using CovidAlertVaccinationModel using OnlineStats using Plots using CovidAlertVaccinationModel:vaccination_data,ymo_vac,ymo_attack_rate const samples = 10 function solve_and_plot_parameters() p = CovidAlertVaccinationModel.get_app_parameters() display(p) out,avg_populations = mean_solve(samples, p,DebugRecorder) p = plot_model(nothing,[nothing],[out],p.infection_introduction_day,p.immunization_begin_day) ymo_vaccination_ts = mean.(out.daily_immunized_by_age) total_postinf_vaccination = mean.(out.total_postinf_vaccination)#sum.(eachrow(ymo_vaccination_ts[:,180:end])) final_size = mean.(out.unvac_final_size_by_age)#sum.(eachrow(mean.(out.daily_unvac_cases_by_age))) total_preinf_vaccination = mean.(out.total_preinf_vaccination)#sum.(eachrow(ymo_vaccination_ts[:,1:180])) target_final_size = ymo_attack_rate .*avg_populations target_preinf_vac = ymo_vac .* sum(vaccination_data[1:4]) .* avg_populations target_postinf_vac = ymo_vac .* sum(vaccination_data[5:end]) .*avg_populations println("obs final size: $final_size, target: $target_final_size") println("obs preinf vac: $total_preinf_vaccination, target: $target_preinf_vac") println("obs postinf vac: $total_postinf_vaccination,target: $target_postinf_vac") total_final_size = sum.(eachrow(mean.(out.daily_cases_by_age))) println("vac + unvac cases proportion: $(total_final_size./avg_populations))") display(sum.(eachrow(ymo_vaccination_ts)) ./avg_populations) savefig(p,"timeseries.pdf") return out end out = solve_and_plot_parameters()