gamma1, gamma2 = 0.5, 0.5
data = dict()
countries = ["Italy", "Canada", "Serbia", "Ukraine", "Egypt", "Peru"]
r = 1.3
rV = 1.0 / 100
vaccination_strategy = "old_first"
model = "vaccine_rate"
for country in countries:
print(country)
data[country] = dict()
# import country
country_dict = import_country(country, "../../data/countries/")
# initial conditions
ics = np.zeros((16, 20))
for k in range(16):
ics[k, 4] = eps**(-1) / (mu**(-1) + omega**(-1) +
eps**(-1)) * i0 * country_dict["Nk"][k]
ics[k, 5] = omega**(-1) / (mu**(-1) + omega**(-1) +
eps**(-1)) * i0 * country_dict["Nk"][k]
ics[k, 6] = f * mu**(-1) / (mu**(-1) + omega**(-1) +
eps**(-1)) * i0 * country_dict["Nk"][k]
ics[k,
7] = (1 - f) * mu**(-1) / (mu**(-1) + omega**(-1) +
eps**(-1)) * i0 * country_dict["Nk"][k]
ics[k, 9] = r0 * country_dict["Nk"][k]
ics[k, 0] = country_dict["Nk"][k] - ics[k, 4] - ics[k, 5] - ics[
r = 1.0
alpha, gamma, rV, VES, VEM = 0.0, 0.0, 0.0, 0.0, 0.0
# number of compartment and age groups
ncomp = 20
nage = 16
# parse basin name
#parser = argparse.ArgumentParser(description='Optional app description')
#parser.add_argument('basin', type=str, help='name of the basin')
#args = parser.parse_args()
#basin = args.basin
basin = "Italy"
# import country
country_dict = import_country(basin, "../../data/countries/")
# I0
new_pos = country_dict["cases"].loc[country_dict["cases"]["year_week"] ==
"2020-35"]["weekly_count"].values[0]
# R(t=0)
r0 = get_totR("../../data/", start_date,
country_dict["country"]) / country_dict["Nk"].sum()
# pre-compute contacts matrices
Cs = {}
date, dates = start_date, [start_date]
for i in range((end_date - start_date).days):
Cs[date] = update_contacts(country_dict, date)
date += timedelta(days=1)
