]
self.pcr = res.x[7]
if step_2:
# Initial values of parameters:
init_prm = (self.beta, self.sigma, self.gamma, self.hp, self.s, self.t)
# Time vector:
time = self.dataset[:, 0]
# Bounds
bds = [(self.beta_min, self.beta_max), (self.sigma_min, self.sigma_max),
(self.gamma_min, self.gamma_max),
(self.hp_min, self.hp_max), (self.s_min, self.s_max), (self.t_min, self.t_max)]
# Constraint on parameters:
cons = ({'type': 'ineq', 'fun': lambda x: -x[0] + self.beta_max},
{'type': 'ineq', 'fun': lambda x: -x[1] + self.sigma_max},
{'type': 'ineq', 'fun': lambda x: -x[2] + self.gamma_max},
{'type': 'ineq', 'fun': lambda x: -x[3] + self.hp_max},
{'type': 'ineq', 'fun': lambda x: -x[4] + self.s_max},
{'type': 'ineq', 'fun': lambda x: -x[5] + self.t_max},
{'type': 'ineq', 'fun': lambda x: x[0] - self.beta_min},
{'type': 'ineq', 'fun': lambda x: x[1] - self.sigma_min},
{'type': 'ineq', 'fun': lambda x: x[2] - self.gamma_min},
{'type': 'ineq', 'fun': lambda x: x[3] - self.hp_min},
{'type': 'ineq', 'fun': lambda x: x[4] - self.s_min},
{'type': 'ineq', 'fun': lambda x: x[5] - self.s_min})
# Optimizer
res = None
if self.optimizer == 'LBFGSB':
res = minimize(self.objective, np.asarray(init_prm),
method='L-BFGS-B',
options={'eps': self.opti_step},
constraints=cons,
bounds=bds,
args=('method_2', False, display))
else:
if self.optimizer == 'COBYLA':
res = minimize(self.objective, np.asarray(init_prm),
method='COBYLA',
args=('method_2', False, display),
constraints=cons)
else: # Auto
res = minimize(self.objective, np.asarray(init_prm),
constraints=cons,
options={'eps': self.opti_step},
args=('method_2', False, display))
if display:
# Print optimizer result
print(res)
# Update model parameters:
self.beta = res.x[0]
self.sigma = res.x[1]
self.gamma = res.x[2]
self.hp = res.x[3]
self.s = res.x[4]
self.t = res.x[5]
def fit_rates(self):
# Valeurs de départ:
init_prm = [self.t * self.s]
cons = ({'type': 'ineq', 'fun': lambda x: -x[0] + 0.85},
{'type': 'ineq', 'fun': lambda x: x[0] - 0.3})
res = minimize(self.fit_rate_objectif, np.asarray(init_prm),
method='COBYLA',
constraints=cons)
print('=========================================')
print('Fit rate result:')
print(res)
def fit_rate_objectif(self, parameters):
model = SEIR()
model.set_param()
model.s = 1
model.t = parameters[0]
model.import_dataset()
model.fit()
score = model.score(output='sum_tot')
print('score for t= {}: {} '.format(parameters[0], - score))
return - score
def objective(self, parameters, method, print_details=False, display=False):
"""
The objective function to minimize during the fitting process.
These function compute the probability of each observed values accroding to predictions
take the logarighm value and make the sum.
"""
if method == 'method_1':
# Make predictions:
params = tuple(parameters)
init_state = self.get_initial_state()
if display:
print(params)
pred = self.predict(duration=self.dataset.shape[0],
parameters=params,
initial_state=init_state)
# Uncumul positive test:
uncumul = []
uncumul.append(pred[0][7])
for i in range(1, pred.shape[0]):
uncumul.append(pred[i][7] - pred[i-1][7])
# Compare with dataset:
prb = 0
for i in range(0, pred.shape[0]):
p_k1 = p_k2 = p_k3 = p_k4 = p_k5 = self.overflow
# ======================================= #
# PART 1: Fit on positive test
# ======================================= #
pa = self.s * self.t
#n = np.around(uncumul[i] * pa)
n = np.around(uncumul[i])
k = self.dataset[i][1]
p = 1 / self.binom_smoother
if k < 0 and n < 0:
k *= -1
n *= -1
if k > n:
tmp = n
n = k
k = tmp
if k < 0:
n += - k + 1
k = 1
#n *= self.binom_smoother
prob = binom.pmf(k=k, n=n, p=p)
if prob > 0:
p_k1 = np.log(binom.pmf(k=k, n=n, p=pa))
else:
p_k1 = self.overflow
if p_k1 == - math.inf:
p_k1 = - 1000
prb -= p_k1 * self.w_1
# ======================================= #
# PART 2: Fit on hospit
# ======================================= #
n = np.around(pred[i][4])
k = self.dataset[i][3]
p = 1 / self.binom_smoother
if k < 0 and n < 0:
k *= -1
n *= -1
if k > n:
tmp = n
n = k
k = tmp
if k < 0:
n += - k + 1
k = 1
n *= self.binom_smoother
prob = binom.pmf(k=k, n=n, p=p)
if prob > 0:
p_k2 = np.log(binom.pmf(k=k, n=n, p=p))
else:
p_k2 = self.overflow
prb -= p_k2 * self.w_2
# ======================================= #
# PART 3: Fit on cumul hospit
# ======================================= #
n = np.around(pred[i][8])
k = self.dataset[i][4]
p = 1 / self.binom_smoother
if k < 0 and n < 0:
k *= -1
n *= -1
if k > n:
tmp = n
n = k
k = tmp
if k < 0:
n += - k + 1
k = 1
n *= self.binom_smoother
prob = binom.pmf(k=k, n=n, p=p)
if prob > 0:
p_k3 = np.log(binom.pmf(k=k, n=n, p=p))
else:
p_k3 = self.overflow
prb -= p_k3 * self.w_3
# ======================================= #
# Part 4: Fit on Critical
# ======================================= #
n = np.around(pred[i][5])
k = self.dataset[i][5]
p = 1 / self.binom_smoother
if k < 0 and n < 0:
k *= -1
n *= -1
if k > n:
tmp = n
n = k
k = tmp
if k < 0:
n += - k + 1
k = 1
n *= self.binom_smoother
prob = binom.pmf(k=k, n=n, p=p)
if prob > 0:
p_k4 = np.log(binom.pmf(k=k, n=n, p=p))
else:
p_k4 = self.overflow
prb -= p_k4 * self.w_4
# ======================================= #
# Part 5: Fit on Fatalities
# ======================================= #
n = np.around(pred[i][6])
k = self.dataset[i][6]
p = 1 / self.binom_smoother
if k < 0 and n < 0:
k *= -1
n *= -1
if k > n:
tmp = n
n = k
k = tmp
if k < 0:
n += - k + 1
k = 1
n *= self.binom_smoother
prob = binom.pmf(k=k, n=n, p=p)
if prob > 0:
p_k5 = np.log(binom.pmf(k=k, n=n, p=p))
else:
p_k5 = self.overflow
prb -= p_k5 * self.w_5
if print_details:
print('iter {}: {} - {} - {} - {} - {} - {}'.format(i, p_k1, p_k2, p_k3, p_k4, p_k5, p_k6))
print('test+ cumul: {} - {}'.format(np.around(pred[i][7] * params[8] * params[9]), self.dataset[i][7]))
print('hospit: {} - {}'.format(np.around(pred[i][4]), self.dataset[i][3]))
print('hospit cumul: {} - {}'.format(np.around(pred[i][8]), self.dataset[i][4]))
print('critical: {} - {}'.format(np.around(pred[i][5]), self.dataset[i][5]))
print('Fatalities: {} - {}'.format(np.around(pred[i][6]), self.dataset[i][6]))
if display:
print(prb)
return prb
if method == 'method_2':
# Make predictions:
tpl = tuple(parameters)
params = (tpl[0], tpl[1], tpl[2], tpl[3], self.hcr, self.pc, self.pd, self.pcr)
init_state = self.get_initial_state(sensib=tpl[-2], test_rate=tpl[-1])
if display:
print(params)
pred = self.predict(duration=self.dataset.shape[0],
parameters=params,
initial_state=init_state)
# Uncumul positive test:
uncumul = []
uncumul.append(pred[0][7])
for i in range(1, pred.shape[0]):
uncumul.append(pred[i][7] - pred[i-1][7])
# Compare with dataset:
prb = 0
for i in range(0, pred.shape[0]):
p_k1 = p_k2 = p_k3 = p_k4 = p_k5 = self.overflow
# ======================================= #
# PART 1: Fit on positive test
# ======================================= #
pa = tpl[-2] * tpl[-1]
n = np.around(uncumul[i] * pa)
k = self.dataset[i][1]
p = 1 / self.binom_smoother
if k < 0 and n < 0:
k *= -1
n *= -1
if k > n:
tmp = n
n = k
k = tmp
if k < 0:
n += - k + 1
k = 1
n *= self.binom_smoother
prob = binom.pmf(k=k, n=n, p=p)
if prob > 0:
p_k1 = np.log(binom.pmf(k=k, n=n, p=p))
else:
p_k1 = self.overflow
prb -= p_k1 * self.w_1
# ======================================= #
# PART 2: Fit on hospit
# ======================================= #
n = np.around(pred[i][4])
k = self.dataset[i][3]
p = 1 / self.binom_smoother
if k < 0 and n < 0:
k *= -1
n *= -1
if k > n:
tmp = n
n = k
k = tmp
if k < 0:
n += - k + 1
k = 1
n *= self.binom_smoother
prob = binom.pmf(k=k, n=n, p=p)
if prob > 0:
p_k2 = np.log(binom.pmf(k=k, n=n, p=p))
else:
p_k2 = self.overflow
prb -= p_k2 * self.w_2
# ======================================= #
# PART 3: Fit on cumul hospit
# ======================================= #
n = np.around(pred[i][8])
k = self.dataset[i][4]
p = 1 / self.binom_smoother
if k < 0 and n < 0:
k *= -1
n *= -1
if k > n:
tmp = n
n = k
k = tmp
if k < 0:
n += - k + 1
k = 1
n *= self.binom_smoother
prob = binom.pmf(k=k, n=n, p=p)
if prob > 0:
p_k3 = np.log(binom.pmf(k=k, n=n, p=p))
else:
p_k3 = self.overflow
prb -= p_k3 * self.w_3
# ======================================= #
# Part 4: Fit on Critical
# ======================================= #
n = np.around(pred[i][5])
k = self.dataset[i][5]
p = 1 / self.binom_smoother
if k < 0 and n < 0:
k *= -1
n *= -1
if k > n:
tmp = n
n = k
k = tmp
if k < 0:
n += - k + 1
k = 1
n *= self.binom_smoother
prob = binom.pmf(k=k, n=n, p=p)
if prob > 0:
p_k4 = np.log(binom.pmf(k=k, n=n, p=p))
else:
p_k4 = self.overflow
prb -= p_k4 * self.w_4
# ======================================= #
# Part 5: Fit on Fatalities
# ======================================= #
n = np.around(pred[i][6])
k = self.dataset[i][6]
p = 1 / self.binom_smoother
if k < 0 and n < 0:
k *= -1
n *= -1
if k > n:
tmp = n
n = k
k = tmp
if k < 0:
n += - k + 1
k = 1
n *= self.binom_smoother
prob = binom.pmf(k=k, n=n, p=p)
if prob > 0:
p_k5 = np.log(binom.pmf(k=k, n=n, p=p))
else:
p_k5 = self.overflow
prb -= p_k5 * self.w_5
if print_details:
print('iter {}: {} - {} - {} - {} - {} - {}'.format(i, p_k1, p_k2, p_k3, p_k4, p_k5, p_k6))
print('test+ cumul: {} - {}'.format(np.around(pred[i][7] * params[8] * params[9]), self.dataset[i][7]))
print('hospit: {} - {}'.format(np.around(pred[i][4]), self.dataset[i][3]))
print('hospit cumul: {} - {}'.format(np.around(pred[i][8]), self.dataset[i][4]))
print('critical: {} - {}'.format(np.around(pred[i][5]), self.dataset[i][5]))
print('Fatalities: {} - {}'.format(np.around(pred[i][6]), self.dataset[i][6]))
if display:
print(prb)
return prb
def score(self, output='raw'):
# Get parameters
params = tuple(self.get_parameters())
# Get Initial state
init_state = self.get_initial_state()
pred = self.predict(duration=self.dataset.shape[0],
parameters=params,
initial_state=init_state)
# Uncumul positive test:
uncumul = []
uncumul.append(pred[0][7])
for i in range(1, pred.shape[0]):
uncumul.append(pred[i][7] - pred[i - 1][7])
# Store raw result:
raw = np.zeros((pred.shape[0], 4))
# Compare with dataset:
for i in range(0, pred.shape[0]):
p_k1 = p_k2 = p_k3 = p_k4 = self.overflow
# ======================================= #
# PART 1: Compare positives
# ======================================= #
n = np.around(uncumul[i] * self.s * self.t)
k = self.dataset[i][1]
p = 1 / self.b_s_score
if k < 0 and n < 0:
k *= -1
n *= -1
if k > n:
tmp = n
n = k
k = tmp
if k < 0:
n += - k + 1
k = 1
n *= self.b_s_score
prob = binom.pmf(k=k, n=n, p=p)
if prob > 0:
p_k1 = np.log(binom.pmf(k=k, n=n, p=p))
else:
p_k1 = self.overflow
raw[i][0] = p_k1
# ======================================= #
# PART 2: Compare on Hospit cumul
# ======================================= #
n = np.around(pred[i][8])
k = self.dataset[i][4]
p = 1 / self.b_s_score
if k < 0 and n < 0:
k *= -1
n *= -1
if k > n:
tmp = n
n = k
k = tmp
if k < 0:
n += - k + 1
k = 1
n *= self.b_s_score
prob = binom.pmf(k=k, n=n, p=p)
if prob > 0:
p_k2 = np.log(binom.pmf(k=k, n=n, p=p))
else:
p_k2 = self.overflow
raw[i][1] = p_k2
# ======================================= #
# Part 3: Compare criticals
# ======================================= #
n = np.around(pred[i][5])
k = self.dataset[i][5]
p = 1 / self.b_s_score
if k < 0 and n < 0:
k *= -1
n *= -1
if k > n:
tmp = n
n = k
k = tmp
if k < 0:
n += - k + 1
k = 1
n *= self.b_s_score
prob = binom.pmf(k=k, n=n, p=p)
if prob > 0:
p_k3 = np.log(binom.pmf(k=k, n=n, p=p))
else:
p_k3 = self.overflow
raw[i][2] = p_k3
# ======================================= #
# Part 4: Compare fatalities
# ======================================= #
n = np.around(pred[i][6])
k = self.dataset[i][6]
p = 1 / self.b_s_score
if k < 0 and n < 0:
k *= -1
n *= -1
if k > n:
tmp = n
n = k
k = tmp
if k < 0:
n += - k + 1
k = 1
n *= self.b_s_score
prob = binom.pmf(k=k, n=n, p=p)
if prob > 0:
p_k4 = np.log(binom.pmf(k=k, n=n, p=p))
else:
p_k4 = self.overflow
raw[i][3] = p_k4
if output == 'raw':
return raw
if output == 'sum_tot':
return np.sum(raw)
def import_dataset(self):
url = "https://raw.githubusercontent.com/ADelau/proj0016-epidemic-data/main/data.csv"
# Import the dataframe:
raw = pd.read_csv(url, sep=',', header=0)
raw['num_positive'][0] = 1
# Ad a new column at the end with cumulative positive cases at the right
cumul_positive = np.copy(raw['num_positive'].to_numpy())
for i in range(1, len(cumul_positive)):
cumul_positive[i] += cumul_positive[i-1]
raw.insert(7, 'cumul_positive', cumul_positive)
if self.smoothing:
self.dataframe = dataframe_smoothing(raw)
else: self.dataframe = raw
self.dataset = self.dataframe.to_numpy()
self.I_0 = self.dataset[0][1] / (self.s * self.t)
self.E_0 = self.I_0 * 5
self.R_0 = 0
self.S_0 = 1000000 - self.I_0 - self.E_0
def set_param(self):
"""
Set the actual best values of parameters:
Note: header for the result file:
sum_tot;beta;sigma;gamma;hp;hcr;pc;pd;pcr;sensib;test_rate;w1;w2;w3;w4;w5;binom_smoother;opti_step;optimizer;smoothing;mean_tot_bis;sum_tot;std_tot;mean_test;sum_test;std_test;mean_hospit;sum_hospit;std_hospit;mean_critical;sum_critical;std_critical;mean_fata;sum_fata;std_fata
"""
# Epidemic parameters:
#self.beta = 0.453638
#self.sigma = 0.985727
#self.gamma = 0.238646
#self.hp = 0.0207093
#self.hcr = 0.0313489
#self.pc = 0.0776738
#self.pd = 0.0417785
#self.pcr = 0.244847
self.beta = 0.453638
self.sigma = 0.885727
self.gamma = 0.208646
self.hp = 0.0207093
self.hcr = 0.0313489
self.pc = 0.0776738
self.pd = 0.0417785
self.pcr = 0.244847
# Hyper parameters:
self.s = 0.74668015
self.t = 0.891375
self.w_1 = 2
self.w_2 = 2
self.w_3 = 1
self.w_4 = 1
self.w_5 = 1
self.binom_smoother = 4
self.opti_step = 0.1
self.optimizer = 'COBYLA'
self.smoothing = False
def valid_result_analysis():
# Import validation result:
result = pd.read_csv('validation_result.csv', sep=';')
result.sort_values(by=['sum_tot', 'std_tot'], inplace=True, ignore_index=True, ascending=False)
print(result)
# Numpy version:
npr = result.to_numpy()
# exec:
for i in range(0, npr.shape[0]):
# Create a model:
model = SEIR()
# Load parameters:
model.beta = npr[i][1]
model.sigma = npr[i][2]
model.gamma = npr[i][3]
model.hp = npr[i][4]
model.hcr = npr[i][5]
model.pc = npr[i][6]
model.pd = npr[i][7]
model.pcr = npr[i][8]
model.s = npr[i][9]
model.t = npr[i][10]
model.optimizer = 'COBYLA'
model.smoothing = False
# Import dataset:
model.import_dataset()
# Make predictions:
predictions = model.predict(duration=model.dataset.shape[0])
# Uncumul
uncumul = []
uncumul.append(predictions[0][7])
for j in range(1, predictions.shape[0]):
uncumul.append(predictions[j][7] - predictions[j - 1][7])
# Plot:
time = model.dataset[:, 0]
# Adapt test + with sensit and testing rate
for j in range(0, len(time)):
uncumul[j] = uncumul[j] * model.s * model.t
# Plot cumul positive
plt.scatter(time, model.dataset[:, 1], c='blue', label='test+')
plt.plot(time, uncumul, c='blue', label='test+')
# Plot hospit
plt.scatter(time, model.dataset[:, 4], c='red', label='hospit cumul pred')
plt.plot(time, predictions[:, 8], c='red', label='pred hopit cumul')
plt.legend()
plt.title('index {}'.format(i))
plt.show()
# Plot critical
plt.scatter(time, model.dataset[:, 5], c='green', label='critical data')
plt.plot(time, predictions[:, 5], c='green', label='critical pred')
plt.scatter(time, model.dataset[:, 6], c='black', label='fatalities data')
plt.plot(time, predictions[:, 6], c='black', label='fatalities pred')
plt.legend()
plt.title('index {}'.format(i))
plt.show()
print('---------------------------------------------------------')
row = result.loc[i, :]
print(row)
print("<Press enter/return to continue>")
input()
def first():
# Create the model:
model = SEIR()
# Import dataset:
model.import_dataset()
model.set_param()
# Fit:
model.fit(display=True)
model.fit(step_2=True, display=True)
params = model.get_parameters()
# Make a prediction:
prd = model.predict(model.dataset.shape[0], parameters=params)
# Uncumul:
uncumul = []
uncumul.append(prd[0][7])
for i in range(1, prd.shape[0]):
uncumul.append(prd[i][7] - prd[i-1][7])
for i in range(0, prd.shape[0]):
uncumul[i] = uncumul[i] * model.s * model.t
print('=== For positif: ')
for i in range(0, 10):
print('dataset: {}, predict = {}'.format(model.dataset[i, 1], uncumul[i]))
print('=== For hospit: ')
for i in range(0, 10):
print('dataset: {}, predict = {}'.format(model.dataset[i, 3], prd[i][4]))
print('=== E values: ')
#print(prd[:, 1])
# Plot
plt.scatter(model.dataset[:, 0], model.dataset[:, 1], c='blue', label='testing data')
plt.scatter(model.dataset[:, 0], model.dataset[:, 4], c='green', label='hospit_cum')
plt.plot(model.dataset[:, 0], prd[:, 4], c='yellow', label='hospit cum pred')
plt.plot(model.dataset[:, 0], uncumul, c='red', label='predictions')
plt.legend()
plt.show()
plt.scatter(model.dataset[:, 0], model.dataset[:, 5], c='blue', label='critical data')
plt.plot(model.dataset[:, 0], prd[:, 5], c='red', label='critical prediction')
plt.scatter(model.dataset[:, 0], model.dataset[:, 6], c='yellow', label='dead data')
plt.plot(model.dataset[:, 0], prd[:, 6], c='green', label='dead predict')
plt.legend()
plt.show()
def sec():
# Create the model:
model = SEIR()
# Set best parameters:
model.set_param()
# Import dataset:
model.import_dataset()
#model.fit_rates()
# Make predictions: