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:]