def plot_model(contact_matrix,
susceptible,
infectious_rate,
days,
reference_cases=None,
measure_factor: float = 1,
measure_day=0,
reference_hospital=None,
offset=0,
first_patient_age=38,
name='model',
scenario=None,
cap_ic=False,
title='') -> Model:
model = Model(contact_matrix,
susceptible,
infectious_rate,
measure_factor=measure_factor,
measure_day=measure_day,
first_patient_age=first_patient_age,
scenario=scenario,
cap_ic=cap_ic)
model.run(days)
y = [
model.infected_data, model.recovered_data, model.exposed_data,
model.hospital_data, model.ic_data, model.dead_data
]
labels = ['infected', 'recovered', 'exposed', 'hospitalized', 'ic', 'dead']
plot(y, labels, name, 'Amount', title=title)
if reference_cases is not None:
y = [reference_cases, model.case_data]
labels = ['Reference Cases', 'Model Cases']
plot(y,
labels,
'ref_cases',
'Amount',
title='Verschil model werkelijkheid')
if reference_hospital is not None:
y = [
np.append(np.zeros(offset), reference_hospital),
model.hospital_data + model.ic_data
]
labels = ['Reference Hospital + ic', 'Model Hospital + ic']
plot(y,
labels,
'ref_hospital',
'Amount',
title='Verschil model werkelijkheid')
return model
def optimal_fit(reference_infected, infectious_rate, contact_matrix,
susceptible, num_days):
# initial model, do once with higher infectious rate, once with lower, continue with best fit
inf_rate_diff = 0.001
model = Model(contact_matrix, susceptible, infectious_rate + inf_rate_diff)
model.run(num_days)
residual = np.absolute(model.case_data - reference_infected)
pos_sum_of_squares = np.sum(residual**2)
inf_rate_diff = -0.001
model = Model(contact_matrix, susceptible, infectious_rate + inf_rate_diff)
model.run(num_days)
residual = np.absolute(model.case_data - reference_infected)
neg_sum_of_squares = np.sum(residual**2)
if neg_sum_of_squares < pos_sum_of_squares:
infectious_rate -= 0.001
prev_sum_of_squares = neg_sum_of_squares
while True:
infectious_rate -= 0.001
model = Model(contact_matrix, susceptible, infectious_rate)
model.run(num_days)
residual = np.absolute(model.case_data - reference_infected)
sum_of_squares = np.sum(residual**2)
if sum_of_squares > prev_sum_of_squares or infectious_rate <= 0 or infectious_rate >= 1:
break
else:
prev_sum_of_squares = sum_of_squares
else:
infectious_rate += 0.001
prev_sum_of_squares = pos_sum_of_squares
while True:
infectious_rate += 0.001
model = Model(contact_matrix, susceptible, infectious_rate)
model.run(num_days)
residual = np.absolute(model.case_data - reference_infected)
sum_of_squares = np.sum(residual**2)
if sum_of_squares > prev_sum_of_squares or infectious_rate <= 0 or infectious_rate >= 1:
break
else:
prev_sum_of_squares = sum_of_squares
return infectious_rate
def calculate_sum_of_squares(contact_matrix, days, infectious_rate,
measure_day, offset, reference_hospital,
susceptible, first_patient_age):
num_days = days + offset
factor = 0.2
factor_diff = 0.001
model = Model(contact_matrix,
susceptible,
infectious_rate,
measure_factor=factor + factor_diff,
measure_day=measure_day,
first_patient_age=first_patient_age)
model.run(num_days)
temp = model.hospital_data + model.ic_data
residual = np.absolute(temp[offset:] - reference_hospital)
pos_sum_of_squares = np.sum(residual**2)
factor_diff = -0.001
model = Model(contact_matrix,
susceptible,
infectious_rate,
measure_factor=factor + factor_diff,
measure_day=measure_day,
first_patient_age=first_patient_age)
model.run(num_days)
temp = model.hospital_data + model.ic_data
residual = np.absolute(temp[offset:] - reference_hospital)
neg_sum_of_squares = np.sum(residual**2)
if neg_sum_of_squares < pos_sum_of_squares:
factor_diff -= 0.001
prev_sum_of_squares = neg_sum_of_squares
while True:
factor_diff -= 0.001
model = Model(contact_matrix,
susceptible,
infectious_rate,
measure_factor=factor + factor_diff,
measure_day=measure_day,
first_patient_age=first_patient_age)
model.run(num_days)
temp = model.hospital_data + model.ic_data
residual = np.absolute(temp[offset:] - reference_hospital)
sum_of_squares = np.sum(residual**2)
if sum_of_squares > prev_sum_of_squares or infectious_rate <= 0 or infectious_rate >= 1:
factor_diff += 0.001
break
else:
prev_sum_of_squares = sum_of_squares
else:
factor_diff += 0.001
prev_sum_of_squares = pos_sum_of_squares
while True:
factor_diff += 0.001
model = Model(contact_matrix,
susceptible,
infectious_rate,
measure_factor=factor + factor_diff,
measure_day=measure_day,
first_patient_age=first_patient_age)
model.run(num_days)
temp = model.hospital_data + model.ic_data
residual = np.absolute(temp[offset:] - reference_hospital)
sum_of_squares = np.sum(residual**2)
if sum_of_squares > prev_sum_of_squares:
factor_diff -= 0.001
break
else:
prev_sum_of_squares = sum_of_squares
return factor + factor_diff, prev_sum_of_squares
