def cost_function(x):
(a, b, c, d, e, f) = x
v = a * 10**(-b)
K_r_0 = c * 10**(-d)
K_d_0 = e * 10**(-f)
time_sim, cases_sim, healthy_sim, recovered_sim, deaths_sim \
= calculate_epidemic(
C=0, v=v, x_n=x_n, y_n=y_n, t_final=max(time_number_days),
K_r_0=K_r_0, K_r_minus=0,
K_d_0=K_d_0, K_d_plus=0)
interp_cases = interpolate.interp1d(time_sim,
cases_sim,
fill_value='extrapolate')
interp_deaths = interpolate.interp1d(time_sim,
deaths_sim,
fill_value='extrapolate')
fitness = 0
N = 0
for i in range(len(time_number_days)):
fitness += (
abs(deaths_ref[i] - interp_deaths(time_number_days[i])) /
max(deaths_ref))
fitness += (abs(cases_ref[i] - interp_cases(time_number_days[i])) /
max(cases_ref))
N += 2
fitness /= N
print("Fit mean difference: " + str(fitness), end='\r')
return fitness
def fit_country(country, save_to_json=False):
def cost_function(x):
(a, b, c, d, e, f) = x
v = a * 10**(-b)
K_r_0 = c * 10**(-d)
K_d_0 = e * 10**(-f)
time_sim, cases_sim, healthy_sim, recovered_sim, deaths_sim \
= calculate_epidemic(
C=0, v=v, x_n=x_n, y_n=y_n, t_final=max(time_number_days),
K_r_0=K_r_0, K_r_minus=0,
K_d_0=K_d_0, K_d_plus=0)
interp_cases = interpolate.interp1d(time_sim,
cases_sim,
fill_value='extrapolate')
interp_deaths = interpolate.interp1d(time_sim,
deaths_sim,
fill_value='extrapolate')
fitness = 0
N = 0
for i in range(len(time_number_days)):
fitness += (
abs(deaths_ref[i] - interp_deaths(time_number_days[i])) /
max(deaths_ref))
fitness += (abs(cases_ref[i] - interp_cases(time_number_days[i])) /
max(cases_ref))
N += 2
fitness /= N
print("Fit mean difference: " + str(fitness), end='\r')
return fitness
x_n = 1e5 # initial healthy population arbitrary
time, time_number_days, cases_ref, deaths_ref = get_data(country)
y_n = cases_ref[0]
x0 = (2.78, 6.08, 25, 1.9, 1, 2)
print('Fitting...')
res = scipy.optimize.minimize(cost_function, x0, method="Nelder-Mead")
x_opt = res.x
print('-' * 50)
(a, b, c, d, e, f) = x_opt
v = a * 10**(-b)
K_r_0 = c * 10**(-d)
K_d_0 = e * 10**(-f)
print("Fit mean difference: " + "{:.2%}".format(res.fun))
print("K_c = %.2e" % v)
print("K_r = %.2e" % K_r_0)
print("K_d = %.2e" % K_d_0)
time_sim, cases_sim, healthy_sim, recovered_sim, deaths_sim \
= calculate_epidemic(
C=0, v=v, x_n=x_n, y_n=y_n, t_final=60, K_r_0=K_r_0, K_r_minus=0,
K_d_0=K_d_0, K_d_plus=0)
if save_to_json is True:
print("Saving...")
save_data(country, time, time_sim, cases_sim, deaths_sim)
return time_sim, cases_sim, healthy_sim, recovered_sim, deaths_sim