k, 6] - ics[k, 7] - ics[k, 9]
# compute contacts matrices
Cs = {}
C = country_dict["contacts_home"] + country_dict["contacts_work"] + \
country_dict["contacts_school"] + country_dict["contacts_other_locations"]
date, dates = start_date, [start_date]
for i in range((end_date - start_date).days):
Cs[date] = C
date += timedelta(days=1)
dates.append(date)
# run the baseline solution with no vaccine
sol_baseline, Vt, ws = integrate_BV(ics, (end_date - start_date).days, R0,
eps, mu, omega, chi, f, IFR, Delta,
1.0, 0.0, 0.0, 0.0, 0.0, 0.0, Cs,
country_dict["Nk"], "homogeneous",
"vaccine_rate", dates)
# run different rollout speeds
for rV in rVs:
print("\t", rV)
data[country][rV] = dict()
# run different r value
for r in rs:
print("\t\t", r)
data[country][rV][r] = dict()
# run different models
for model in models:
k, 6] - ics[k, 7] - ics[k, 9]
# compute contacts matrices
Cs = {}
C = country_dict["contacts_home"] + country_dict["contacts_work"] + \
country_dict["contacts_school"] + country_dict["contacts_other_locations"]
date, dates = start_date, [start_date]
for i in range((end_date - start_date).days):
Cs[date] = C
date += timedelta(days=1)
dates.append(date)
# run the baseline (no behavior)
sol_baseline, Vt, vs = integrate_BV(ics, (end_date - start_date).days, R0,
eps, mu, omega, chi, f, IFR, Delta,
1.0, 0.0, 0.0, 0.0, 0.0, 0.0, Cs,
country_dict["Nk"], "old_first",
"vaccine_rate", dates)
# run different rollout speeds
for gamma in gammas:
print("\t", gamma)
data[country][gamma] = dict()
# run different strategies
for strategy in vaccination_strategies:
print("\t\t", strategy)
data[country][gamma][strategy] = []
# run different alphas
for i in range(len(alpha_s)):
ics[k, 9] = r0 * country_dict["Nk"][k]
ics[k, 0] = country_dict["Nk"][k] - ics[k, 4] - ics[k, 5] - ics[
k, 6] - ics[k, 7] - ics[k, 9]
R0 = np.random.uniform(0.8, 2.2)
Delta = np.random.randint(14, 25)
solution, Vt, vs = integrate_BV(ics, (end_date - start_date).days,
R0,
eps,
mu,
omega,
chi,
f,
IFR,
Delta,
r,
alpha,
gamma,
rV,
VES,
VEM,
Cs,
country_dict["Nk"],
vaccination_strategy="homogeneous",
model="vaccine_rate",
dates=dates)
# sum over age and create df
solution_age = solution.sum(axis=0)
df_deaths = pd.DataFrame(data={
"deaths": np.diff(solution_age[11]),
"dates": dates[1:]
