cfr_over_time = np.zeros((num_runs, int(params['T']))) fraction_over_70_time = np.zeros((num_runs, int(params['T']))) fraction_below_30_time = np.zeros((num_runs, int(params['T']))) median_age_time = np.zeros((num_runs, int(params['T']))) total_infections_time = np.zeros((num_runs, int(params['T']))) total_documented_time = np.zeros((num_runs, int(params['T']))) dead_by_age = np.zeros((num_runs, n_ages)) total_deaths_time = np.zeros((num_runs, int(params['T']))) all_final_s = np.zeros((num_runs, int(params['n']))) for i in range(num_runs): print(i) S, E, Mild, Documented, Severe, Critical, R, D, Q, num_infected_by, time_documented, \ time_to_activation, time_to_death, time_to_recovery, time_critical, time_exposed, \ num_infected_asympt, age, time_infected, time_to_severe \ = run_complete_simulation(seed + i,country, contact_matrix, p_mild_severe, p_severe_critical, \ p_critical_death, mean_time_to_isolate_factor, \ lockdown_factor_age, p_infect_household, fraction_stay_home, params, load_population) #This is all for analyzing the age distribution of infection, CFR over time, etc. for t in range(T): if (time_exposed == t).sum() > 0: r_0_over_time[i, t] = num_infected_by[time_exposed == t].mean() cfr_over_time[i, t] = D[-1, time_exposed == t].sum() / ( D[-1, time_exposed == t].sum() + R[-1, time_exposed == t].sum()) fraction_over_70_time[i, t] = (age[time_exposed == t] >= 70).mean() fraction_below_30_time[i, t] = (age[time_exposed == t] < 30).mean() median_age_time[i, t] = np.median(age[time_exposed == t]) total_infections_time[i] = (params['n'] - S.sum(axis=1) - E.sum(axis=1)) total_documented_time[i] = Documented.sum(axis=1) for patient_age in range(n_ages):
Mild_per_time = np.zeros((N_SIMS_PER_JOB, T)) Severe_per_time = np.zeros((N_SIMS_PER_JOB, T)) Critical_per_time = np.zeros((N_SIMS_PER_JOB, T)) R_per_time = np.zeros((N_SIMS_PER_JOB, T)) Q_per_time = np.zeros((N_SIMS_PER_JOB, T)) Documented_per_time = np.zeros((N_SIMS_PER_JOB, T)) for i in range(N_SIMS_PER_JOB): params['seed'] += 1 S, E, Mild, Documented, Severe, Critical, R, D, Q, num_infected_by,time_documented, \ time_to_activation, time_to_death, time_to_recovery, time_critical, time_exposed, num_infected_asympt,\ age, time_infected, time_to_severe = run_complete_simulation(int(params['seed']),country, contact_matrix, p_mild_severe, p_severe_critical, \ p_critical_death, mean_time_to_isolate_factor, lockdown_factor_age, p_infect_household, \ fraction_stay_home, params, load_population=load_population) infected_per_time[i] = params['n'] - S.sum(axis=1) S_per_time[i] = S.sum(axis=1) E_per_time[i] = E.sum(axis=1) D_per_time[i] = D.sum(axis=1) Mild_per_time[i] = Mild.sum(axis=1) Severe_per_time[i] = Severe.sum(axis=1) Critical_per_time[i] = Critical.sum(axis=1) R_per_time[i] = R.sum(axis=1) Q_per_time[i] = Q.sum(axis=1) r0_total[i] = num_infected_by[np.logical_and(time_exposed <= 20,