diff --git "a/data/dataset_Vaccine.csv" "b/data/dataset_Vaccine.csv"
new file mode 100644--- /dev/null
+++ "b/data/dataset_Vaccine.csv"
@@ -0,0 +1,10612 @@
+"keyword","repo_name","file_path","file_extension","file_size","line_count","content","language"
+"Vaccine","kbubar/vaccine_prioritization","main.R",".R","20291","455","# Vaccine Strategy Simple Model
+#setwd(""~/Vaccine Strategy/Vaccine_Allocation_Project"")
+
+# _____________________________________________________________________
+# IMPORT ----
+# _____________________________________________________________________
+source(""run_sim.R"")
+source(""helper_functions.R"")
+country <- ""USA""
+source(""setup.R"")
+
+# _____________________________________________________________________
+# FIGURE 1: Dynamics curves ####
+# _____________________________________________________________________
+this_C <- C/scale_15
+ptm <- proc.time()
+for (i in seq(0, 50, by = 1)){
+ j <- i/100
+ list_all[[paste0(i)]] <- run_sim(this_C, j, ""all"", num_perday, v_e_type, this_v_e)
+ list_kids[[paste0(i)]] <- run_sim(this_C, j, ""kids"", num_perday, v_e_type, this_v_e)
+ list_adults[[paste0(i)]] <- run_sim(this_C, j, ""adults"", num_perday, v_e_type, this_v_e)
+ list_elderly[[paste0(i)]] <- run_sim(this_C, j, ""elderly"", num_perday, v_e_type, this_v_e)
+ list_twentyplus[[paste0(i)]] <- run_sim(this_C, j, ""twentyplus"", num_perday, v_e_type, this_v_e)
+}
+proc.time() - ptm
+
+p_mort <- plot_over_vax_avail(""deaths"")
+p_infect <- plot_over_vax_avail(""cases"")
+
+t_one <- 11
+infect_10 <- plot_strat_overtime(""I"", list_all[[1]], list_all[[t_one]], list_adults[[t_one]],
+ list_kids[[t_one]], list_twentyplus[[t_one]], list_elderly[[t_one]], 0.1/num_perday) +
+ onlyy_theme +
+ ggtitle(""10% vaccine supply"") +
+ theme(plot.title = element_text(color = ""black""))
+t_two <- 41
+infect_30 <- plot_strat_overtime(""I"", list_all[[1]], list_all[[t_two]], list_adults[[t_two]],
+ list_kids[[t_two]], list_twentyplus[[t_two]], list_elderly[[t_two]], 0.3/num_perday) +
+ nolabels_theme +
+ ggtitle(""30% vaccine supply"") +
+ theme(plot.title = element_text(color = ""black""))
+
+mort_10 <- plot_strat_overtime(""D"", list_all[[1]], list_all[[t_one]], list_adults[[t_one]],
+ list_kids[[t_one]], list_twentyplus[[t_one]], list_elderly[[t_one]], 0.1/num_perday)
+
+mort_30 <- plot_strat_overtime(""D"", list_all[[1]], list_all[[t_two]], list_adults[[t_two]],
+ list_kids[[t_two]], list_twentyplus[[t_two]], list_elderly[[t_two]], 0.3/num_perday) +
+ onlyx_theme
+
+strategy_panel <- plot_vaxdist_hist()
+
+sub_panel2 <- ggarrange(infect_10, infect_30, p_infect + onlyy_theme,
+ mort_10, mort_30, p_mort + xlab(""Total vaccine supply\n(% of population)""),
+ nrow = 2)
+
+# export as 9.5x4
+grid.arrange(strategy_panel, sub_panel2,
+ widths = c(2, 7.5))
+
+# _____________________________________________________________________
+# FIGURE 1E and I: Reduction in infections & deaths ####
+# Scenario 1: R0 = 1.15, continuous rollout at num_perday = 0.2%/day
+# _____________________________________________________________________
+ptm <- proc.time()
+
+cores=detectCores()
+cl <- makeCluster(cores[1]-1) # to not overload your computer
+registerDoParallel(cl)
+
+scenarios <- 1:2
+fig1_plots <- foreach (p = scenarios) %dopar% {
+ list_all <- list_kids <- list_adults <- list_elderly <- list_twentyplus <- vector(mode = ""list"")
+ # specify parameters for each scenario
+ if (p == 1){
+ this_C <- C/scale_115
+ } else if (p == 2){
+ this_C <- C/scale_15
+ }
+
+ for (i in seq(0, 50, by = 1)){
+ j <- i/100
+ list_all[[paste0(i)]] <- run_sim(this_C, j, ""all"", num_perday, v_e_type, this_v_e)
+ list_kids[[paste0(i)]] <- run_sim(this_C, j, ""kids"", num_perday, v_e_type, this_v_e)
+ list_adults[[paste0(i)]] <- run_sim(this_C, j, ""adults"", num_perday, v_e_type, this_v_e)
+ list_elderly[[paste0(i)]] <- run_sim(this_C, j, ""elderly"", num_perday, v_e_type, this_v_e)
+ list_twentyplus[[paste0(i)]] <- run_sim(this_C, j, ""twentyplus"", num_perday, v_e_type, this_v_e)
+ }
+ p_mort <- plot_over_vax_avail(""deaths"")
+ p_infect <- plot_over_vax_avail(""cases"")
+ p_yll <- plot_over_vax_avail(""YLL"")
+ list(p_mort, p_infect, p_yll)
+}
+stopCluster(cl)
+
+mort_1 <- fig1_plots[[1]][[1]] +
+ theme(axis.title.x = element_blank()) +
+ theme(plot.margin=unit(c(0.25,0.25,0.25,0.25), ""cm""))
+mort_2 <- fig1_plots[[2]][[1]] +
+ onlyx_theme +
+ theme(axis.title.x = element_blank())
+
+infect_1 <- fig1_plots[[1]][[2]] +
+ onlyy_theme
+infect_2 <- fig1_plots[[2]][[2]] +
+ nolabels_theme
+
+# export as pdf 5x4""
+panel <- ggarrange(infect_1, mort_1,
+ nrow = 2,
+ label.args = list(gp = grid::gpar(fontsize=12, fontface = ""bold""),
+ hjust=0, vjust = 1.1),
+ bottom = textGrob(""Total vaccine supply (% of population)"", gp = gpar(fontsize = 12),
+ vjust = 0.3, hjust = 0.36),
+ padding = unit(0.5, ""line""))
+
+proc.time() - ptm
+
+# _____________________________________________________________________
+# FIGURE 3: Age-dep ve ####
+# Update plot_over_vax_avail so solid line is thinner than dashed line (size = 0.75)
+# _____________________________________________________________________
+ptm <- proc.time()
+
+cores=detectCores()
+cl <- makeCluster(cores[1]-1) #not to overload your computer
+registerDoParallel(cl)
+
+scenarios <- 1:2
+fig2_plots <- foreach (p = scenarios) %dopar% {
+ list_all <- list_kids <- list_adults <- list_elderly <- list_twentyplus <- vector(mode = ""list"")
+ list_all_var <- list_kids_var <- list_adults_var <- list_elderly_var <- list_twentyplus_var <- vector(mode = ""list"")
+ # specify parameters for each scenario
+ if (p == 1){
+ this_C <- C/scale_115
+ } else if (p == 2){
+ this_C <- C/scale_15
+ }
+
+ for (i in seq(0, 50, by = 1)){
+ j <- i/100
+ list_all[[paste0(i)]] <- run_sim(this_C, j, ""all"", num_perday, v_e_type, this_v_e)
+ list_kids[[paste0(i)]] <- run_sim(this_C, j, ""kids"", num_perday, v_e_type, this_v_e)
+ list_adults[[paste0(i)]] <- run_sim(this_C, j, ""adults"", num_perday, v_e_type, this_v_e)
+ list_elderly[[paste0(i)]] <- run_sim(this_C, j, ""elderly"", num_perday, v_e_type, this_v_e)
+ list_twentyplus[[paste0(i)]] <- run_sim(this_C, j, ""twentyplus"", num_perday, v_e_type, this_v_e)
+ list_all_var[[paste0(i)]] <- run_sim(this_C, j, ""all"", num_perday, v_e_type, v_e_var)
+ list_kids_var[[paste0(i)]] <- run_sim(this_C, j, ""kids"", num_perday, v_e_type, v_e_var)
+ list_adults_var[[paste0(i)]] <- run_sim(this_C, j, ""adults"", num_perday, v_e_type, v_e_var)
+ list_elderly_var[[paste0(i)]] <- run_sim(this_C, j, ""elderly"", num_perday, v_e_type, v_e_var)
+ list_twentyplus_var[[paste0(i)]] <- run_sim(this_C, j, ""twentyplus"", num_perday, v_e_type, v_e_var)
+ }
+
+ p_mort <- plot_over_vax_avail(""deaths"", TRUE)
+
+ p_infect <- plot_over_vax_avail(""cases"", TRUE)
+
+ p_yll <- plot_over_vax_avail(""YLL"", TRUE)
+
+ list(p_mort, p_infect, p_yll)
+}
+stopCluster(cl)
+proc.time() - ptm
+
+mort_1 <- fig2_plots[[1]][[1]] +
+ theme(legend.position = ""none"",
+ axis.title.x = element_blank())
+mort_2 <- fig2_plots[[2]][[1]]+
+ onlyx_theme +
+ theme(axis.title.x = element_blank())
+
+age_dep_ve <- plot_age_dep_ve()
+
+# export as 7.5x2
+panel <- ggarrange(age_dep_ve, mort_1, mort_2,
+ nrow = 1, widths = c(2, 2, 2),
+ labels = c('A', 'B', 'C'),
+ label.args = list(gp = grid::gpar(fontsize=12, fontface = ""bold""),
+ hjust=0, vjust = 1),
+ padding = unit(0.5, ""line""))
+
+# _____________________________________________________________________
+# FIGURE 4: NYC seroprevalence ####
+# scenario 2 only: R0 = 1.5, continuous rollout with num_perday = 0.002
+# _____________________________________________________________________
+this_scale <- scale_u_for_R0(u_var, C, 2.038) # so that realized R is 1.5
+print(compute_R_with_sero(u_var, C/this_scale, sero_NY))
+
+ptm <- proc.time()
+
+list_all <- list_kids <- list_adults <- list_elderly <- list_twentyplus <- vector(mode = ""list"")
+list_all_var <- list_kids_var <- list_adults_var <- list_elderly_var <- list_twentyplus_var <- vector(mode = ""list"")
+
+this_C <- C/this_scale
+this_sp <- 0.99
+this_se <- 0.70
+
+for (i in seq(0, 50, by = 1)){
+ j <- i/100
+ list_all[[paste0(i)]] <- run_sim(this_C, j, ""all"", num_perday, v_e_type, this_v_e, sero = sero_NY)
+ list_kids[[paste0(i)]] <- run_sim(this_C, j, ""kids"", num_perday, v_e_type, this_v_e, sero = sero_NY)
+ list_adults[[paste0(i)]] <- run_sim(this_C, j, ""adults"", num_perday, v_e_type, this_v_e, sero = sero_NY)
+ list_elderly[[paste0(i)]] <- run_sim(this_C, j, ""elderly"", num_perday, v_e_type, this_v_e, sero = sero_NY)
+ list_twentyplus[[paste0(i)]] <- run_sim(this_C, j, ""twentyplus"", num_perday, v_e_type, this_v_e, sero = sero_NY)
+ list_all_var[[paste0(i)]] <- run_sim(this_C, j, ""all"", num_perday, v_e_type, this_v_e, sero = sero_NY, sp = this_sp, se = this_se)
+ list_kids_var[[paste0(i)]] <- run_sim(this_C, j, ""kids"", num_perday, v_e_type, this_v_e, sero = sero_NY, sp = this_sp, se = this_se)
+ list_adults_var[[paste0(i)]] <- run_sim(this_C, j, ""adults"", num_perday, v_e_type, this_v_e, sero = sero_NY, sp = this_sp, se = this_se)
+ list_elderly_var[[paste0(i)]] <- run_sim(this_C, j, ""elderly"", num_perday, v_e_type, this_v_e, sero = sero_NY, sp = this_sp, se = this_se)
+ list_twentyplus_var[[paste0(i)]] <- run_sim(this_C, j, ""twentyplus"", num_perday, v_e_type, this_v_e, sero = sero_NY, sp = this_sp, se = this_se)
+}
+proc.time() - ptm
+p_mort <- plot_over_vax_avail(""deaths"", TRUE)
+
+p_infect <- plot_over_vax_avail(""cases"", TRUE) +
+ theme(axis.title.x = element_blank())
+
+p_yll <- plot_over_vax_avail(""YLL"", TRUE)+
+ theme(axis.title.x = element_blank())
+
+panel <- ggarrange(p_infect, p_mort, p_yll,
+ nrow = 1,
+ labels = c('A', 'B', 'C'),
+ label.args = list(gp = grid::gpar(fontsize=12, fontface = ""bold""),
+ hjust=0, vjust = 1),
+ padding = unit(0.5, ""line""))
+
+# _____________________________________________________________________
+# FIGURE S10: Synthetic seroprevalence (40%) for US ####
+# scenario 2 only: R0 = 2.6, continuous rollout with num_perday = 0.01
+# _____________________________________________________________________
+IC_syn <- readRDS(""synthetic_sero_US.RData"")
+sero_syn <- IC_syn[82:90]/N_i + IC_syn[100:108]/N_i
+this_scale <- scale_u_for_R0(u_var, C, 2.6) # so that realized R is 1.43
+print(compute_R_with_sero(u_var, C/this_scale, sero_syn))
+
+this_C <- C/this_scale
+ptm <- proc.time()
+
+list_all <- list_kids <- list_adults <- list_elderly <- list_twentyplus <- vector(mode = ""list"")
+list_all_var <- list_kids_var <- list_adults_var <- list_elderly_var <- list_twentyplus_var <- vector(mode = ""list"")
+num_perday <- 0.002
+
+for (i in seq(0, 50, by = 1)){
+ j <- i/100
+ list_all[[paste0(i)]] <- run_sim(this_C, j, ""all"", num_perday, v_e_type,
+ this_v_e, u = u_var, syn_sero_compartments = IC_syn)
+ list_kids[[paste0(i)]] <- run_sim(this_C, j, ""kids"", num_perday, v_e_type,
+ this_v_e, u = u_var, syn_sero_compartments = IC_syn)
+ list_adults[[paste0(i)]] <- run_sim(this_C, j, ""adults"", num_perday, v_e_type,
+ this_v_e, u = u_var,syn_sero_compartments = IC_syn)
+ list_elderly[[paste0(i)]] <- run_sim(this_C, j, ""elderly"", num_perday, v_e_type,
+ this_v_e, u = u_var,syn_sero_compartments = IC_syn)
+ list_twentyplus[[paste0(i)]] <- run_sim(this_C, j, ""twentyplus"", num_perday, v_e_type,
+ this_v_e, u = u_var, syn_sero_compartments = IC_syn)
+ list_all_var[[paste0(i)]] <- run_sim(this_C, j, ""all"", num_perday, v_e_type,
+ this_v_e, u = u_var,syn_sero_compartments = IC_syn, sp = this_sp, se = this_se)
+ list_kids_var[[paste0(i)]] <- run_sim(this_C, j, ""kids"", num_perday, v_e_type,
+ this_v_e, u = u_var,syn_sero_compartments = IC_syn, sp = this_sp, se = this_se)
+ list_adults_var[[paste0(i)]] <- run_sim(this_C, j, ""adults"", num_perday, v_e_type,
+ this_v_e, u = u_var,syn_sero_compartments = IC_syn, sp = this_sp, se = this_se)
+ list_elderly_var[[paste0(i)]] <- run_sim(this_C, j, ""elderly"", num_perday, v_e_type,
+ this_v_e, u = u_var, syn_sero_compartments = IC_syn, sp = this_sp, se = this_se)
+ list_twentyplus_var[[paste0(i)]] <- run_sim(this_C, j, ""twentyplus"", num_perday, v_e_type,
+ this_v_e, u = u_var,syn_sero_compartments = IC_syn, sp = this_sp, se = this_se)
+}
+proc.time() - ptm
+p_mort <- plot_over_vax_avail(""deaths"", TRUE)
+
+p_infect <- plot_over_vax_avail(""cases"", TRUE) +
+ theme(axis.title.x = element_blank())
+
+p_yll <- plot_over_vax_avail(""YLL"", TRUE)+
+ theme(axis.title.x = element_blank())
+
+# export as pdf 9.5x2
+panel <- ggarrange(p_infect, p_mort, p_yll,
+ nrow = 1,
+ labels = c('A', 'B', 'C'),
+ label.args = list(gp = grid::gpar(fontsize=12, fontface = ""bold""),
+ hjust=0, vjust = 1),
+ padding = unit(0.5, ""line""))
+
+# _____________________________________________________________________
+# FIGURE S9: Low seroprevalence for US - Connecticut sero ####
+# scenario 2 only: R0 = 1.5, continuous rollout with num_perday = 0.002
+# _____________________________________________________________________
+this_scale <- scale_u_for_R0(u_var, C, 1.5508) # so that realized R is 1.5
+print(compute_R_with_sero(u_var, C/this_scale, sero_CT))
+
+ptm <- proc.time()
+
+list_all <- list_kids <- list_adults <- list_elderly <- list_twentyplus <- vector(mode = ""list"")
+list_all_var <- list_kids_var <- list_adults_var <- list_elderly_var <- list_twentyplus_var <- vector(mode = ""list"")
+num_perday <- 0.002
+this_C <- C/this_scale
+
+for (i in seq(0, 50, by = 1)){
+ j <- i/100
+ list_all[[paste0(i)]] <- run_sim(this_C, j, ""all"", num_perday, v_e_type, this_v_e, u = u_var, sero = sero_CT)
+ list_kids[[paste0(i)]] <- run_sim(this_C, j, ""kids"", num_perday, v_e_type, this_v_e, u = u_var, sero = sero_CT)
+ list_adults[[paste0(i)]] <- run_sim(this_C, j, ""adults"", num_perday, v_e_type, this_v_e, u = u_var,sero = sero_CT)
+ list_elderly[[paste0(i)]] <- run_sim(this_C, j, ""elderly"", num_perday, v_e_type, this_v_e, u = u_var,sero = sero_CT)
+ list_twentyplus[[paste0(i)]] <- run_sim(this_C, j, ""twentyplus"", num_perday, v_e_type, this_v_e, u = u_var, sero = sero_CT)
+ list_all_var[[paste0(i)]] <- run_sim(this_C, j, ""all"", num_perday, v_e_type, this_v_e, u = u_var,sero = sero_CT, sp = this_sp, se = this_se)
+ list_kids_var[[paste0(i)]] <- run_sim(this_C, j, ""kids"", num_perday, v_e_type, this_v_e, u = u_var,sero = sero_CT, sp = this_sp, se = this_se)
+ list_adults_var[[paste0(i)]] <- run_sim(this_C, j, ""adults"", num_perday, v_e_type, this_v_e, u = u_var,sero = sero_CT, sp = this_sp, se = this_se)
+ list_elderly_var[[paste0(i)]] <- run_sim(this_C, j, ""elderly"", num_perday, v_e_type, this_v_e, u = u_var, sero = sero_CT, sp = this_sp, se = this_se)
+ list_twentyplus_var[[paste0(i)]] <- run_sim(this_C, j, ""twentyplus"", num_perday, v_e_type, this_v_e, u = u_var,sero = sero_CT, sp = this_sp, se = this_se)
+}
+proc.time() - ptm
+p_mort <- plot_over_vax_avail(""deaths"", TRUE)
+
+p_infect <- plot_over_vax_avail(""cases"", TRUE) +
+ theme(axis.title.x = element_blank())
+
+p_yll <- plot_over_vax_avail(""YLL"", TRUE)+
+ theme(axis.title.x = element_blank())
+
+panel <- ggarrange(p_infect, p_mort, p_yll,
+ nrow = 1,
+ labels = c('A', 'B', 'C'),
+ label.args = list(gp = grid::gpar(fontsize=12, fontface = ""bold""),
+ hjust=0, vjust = 1),
+ padding = unit(0.5, ""line""))
+
+# _____________________________________________________________________
+# FIGURE S8: Tipping point heatmaps ####
+# scenario 2 only: R0 = 1.5, continuous rollout with num_perday = 0.002
+# _____________________________________________________________________
+
+# loop over vaccine supply
+baseline_vec <- seq(1, 0.3, by = -0.1)
+
+tp_val <- matrix(NA, 8, 51)
+tp_strat <- matrix(NA, 8, 51)
+
+this_C <- C_26
+
+ptm <- proc.time()
+cores=detectCores()
+cl <- makeCluster(cores[1]-1)
+registerDoParallel(cl)
+
+v_e_type <- ""aorn""
+this_hinge_age <- 50 # 50, 60, or 70 : age group that v_e begins decreasing after
+list_tp_leaky <- foreach (j = 1:length(baseline_vec)) %dopar% {
+ tp_val <- c()
+ tp_strat <- c()
+ for (i in seq(0, 50, by = 1)){
+ temp <- v_e_bisection(baseline_vec[j], this_hinge_age, i, num_perday = 1, v_e_type)
+ tp_val[i] <- temp[[1]]
+ tp_strat[i] <- temp[[2]]
+
+ #tp_val[j, i] <- temp[[1]]
+ #tp_strat[j, i]<- temp[[2]]
+ }
+ out <- list(tp_val, tp_strat)
+}
+stopCluster(cl)
+proc.time() - ptm
+
+saveRDS(list_tp_leaky, ""tp_aorn_59_A_ESP.RData"")
+
+# list_tp_leaky[[1]] corresponds to baseline ve 100%
+# list_tp_leaky[[8]] corresponds to baseline ve 30%
+
+list_79_2 <- readRDS(""tp_aorn_79_C_IND.RData"")
+list_69_2 <- readRDS(""tp_aorn_69_C_IND.RData"")
+list_59_2 <- readRDS(""tp_aorn_59_C_IND.RData"")
+
+list_79_3 <- readRDS(""tp_aorn_79_A_IND.RData"")
+list_69_3 <- readRDS(""tp_aorn_69_A_IND.RData"")
+list_59_3 <- readRDS(""tp_aorn_59_A_IND.RData"")
+
+p79_2 <- plot_tipping_point_heatmap_2(list_79_2) +
+ theme(legend.position = ""none"",
+ axis.title.y = element_blank())
+p69_2 <- plot_tipping_point_heatmap_2(list_69_2)+
+ ylab(""Baseline ve (%)"") +
+ theme(legend.position = ""none"",
+ axis.text.x = element_blank())
+p59_2 <- plot_tipping_point_heatmap_2(list_59_2) +
+ ggtitle(""Continuous rollout"") +
+ theme(legend.position = ""none"",
+ plot.title = element_text(size = 12, face = ""plain""),
+ axis.title.y = element_blank(),
+ axis.text.x = element_blank())
+
+p79_3 <- plot_tipping_point_heatmap_2(list_79_3) +
+ theme(axis.title.y = element_blank(),
+ legend.position = ""none"",
+ axis.text.y = element_blank())
+p69_3 <- plot_tipping_point_heatmap_2(list_69_3) +
+ theme(axis.title.y = element_blank(),
+ axis.text.x = element_blank(),
+ axis.text.y = element_blank() ,
+ legend.position = ""none"")
+p59_3 <- plot_tipping_point_heatmap_2(list_59_3) +
+ ggtitle(""Anticipatory rollout"") +
+ theme(axis.title.y = element_blank(),
+ axis.text.y = element_blank(),
+ axis.text.x = element_blank(),
+ plot.title = element_text(size = 12, face = ""plain""))
+
+leg <- get_legend(p59_3)
+
+p59_3 <- p59_3 + theme(legend.position = ""none"")
+
+#grid.arrange(p59_2, p59_3, p69_2, p69_3, p79_2, p79_3,
+# ncol = 2)
+
+p <- ggarrange(p59_2, p59_3, p69_2, p69_3, p79_2, p79_3,
+ nrow = 3,
+ # labels = c(""A"", ""B"", ""C"", ""D "", ""E "", ""F ""),
+ # label.args = list(gp = grid::gpar(fontsize=12, fontface = ""bold""),
+ # hjust=0, vjust = 1),
+ bottom = textGrob(""Total vaccine supply (% of population)"", gp = gpar(fontsize = 12),
+ vjust = 0.3, hjust = 0.36),
+ padding = unit(0.5,
+ ""line""))
+
+# save as 5x7
+t1 <- textGrob(""Hinge age\n59"")
+t2 <- textGrob(""Hinge age\n69"")
+t3 <- textGrob(""Hinge age\n79"")
+
+lay <- rbind(c(1, 4, 5),
+ c(2, 4, 5),
+ c(3, 4, 5))
+
+# export as 8x6
+g <- grid.arrange(t1, t2, t3, p, leg,
+ layout_matrix = lay,
+ widths = c(1, 6, 1.5))
+
+# _____________________________________________________________________
+# FIGURE S13-15: Dynamics curves ####
+# scenario 2 only: R0 = 1.5, continuous rollout with num_perday = 0.002
+# _____________________________________________________________________
+num_perday <- 0.002
+this_C <- C/scale_15
+
+for (i in seq(0, 50, by = 1)){
+ j <- i/100
+ list_all[[paste0(i)]] <- run_sim(this_C, j, ""all"", num_perday, v_e_type, this_v_e)
+ list_kids[[paste0(i)]] <- run_sim(this_C, j, ""kids"", num_perday, v_e_type, this_v_e)
+ list_adults[[paste0(i)]] <- run_sim(this_C, j, ""adults"", num_perday, v_e_type, this_v_e)
+ list_elderly[[paste0(i)]] <- run_sim(this_C, j, ""elderly"", num_perday, v_e_type, this_v_e)
+ list_twentyplus[[paste0(i)]] <- run_sim(this_C, j, ""twentyplus"", num_perday, v_e_type, this_v_e)
+}
+# export 6*5.5
+infect <- plot_supp_dynamics_panel(""I"")
+cumul_infect <- plot_supp_dynamics_panel(""R"")
+cumul_mort <- plot_supp_dynamics_panel(""D"")
+
+","R"
+"Vaccine","kbubar/vaccine_prioritization","get_age_demographics.R",".R","940","40","# Combine 5 year age-group demographics from UN into 10 years age-groups
+setwd(""~/Vaccine Strategy/Vaccine_Allocation_Project"")
+
+library(grid)
+library(readxl)
+
+# population in 1000s
+df <- read_excel(""WPP2019_POP_F07_1_POPULATION_BY_AGE_BOTH_SEXES.xlsx"", ""ESTIMATES"")
+
+# country codes:
+# Belgium: BEL
+# United States: USA
+# India: IND
+# Spain: ESP
+# Zimbabwe: ZWE
+# Brazil: BRA
+# China: CHN
+# South Africa: ZAF
+# Poland: POL
+
+countrycode <- ""POL""
+country <- ""Poland""
+popdata <- df[df$Country == country,]
+popdata <- popdata[popdata$Year == 2020,]
+
+pop_demo <- rep(0, 10)
+col_num <- 9 # col of 0-4 y.o.
+
+for (i in 1:8){
+ pop_demo[i] <- as.numeric(popdata[col_num]) + as.numeric(popdata[col_num + 1])
+ col_num <- col_num + 2
+}
+
+pop_demo[9] <- sum(as.numeric(popdata[col_num:29]))
+total_pop <- sum(pop_demo)
+pop_demo <- pop_demo/total_pop
+pop_demo[10] <- total_pop*1000
+
+saveRDS(pop_demo, paste0(""age_demographics_"", countrycode, "".RData""))
+","R"
+"Vaccine","kbubar/vaccine_prioritization","get_YLL_vec.R",".R","1626","58","# Get YLL vec for a specific country from WHO GHO data
+# https://apps.who.int/gho/data/view.main.LT62160?lang=en
+
+# country codes:
+# Belgium: BEL
+# United States: USA
+# India: IND
+# Spain: ESP
+# Zimbabwe: ZWE
+# Brazil: BRA
+# China: CHN
+# South Africa: ZAF
+# Poland: POL
+
+setwd(""~/Vaccine Strategy/Vaccine_Allocation_Project"")
+
+# read in YLL data
+df <- read_excel(""WPP2019_MORT_F16_1_LIFE_EXPECTANCY_BY_AGE_BOTH_SEXES.xlsx"", ""ESTIMATES"", skip = 16)
+demo_df <- read_excel(""WPP2019_POP_F07_1_POPULATION_BY_AGE_BOTH_SEXES.xlsx"", ""ESTIMATES"")
+
+country <- ""Belgium""
+countrycode <- ""BEL""
+data <- df[df$Type == ""Country/Area"",]
+data <- data[data$`Region, subregion, country or area *` == country,]
+data <- data[data$Period == ""2015-2020"",]
+
+# read in pop demographics to weigh YLL
+
+popdata <- demo_df[demo_df$Country == country,]
+popdata <- popdata[popdata$Year == 2020,]
+
+pop_demo <- rep(0, 20)
+yll_data <- rep(0, 20)
+col_num_pop <- 9 # col of 0-4 y.o.
+col_num_yll <- 10
+
+for (i in 1:21){
+ pop_demo[i] <- as.numeric(popdata[col_num_pop])
+ yll_data[i] <- as.numeric(data[col_num_yll])
+ col_num_pop <- col_num_pop + 1
+ col_num_yll <- col_num_yll + 1
+}
+
+# weighted avg yll binned into 10y age bins (0-9, 10-19, ..., 80+)
+yll_vec <- rep(0, 9)
+
+count <- 1
+for (i in 1:8){
+ temp_sum <- pop_demo[count] + pop_demo[count + 1]
+ yll_vec[i] <- yll_data[count]*pop_demo[count]/temp_sum + yll_data[count+1]*pop_demo[count+1]/temp_sum
+ count <- count + 2
+}
+
+temp_sum <- sum(pop_demo[count:21])
+yll_vec[9] <- sum(yll_data[count:21]*pop_demo[count:21]/temp_sum)
+
+saveRDS(yll_vec, paste0(""yll_vec_"", countrycode, "".RData""))
+","R"
+"Vaccine","kbubar/vaccine_prioritization","run_sim.R",".R","12137","275","run_sim = function(C, percent_vax, strategy, num_perday, v_e_type, v_e = v_e_constant,
+ u = u_var, sero = sero_none, sp = 1, se = 0, refuse_vax = 0.3, syn_sero_compartments = NA){
+ # IC: 0.25% of each age group exposted and infected if numperday != 1
+ # Sero+ start in recovered
+ # refuse_vax % proportion of each compartment start in *x (Sx, Ex, Ix and Rx)
+ # everyone else starts in S
+ # Runtime: 365 days post start of rollout
+ # Rollout: Continuous at num_perday% of total pop/day until all vaccines are distributed
+ # so num_perday = 1 implies anticipatory rollout
+
+ E_0 <- Ev_0 <- Ex_0 <- Sv_0 <- Sx_0 <- Iv_0 <- Rv_0 <- D_0 <- rep(0, num_groups)
+ R_0 <- (N_i * sero)*(1-refuse_vax)
+ Rx_0 <- (N_i * sero)*refuse_vax
+
+ if (num_perday == 1){
+ I_0 <- rep(1, num_groups)
+ Ix_0 <- rep(1, num_groups)
+ } else {
+ I_0 <- (N_i*0.0025)*(1-refuse_vax) # 0.5% of each age group starts in exposed and infected
+ Ix_0 <- (N_i*0.0025)*refuse_vax
+ E_0 <- (N_i*0.0025)*(1-refuse_vax)
+ Ex_0 <- (N_i*0.0025)*refuse_vax
+ }
+
+ S_0 <- (N_i - I_0 - E_0 - R_0 - Ix_0 - Ex_0 - Rx_0)*(1-refuse_vax)
+ Sx_0 <- (N_i - I_0 - E_0 - R_0 - Ix_0 - Ex_0 - Rx_0)*refuse_vax
+
+ # specify group to vaccinate according to allocation strategy
+ if (strategy == ""all""){
+ groups <- 1:9
+ } else if (strategy == ""kids""){
+ groups <- 1:2
+ } else if (strategy == ""adults"") {
+ groups <- 3:5
+ } else if (strategy == ""elderly"") {
+ groups <- 7:9
+ } else if (strategy == ""twentyplus"") {
+ groups <- 3:9
+ }
+ people_to_vax <- sum(S_0[groups] + E_0[groups] + R_0[groups])
+
+ vax_proportion <- rep(0, num_groups)
+ vax_proportion[groups] <- (S_0[groups] + E_0[groups] + R_0[groups])/people_to_vax
+
+ vax_supply <- percent_vax*pop_total
+
+ # anticipatory rollout IC - vaccinate people at t=0
+ if (num_perday == 1){
+ nvax <- vax_supply
+ vax_distribution <- nvax*vax_proportion
+ S <- S_0
+ E <- E_0
+ R <- R_0
+ vax_eligible <- (S+E)*sp + R*(1-se)
+
+ if (any(vax_distribution > vax_eligible)){
+ # make sure everyone in the specificed age groups are vaccinated
+ if (!all(vax_distribution[groups] > vax_eligible[groups])){
+ temp <- vax_distribution
+ temp[vax_distribution > vax_eligible] <- vax_eligible[vax_distribution > vax_eligible]
+ leftover_vax <- sum(vax_distribution - temp)
+
+ full_groups <- 1:9
+ full_groups <- full_groups[vax_distribution > vax_eligible]
+ leftover_groups <- groups[!groups %in% full_groups]
+ people_to_vax <- sum(vax_eligible[leftover_groups])
+ vax_proportion <- rep(0, num_groups)
+ vax_proportion[leftover_groups] <- vax_eligible[leftover_groups]/people_to_vax
+
+ vax_leftover_dist <- leftover_vax*vax_proportion
+ vax_distribution <- temp + vax_leftover_dist
+ }
+ #vax_distribution[vax_distribution > (S+E+R)] <- S[vax_distribution > (S+E+R)] + E[vax_distribution > (S+E+R)] + R[vax_distribution > (S+E+R)]
+ #distribute vaccines to all other age groups if there's doses left over after strategy specified
+ #age groups
+ if (any(vax_distribution > vax_eligible)){
+ temp <- vax_distribution
+ temp[vax_distribution > vax_eligible] <- vax_eligible[vax_distribution > vax_eligible]
+ leftover_vax <- sum(vax_distribution - temp)
+
+ leftover_groups <- 1:9
+ leftover_groups <- leftover_groups[!leftover_groups %in% groups]
+ people_to_vax <- sum(vax_eligible[leftover_groups])
+ vax_proportion <- rep(0, num_groups)
+ vax_proportion[leftover_groups] <- vax_eligible[leftover_groups]/people_to_vax
+
+ vax_leftover_dist <- leftover_vax*vax_proportion
+ vax_distribution <- temp + vax_leftover_dist
+ }
+ }
+
+ alpha <- as.matrix(vax_distribution)/(as.matrix(vax_eligible))
+ alpha[alpha == Inf] <- 0
+ alpha[is.nan(alpha)] <- 0
+
+ if (v_e_type == ""leaky"") {
+ # first move vaccinated people
+ S_0 <- S - as.matrix(S*alpha*sp) - as.matrix(S*alpha*(1-sp))
+ Sv_0 <- as.matrix(S*alpha*sp)
+ Sx_0 <- Sx_0 + as.matrix(S*alpha*(1-sp))
+
+ } else {
+ S_0 <- S - as.matrix(S*alpha*sp) - as.matrix(S*alpha*(1-sp))
+ Sv_0 <- as.matrix(S*alpha*sp*v_e)
+ Sx_0 <- Sx_0 + as.matrix(S*alpha*sp*(1-v_e)) + as.matrix(S*alpha*(1-sp))
+ }
+ R_0 <- R - as.matrix(R*alpha*(1-se)) - as.matrix(R*alpha*se)
+ Rv_0 <- as.matrix(R*alpha*(1-se))
+ Rx_0 <- Rx_0 + as.matrix(R*alpha*se)
+
+ vax_supply <- 0
+ }
+
+ compartments_initial <- c(S_0,Sv_0,Sx_0,E_0,Ev_0,Ex_0,I_0,Iv_0,Ix_0,R_0,Rv_0,Rx_0,D_0,vax_supply)
+
+ if (length(syn_sero_compartments) > 1){
+ compartments_initial <- c(as.numeric(syn_sero_compartments), vax_supply)
+ }
+
+ parameters = list(u=u, C=C, d_E=d_E, d_I=d_I, v_e=v_e, v_e_type = v_e_type, num_groups = num_groups,
+ N_i = N_i, num_perday=num_perday, vax_proportion=vax_proportion, groups=groups, sp=sp, se=se, pop_total=pop_total)
+
+ t <- seq(from=0, to=365, by=1)
+ event_times <- seq(from=0, to=floor(vax_supply/(num_perday*pop_total)), by=1)
+ df <- as.data.frame(deSolve::lsoda(y=compartments_initial, times=t, func=calculate_derivatives, parms=parameters, events=list(func=move_vaccinated_event, time=event_times)))
+
+ names(df) <- c(""time"", ""S1"", ""S2"", ""S3"", ""S4"", ""S5"", ""S6"", ""S7"", ""S8"", ""S9"",
+ ""Sv1"", ""Sv2"", ""Sv3"", ""Sv4"", ""Sv5"", ""Sv6"", ""Sv7"", ""Sv8"", ""Sv9"",
+ ""Sx1"", ""Sx2"", ""Sx3"", ""Sx4"", ""Sx5"", ""Sx6"", ""Sx7"", ""Sx8"", ""Sx9"",
+ ""E1"", ""E2"", ""E3"", ""E4"", ""E5"", ""E6"", ""E7"", ""E8"", ""E9"",
+ ""Ev1"", ""Ev2"", ""Ev3"", ""Ev4"", ""Ev5"", ""Ev6"", ""Ev7"", ""Ev8"", ""Ev9"",
+ ""Ex1"", ""Ex2"", ""Ex3"", ""Ex4"", ""Ex5"", ""Ex6"", ""Ex7"", ""Ex8"", ""Ex9"",
+ ""I1"", ""I2"", ""I3"", ""I4"", ""I5"", ""I6"", ""I7"", ""I8"", ""I9"",
+ ""Iv1"", ""Iv2"", ""Iv3"", ""Iv4"", ""Iv5"", ""Iv6"", ""Iv7"", ""Iv8"", ""Iv9"",
+ ""Ix1"", ""Ix2"", ""Ix3"", ""Ix4"", ""Ix5"", ""Ix6"", ""Ix7"", ""Ix8"", ""Ix9"",
+ ""R1"", ""R2"", ""R3"", ""R4"", ""R5"", ""R6"", ""R7"", ""R8"", ""R9"",
+ ""Rv1"", ""Rv2"", ""Rv3"", ""Rv4"", ""Rv5"", ""Rv6"", ""Rv7"", ""Rv8"", ""Rv9"",
+ ""Rx1"", ""Rx2"", ""Rx3"", ""Rx4"", ""Rx5"", ""Rx6"", ""Rx7"", ""Rx8"", ""Rx9"",
+ ""D1"", ""D2"", ""D3"", ""D4"", ""D5"", ""D6"", ""D7"", ""D8"", ""D9"")
+
+ return(df)
+}
+
+run_sim_NTB = function(C, percent_vax, strategy, num_perday, ve_S, ve_I, ve_P,
+ u = u_var, sero = sero_none, sp = 1, se = 0, refuse_vax = 0.3, syn_sero_compartments = NA){
+ # New IC: start w/ 0.5% of each age group in I
+ # Only run sim for 365 days post start of rollout
+ # Vaccine rollout is continuous at 1% of total pop/day until all vaccines are distributed
+
+ E_0 <- Ev_0 <- Ex_0 <- Sv_0 <- Sx_0 <- Iv_0 <- Rv_0 <- D_0 <- rep(0, num_groups)
+ R_0 <- (N_i * sero)*(1-refuse_vax)
+ Rx_0 <- (N_i * sero)*refuse_vax
+
+ if (num_perday == 1){
+ I_0 <- rep(1, num_groups)
+ Ix_0 <- rep(1, num_groups)
+ } else {
+ I_0 <- (N_i*0.0025)*(1-refuse_vax) # 0.5% of each age group starts in exposed and infected
+ Ix_0 <- (N_i*0.0025)*refuse_vax
+ E_0 <- (N_i*0.0025)*(1-refuse_vax)
+ Ex_0 <- (N_i*0.0025)*refuse_vax
+ }
+
+ S_0 <- (N_i - I_0 - E_0 - R_0 - Ix_0 - Ex_0 - Rx_0)*(1-refuse_vax)
+ Sx_0 <- (N_i - I_0 - E_0 - R_0 - Ix_0 - Ex_0 - Rx_0)*refuse_vax
+
+ # specify group to vaccinate according to allocation strategy
+ if (strategy == ""all""){
+ groups <- 1:9
+ } else if (strategy == ""kids""){
+ groups <- 1:2
+ } else if (strategy == ""adults"") {
+ groups <- 3:5
+ } else if (strategy == ""elderly"") {
+ groups <- 7:9
+ } else if (strategy == ""twentyplus"") {
+ groups <- 3:9
+ }
+ people_to_vax <- sum(S_0[groups] + E_0[groups] + R_0[groups])
+
+ vax_proportion <- rep(0, num_groups)
+ vax_proportion[groups] <- (S_0[groups] + E_0[groups] + R_0[groups])/people_to_vax
+
+ vax_supply <- percent_vax*pop_total
+
+ # anticipatory rollout IC - vaccinate people at t=0
+ if (num_perday == 1){
+ nvax <- vax_supply
+ vax_distribution <- nvax*vax_proportion
+ S <- S_0
+ E <- E_0
+ R <- R_0
+ vax_eligible <- S+E+R
+
+ if (any(vax_distribution > vax_eligible)){
+ # make sure everyone in the specificed age groups are vaccinated
+ if (!all(vax_distribution[groups] > vax_eligible[groups])){
+ temp <- vax_distribution
+ temp[vax_distribution > vax_eligible] <- vax_eligible[vax_distribution > vax_eligible]
+ leftover_vax <- sum(vax_distribution - temp)
+
+ full_groups <- 1:9
+ full_groups <- full_groups[vax_distribution > vax_eligible]
+ leftover_groups <- groups[!groups %in% full_groups]
+ people_to_vax <- sum(vax_eligible[leftover_groups])
+ vax_proportion <- rep(0, num_groups)
+ vax_proportion[leftover_groups] <- vax_eligible[leftover_groups]/people_to_vax
+
+ vax_leftover_dist <- leftover_vax*vax_proportion
+ vax_distribution <- temp + vax_leftover_dist
+ }
+ #vax_distribution[vax_distribution > (S+E+R)] <- S[vax_distribution > (S+E+R)] + E[vax_distribution > (S+E+R)] + R[vax_distribution > (S+E+R)]
+ #distribute vaccines to all other age groups if there's doses left over after strategy specified
+ #age groups
+ if (any(vax_distribution > vax_eligible)){
+ temp <- vax_distribution
+ temp[vax_distribution > vax_eligible] <- vax_eligible[vax_distribution > vax_eligible]
+ leftover_vax <- sum(vax_distribution - temp)
+
+ leftover_groups <- 1:9
+ leftover_groups <- leftover_groups[!leftover_groups %in% groups]
+ people_to_vax <- sum(vax_eligible[leftover_groups])
+ vax_proportion <- rep(0, num_groups)
+ vax_proportion[leftover_groups] <- vax_eligible[leftover_groups]/people_to_vax
+
+ vax_leftover_dist <- leftover_vax*vax_proportion
+ vax_distribution <- temp + vax_leftover_dist
+ }
+ }
+
+ alpha <- as.matrix(vax_distribution)/(as.matrix(vax_eligible))
+ alpha[alpha == Inf] <- 0
+ alpha[is.nan(alpha)] <- 0
+
+ # first move vaccinated people
+ S_0 <- S - as.matrix(S*alpha)
+ Sv_0 <- as.matrix(S*alpha)
+
+ R_0 <- R - as.matrix(R*alpha)
+ Rv_0 <- as.matrix(R*alpha)
+
+ vax_supply <- 0
+ }
+
+ compartments_initial <- c(S_0,Sv_0,Sx_0,E_0,Ev_0,Ex_0,I_0,Iv_0,Ix_0,R_0,Rv_0,Rx_0,D_0,vax_supply)
+
+ if (length(syn_sero_compartments) > 1){
+ compartments_initial <- c(as.numeric(syn_sero_compartments), vax_supply)
+ }
+
+ parameters = list(u=u, C=C, d_E=d_E, d_I=d_I, v_e_type = ""leaky"", num_groups = num_groups, N_i = N_i,
+ sp = sp, se = se, num_perday=num_perday, vax_proportion=vax_proportion, groups=groups, pop_total=pop_total,
+ ve_S = ve_S, ve_I = ve_I, ve_P = ve_P)
+ t <- seq(from=0, to=365, by=1)
+ event_times <- seq(from=0, to=floor(vax_supply/(num_perday*pop_total)), by=1)
+ df <- as.data.frame(deSolve::lsoda(y=compartments_initial, times=t, func=calculate_derivatives_NTB, parms=parameters,
+ events=list(func=move_vaccinated_event, time=event_times)))
+
+ names(df) <- c(""time"", ""S1"", ""S2"", ""S3"", ""S4"", ""S5"", ""S6"", ""S7"", ""S8"", ""S9"",
+ ""Sv1"", ""Sv2"", ""Sv3"", ""Sv4"", ""Sv5"", ""Sv6"", ""Sv7"", ""Sv8"", ""Sv9"",
+ ""Sx1"", ""Sx2"", ""Sx3"", ""Sx4"", ""Sx5"", ""Sx6"", ""Sx7"", ""Sx8"", ""Sx9"",
+ ""E1"", ""E2"", ""E3"", ""E4"", ""E5"", ""E6"", ""E7"", ""E8"", ""E9"",
+ ""Ev1"", ""Ev2"", ""Ev3"", ""Ev4"", ""Ev5"", ""Ev6"", ""Ev7"", ""Ev8"", ""Ev9"",
+ ""Ex1"", ""Ex2"", ""Ex3"", ""Ex4"", ""Ex5"", ""Ex6"", ""Ex7"", ""Ex8"", ""Ex9"",
+ ""I1"", ""I2"", ""I3"", ""I4"", ""I5"", ""I6"", ""I7"", ""I8"", ""I9"",
+ ""Iv1"", ""Iv2"", ""Iv3"", ""Iv4"", ""Iv5"", ""Iv6"", ""Iv7"", ""Iv8"", ""Iv9"",
+ ""Ix1"", ""Ix2"", ""Ix3"", ""Ix4"", ""Ix5"", ""Ix6"", ""Ix7"", ""Ix8"", ""Ix9"",
+ ""R1"", ""R2"", ""R3"", ""R4"", ""R5"", ""R6"", ""R7"", ""R8"", ""R9"",
+ ""Rv1"", ""Rv2"", ""Rv3"", ""Rv4"", ""Rv5"", ""Rv6"", ""Rv7"", ""Rv8"", ""Rv9"",
+ ""Rx1"", ""Rx2"", ""Rx3"", ""Rx4"", ""Rx5"", ""Rx6"", ""Rx7"", ""Rx8"", ""Rx9"",
+ ""D1"", ""D2"", ""D3"", ""D4"", ""D5"", ""D6"", ""D7"", ""D8"", ""D9"")
+
+ return(df)
+}
+","R"
+"Vaccine","kbubar/vaccine_prioritization","run_NTB.R",".R","11676","294","# _____________________________________________________________________
+# IMPORT ----
+# _____________________________________________________________________
+# _____________________________________________________________________
+# FUNCTIONS ----
+# _____________________________________________________________________
+source(""run_sim.R"")
+source(""helper_functions.R"")
+source(""setup.R"")
+
+# NTB: move vaccinated is the same with sp = 1, se = 0
+ve_S <- rep(0, 9)
+ve_I_vec <- list(rep(0, 9),
+ rep(0.1, 9),
+ rep(0.2, 9),
+ rep(0.3, 9),
+ rep(0.4, 9),
+ rep(0.5, 9),
+ rep(0.6, 9),
+ rep(0.7, 9),
+ rep(0.8, 9),
+ rep(0.9, 9),
+ rep(1.0, 9))
+
+ve_P <- rep(0.9,9)
+
+test <- run_sim_NTB(C/scale_15, 0.01, ""all"", 0.01, ve_S, ve_I, ve_P)
+
+ptm <- proc.time()
+
+# clusters
+cores=detectCores()
+cl <- makeCluster(cores[1]-1) # to not overload your computer
+registerDoParallel(cl)
+
+HM <- foreach(ve_I = ve_I_vec) %dopar% {
+
+ # * SCENARIO 4: R0 - 2.6, anticipatory rollout
+ list_all <- list_kids <- list_adults <- list_elderly <- list_twentyplus <- vector(mode = ""list"")
+ num_perday <- 1
+ for (i in seq(0, 50, by = 1)){
+ j <- i/100
+ list_all[[paste0(i)]] <- run_sim_NTB(C_26, j, ""all"", num_perday, ve_S, ve_I, ve_P)
+ list_kids[[paste0(i)]] <- run_sim_NTB(C_26, j, ""kids"", num_perday, ve_S, ve_I, ve_P)
+ list_adults[[paste0(i)]] <- run_sim_NTB(C_26, j, ""adults"", num_perday, ve_S, ve_I, ve_P)
+ list_elderly[[paste0(i)]] <- run_sim_NTB(C_26, j, ""elderly"", num_perday, ve_S, ve_I, ve_P)
+ list_twentyplus[[paste0(i)]] <- run_sim_NTB(C_26, j, ""twentyplus"", num_perday, ve_S, ve_I, ve_P)
+ }
+
+ x_vec <- seq(1, 50, by = 1)
+ I4 <- sapply(x_vec, FUN = function(x) get_best_strat_cases_new(x,
+ list_all,list_kids,list_adults,
+ list_elderly,list_twentyplus))
+ M4 <- sapply(x_vec, FUN = function(x) get_best_strat_deaths_new(x,
+ list_all,list_kids,list_adults,
+ list_elderly,list_twentyplus))
+ Y4 <- sapply(x_vec, FUN = function(x) get_best_strat_yll_new(x,
+ list_all,list_kids,list_adults,
+ list_elderly,list_twentyplus))
+ # * SCENARIO 3: R0 = 1.5, anticipatory rollout
+ list_all <- list_kids <- list_adults <- list_elderly <- list_twentyplus <- vector(mode = ""list"")
+
+ num_perday <- 1
+ for (i in seq(0, 50, by = 1)){
+ j <- i/100
+ list_all[[paste0(i)]] <- run_sim_NTB(C_15, j, ""all"", num_perday, ve_S, ve_I, ve_P)
+ list_kids[[paste0(i)]] <- run_sim_NTB(C_15, j, ""kids"", num_perday, ve_S, ve_I, ve_P)
+ list_adults[[paste0(i)]] <- run_sim_NTB(C_15, j, ""adults"", num_perday, ve_S, ve_I, ve_P)
+ list_elderly[[paste0(i)]] <- run_sim_NTB(C_15, j, ""elderly"", num_perday, ve_S, ve_I, ve_P)
+ list_twentyplus[[paste0(i)]] <- run_sim_NTB(C_15, j, ""twentyplus"", num_perday, ve_S, ve_I, ve_P)
+ }
+
+ I3 <- sapply(x_vec, FUN = function(x) get_best_strat_cases_new(x,
+ list_all,list_kids,list_adults,
+ list_elderly,list_twentyplus))
+ M3 <- sapply(x_vec, FUN = function(x) get_best_strat_deaths_new(x,
+ list_all,list_kids,list_adults,
+ list_elderly,list_twentyplus))
+ Y3 <- sapply(x_vec, FUN = function(x) get_best_strat_yll_new(x,
+ list_all,list_kids,list_adults,
+ list_elderly,list_twentyplus))
+
+ # * SCENARIO 2: R0 - 1.5, continuous rollout
+ num_perday <- 0.002
+ list_all <- list_kids <- list_adults <- list_elderly <- list_twentyplus <- vector(mode = ""list"")
+ for (i in seq(0, 50, by = 1)){
+ j <- i/100
+ list_all[[paste0(i)]] <- run_sim_NTB(C_15 , j, ""all"", num_perday, ve_S, ve_I, ve_P)
+ list_kids[[paste0(i)]] <- run_sim_NTB(C_15 , j, ""kids"", num_perday, ve_S, ve_I, ve_P)
+ list_adults[[paste0(i)]] <- run_sim_NTB(C_15 , j, ""adults"", num_perday, ve_S, ve_I, ve_P)
+ list_elderly[[paste0(i)]] <- run_sim_NTB(C_15 , j, ""elderly"", num_perday, ve_S, ve_I, ve_P)
+ list_twentyplus[[paste0(i)]] <- run_sim_NTB(C_15 , j, ""twentyplus"", num_perday, ve_S, ve_I, ve_P)
+ }
+
+ I2 <- sapply(x_vec, FUN = function(x) get_best_strat_cases_new(x,
+ list_all,list_kids,list_adults,
+ list_elderly,list_twentyplus))
+ M2 <- sapply(x_vec, FUN = function(x) get_best_strat_deaths_new(x,
+ list_all,list_kids,list_adults,
+ list_elderly,list_twentyplus))
+ Y2 <- sapply(x_vec, FUN = function(x) get_best_strat_yll_new(x,
+ list_all,list_kids,list_adults,
+ list_elderly,list_twentyplus))
+ # * SCENARIO 1: R0 - 1.15, continuous rollout
+ num_perday <- 0.002
+ list_all <- list_kids <- list_adults <- list_elderly <- list_twentyplus <- vector(mode = ""list"")
+ for (i in seq(0, 50, by = 1)){
+ j <- i/100
+ list_all[[paste0(i)]] <- run_sim_NTB(C_115 , j, ""all"", num_perday, ve_S, ve_I, ve_P)
+ list_kids[[paste0(i)]] <- run_sim_NTB(C_115 , j, ""kids"", num_perday, ve_S, ve_I, ve_P)
+ list_adults[[paste0(i)]] <- run_sim_NTB(C_115 , j, ""adults"", num_perday, ve_S, ve_I, ve_P)
+ list_elderly[[paste0(i)]] <- run_sim_NTB(C_115 , j, ""elderly"", num_perday, ve_S, ve_I, ve_P)
+ list_twentyplus[[paste0(i)]] <- run_sim_NTB(C_115 , j, ""twentyplus"", num_perday, ve_S, ve_I, ve_P)
+ }
+
+ I1 <- sapply(x_vec, FUN = function(x) get_best_strat_cases_new(x,
+ list_all,list_kids,list_adults,
+ list_elderly,list_twentyplus))
+ M1 <- sapply(x_vec, FUN = function(x) get_best_strat_deaths_new(x,
+ list_all,list_kids,list_adults,
+ list_elderly,list_twentyplus))
+ Y1 <- sapply(x_vec, FUN = function(x) get_best_strat_yll_new(x,
+ list_all,list_kids,list_adults,
+ list_elderly,list_twentyplus))
+ list(s1 = cbind(I1, M1, Y1),
+ s2 = cbind(I2, M2, Y2),
+ s3 = cbind(I3, M3, Y3),
+ s4 = cbind(I4, M4, Y4))
+}
+stopCluster(cl)
+proc.time() - ptm
+#saveRDS(HM, ""HM_USA_NTB.RData"")
+
+param <- ""ve_I""
+
+num <- length(HM)
+
+S1_I <- S2_I <- S3_I <- S4_I <- S1_M <- S2_M <- S3_M <- S4_M <- S1_Y <- S2_Y <- S3_Y <- S4_Y <- matrix(NA, num, 50)
+
+for (i in 1:num){
+ S1_I[i ,] <- HM[[i]]$s1[, 1]
+ S2_I[i ,] <- HM[[i]]$s2[, 1]
+ S3_I[i ,] <- HM[[i]]$s3[, 1]
+ S4_I[i ,] <- HM[[i]]$s4[, 1]
+
+ S1_M[i ,] <- HM[[i]]$s1[, 2]
+ S2_M[i ,] <- HM[[i]]$s2[, 2]
+ S3_M[i ,] <- HM[[i]]$s3[, 2]
+ S4_M[i ,] <- HM[[i]]$s4[, 2]
+
+ S1_Y[i ,] <- HM[[i]]$s1[, 3]
+ S2_Y[i ,] <- HM[[i]]$s2[, 3]
+ S3_Y[i ,] <- HM[[i]]$s3[, 3]
+ S4_Y[i ,] <- HM[[i]]$s4[, 3]
+}
+
+S1_Inew <- S2_Inew <- S3_Inew <- S4_Inew <- S1_Mnew <- S2_Mnew <- S3_Mnew <- S4_Mnew <- S1_Ynew <- S2_Ynew <- S3_Ynew <- S4_Ynew <- matrix(NA, num, 50)
+
+#adults = 1 (20-49)
+#twentyplus = 2
+#elderly = 3
+#kids = 4
+#all = 5
+### I Matrices
+for(i in 1){
+ S1_Inew[S1_I == ""adults""] = 1
+ S1_Inew[S1_I == ""twentyplus""] = 2
+ S1_Inew[S1_I == ""elderly""] = 3
+ S1_Inew[S1_I == ""kids""] = 4
+ S1_Inew[S1_I == ""all""] = 5
+
+ S2_Inew[S2_I == ""adults""] = 1
+ S2_Inew[S2_I == ""twentyplus""] = 2
+ S2_Inew[S2_I == ""elderly""] = 3
+ S2_Inew[S2_I == ""kids""] = 4
+ S2_Inew[S2_I == ""all""] = 5
+
+ S3_Inew[S3_I == ""adults""] = 1
+ S3_Inew[S3_I == ""twentyplus""] = 2
+ S3_Inew[S3_I == ""elderly""] = 3
+ S3_Inew[S3_I == ""kids""] = 4
+ S3_Inew[S3_I == ""all""] = 5
+
+ S4_Inew[S4_I == ""adults""] = 1
+ S4_Inew[S4_I == ""twentyplus""] = 2
+ S4_Inew[S4_I == ""elderly""] = 3
+ S4_Inew[S4_I == ""kids""] = 4
+ S4_Inew[S4_I == ""all""] = 5
+
+ ### M matrices
+ S1_Mnew[S1_M == ""adults""] = 1
+ S1_Mnew[S1_M == ""twentyplus""] = 2
+ S1_Mnew[S1_M == ""elderly""] = 3
+ S1_Mnew[S1_M == ""kids""] = 4
+ S1_Mnew[S1_M == ""all""] = 5
+
+ S2_Mnew[S2_M == ""adults""] = 1
+ S2_Mnew[S2_M == ""twentyplus""] = 2
+ S2_Mnew[S2_M == ""elderly""] = 3
+ S2_Mnew[S2_M == ""kids""] = 4
+ S2_Mnew[S2_M == ""all""] = 5
+
+ S3_Mnew[S3_M == ""adults""] = 1
+ S3_Mnew[S3_M == ""twentyplus""] = 2
+ S3_Mnew[S3_M == ""elderly""] = 3
+ S3_Mnew[S3_M == ""kids""] = 4
+ S3_Mnew[S3_M == ""all""] = 5
+
+ S4_Mnew[S4_M == ""adults""] = 1
+ S4_Mnew[S4_M == ""twentyplus""] = 2
+ S4_Mnew[S4_M == ""elderly""] = 3
+ S4_Mnew[S4_M == ""kids""] = 4
+ S4_Mnew[S4_M == ""all""] = 5
+
+ ### Y matrices
+ S1_Ynew[S1_Y == ""adults""] = 1
+ S1_Ynew[S1_Y == ""twentyplus""] = 2
+ S1_Ynew[S1_Y == ""elderly""] = 3
+ S1_Ynew[S1_Y == ""kids""] = 4
+ S1_Ynew[S1_Y == ""all""] = 5
+
+ S2_Ynew[S2_Y == ""adults""] = 1
+ S2_Ynew[S2_Y == ""twentyplus""] = 2
+ S2_Ynew[S2_Y == ""elderly""] = 3
+ S2_Ynew[S2_Y == ""kids""] = 4
+ S2_Ynew[S2_Y == ""all""] = 5
+
+ S3_Ynew[S3_Y == ""adults""] = 1
+ S3_Ynew[S3_Y == ""twentyplus""] = 2
+ S3_Ynew[S3_Y == ""elderly""] = 3
+ S3_Ynew[S3_Y == ""kids""] = 4
+ S3_Ynew[S3_Y == ""all""] = 5
+
+ S4_Ynew[S4_Y == ""adults""] = 1
+ S4_Ynew[S4_Y == ""twentyplus""] = 2
+ S4_Ynew[S4_Y == ""elderly""] = 3
+ S4_Ynew[S4_Y == ""kids""] = 4
+ S4_Ynew[S4_Y == ""all""] = 5
+}
+
+## ve
+# grey out ve_S = ve_I = 0 for cumulative incidence bc then vaccination has no affect on incidence
+S1_Inew[1,] <- 0
+S2_Inew[1,] <- 0
+S3_Inew[1,] <- 0
+S4_Inew[1,] <- 0
+
+pS1_I <- plot_heatmap(reshape2::melt(S1_Inew), num, param) +
+ ylab(""Cumulative\nincidence\n\n"") +
+ onlyy_theme +
+ ggtitle(""Scenario 1"")
+pS2_I <- plot_heatmap(reshape2::melt(S2_Inew), num, param) +
+ nolabels_theme +
+ ggtitle(""Scenario 2"")
+pS3_I <- plot_heatmap(reshape2::melt(S3_Inew), num, param) +
+ nolabels_theme +
+ ggtitle(""Scenario 3"")
+pS4_I <- plot_heatmap(reshape2::melt(S4_Inew), num, param) +
+ nolabels_theme +
+ ggtitle(""Scenario 4"")
+
+pS1_M <- plot_heatmap(reshape2::melt(S1_Mnew), num, param)+
+ ylab(""Mortality\n\nve_I (%)"") +
+ onlyy_theme
+pS2_M <- plot_heatmap(reshape2::melt(S2_Mnew), num, param) +
+ nolabels_theme
+pS3_M <- plot_heatmap(reshape2::melt(S3_Mnew), num, param) +
+ nolabels_theme
+pS4_M <- plot_heatmap(reshape2::melt(S4_Mnew), num, param) +
+ nolabels_theme
+
+
+pS1_Y <- plot_heatmap(reshape2::melt(S1_Ynew), num, param) +
+ ylab(""Years of\nlife lost\n\n"")
+pS2_Y <- plot_heatmap(reshape2::melt(S2_Ynew), num, param) +
+ onlyx_theme
+pS3_Y <- plot_heatmap(reshape2::melt(S3_Ynew), num, param) +
+ onlyx_theme
+pS4_Y <- plot_heatmap(reshape2::melt(S4_Ynew), num, param) +
+ onlyx_theme
+
+# export as 9"" x 6""
+g <- ggarrange(pS1_I, pS2_I, pS3_I, pS4_I,
+ pS1_M, pS2_M, pS3_M, pS4_M,
+ pS1_Y, pS2_Y, pS3_Y, pS4_Y,
+ nrow = 3,
+ bottom = textGrob(""Total vaccine supply (% of population)"", gp = gpar(fontsize = 12)),
+ padding = unit(0.5, ""line""))
+
+pdf(""C:/Users/bubar/Documents/Vaccine Strategy/Plots/finaldraft_plots/heatmap_NTB.pdf"",
+ height = 6, width = 8.5)
+g
+dev.off()
+","R"
+"Vaccine","kbubar/vaccine_prioritization","run_heatmap_rollout.R",".R","6078","169","# _____________________________________________________________________
+# FUNCTIONS ----
+# _____________________________________________________________________
+source(""run_sim.R"")
+source(""helper_functions.R"")
+country <- ""USA""
+source(""setup.R"")
+# _____________________________________________________________________
+# SET UP ----
+# _____________________________________________________________________
+# scale the susceptibility for each country to get R0 = 1.15, 1.5
+scale_115 <- scale_u_for_R0(u_var, C, 1.15)
+scale_15 <- scale_u_for_R0(u_var, C, 1.5)
+
+C_115 <- C/scale_115
+C_15 <- C/scale_15
+
+rollouts <- c(0.0005, 0.001, 0.002, 0.003, 0.004, 0.005, 0.0075, 0.01)
+
+ptm <- proc.time()
+
+# clusters
+cores=detectCores()
+cl <- makeCluster(cores[1]-1) # to not overload your computer
+registerDoParallel(cl)
+
+HM <- foreach(num_perday = rollouts) %dopar% {
+ # * R0 = 1.15
+ list_all <- list_kids <- list_adults <- list_elderly <- list_twentyplus <- vector(mode = ""list"")
+
+ for (i in seq(0, 50, by = 1)){
+ j <- i/100
+ list_all[[paste0(i)]] <- run_sim(C_115, j, ""all"", num_perday, v_e_type, this_v_e)
+ list_kids[[paste0(i)]] <- run_sim(C_115, j, ""kids"", num_perday, v_e_type, this_v_e)
+ list_adults[[paste0(i)]] <- run_sim(C_115, j, ""adults"", num_perday, v_e_type, this_v_e)
+ list_elderly[[paste0(i)]] <- run_sim(C_115, j, ""elderly"", num_perday, v_e_type, this_v_e)
+ list_twentyplus[[paste0(i)]] <- run_sim(C_115, j, ""twentyplus"", num_perday, v_e_type, this_v_e)
+ }
+ x_vec <- seq(1, 50, by = 1)
+ I1 <- sapply(x_vec, FUN = function(x) get_best_strat_cases_new(x, list_all,list_kids,list_adults,
+ list_elderly,list_twentyplus))
+ M1 <- sapply(x_vec, FUN = function(x) get_best_strat_deaths_new(x, list_all,list_kids,list_adults,
+ list_elderly,list_twentyplus))
+ Y1 <- sapply(x_vec, FUN = function(x) get_best_strat_yll_new(x, list_all,list_kids,list_adults,
+ list_elderly,list_twentyplus))
+
+ # * R0 = 1.5
+ list_all <- list_kids <- list_adults <- list_elderly <- list_twentyplus <- vector(mode = ""list"")
+ for (i in seq(0, 50, by = 1)){
+ j <- i/100
+ list_all[[paste0(i)]] <- run_sim(C_15, j, ""all"", num_perday, v_e_type, this_v_e)
+ list_kids[[paste0(i)]] <- run_sim(C_15, j, ""kids"", num_perday, v_e_type, this_v_e)
+ list_adults[[paste0(i)]] <- run_sim(C_15, j, ""adults"", num_perday, v_e_type, this_v_e)
+ list_elderly[[paste0(i)]] <- run_sim(C_15, j, ""elderly"", num_perday, v_e_type, this_v_e)
+ list_twentyplus[[paste0(i)]] <- run_sim(C_15, j, ""twentyplus"", num_perday, v_e_type, this_v_e)
+ }
+
+ x_vec <- seq(1, 50, by = 1)
+ I2 <- sapply(x_vec, FUN = function(x) get_best_strat_cases_new(x, list_all,list_kids,list_adults,
+ list_elderly,list_twentyplus))
+ M2 <- sapply(x_vec, FUN = function(x) get_best_strat_deaths_new(x, list_all,list_kids,list_adults,
+ list_elderly,list_twentyplus))
+ Y2 <- sapply(x_vec, FUN = function(x) get_best_strat_yll_new(x, list_all,list_kids,list_adults,
+ list_elderly,list_twentyplus))
+ list(s1 = cbind(I1, M1, Y1),
+ s2 = cbind(I2, M2, Y2))
+}
+stopCluster(cl)
+
+saveRDS(HM, ""HM_USA_rollout.RData"")
+proc.time() - ptm
+
+# _____________________________________________________________________
+# PLOT ----
+# _____________________________________________________________________
+HM <- readRDS(""HM_USA_rollout.RData"")
+
+param <- ""rollout""
+
+num <- length(HM)
+
+S1_I <- S2_I <- S1_M <- S2_M <- S1_Y <- S2_Y <- matrix(NA, num, 50)
+
+# Get data in right form for plotting
+for (i in 1:num){
+ S1_I[i ,] <- HM[[i]]$s1[, 1]
+ S2_I[i ,] <- HM[[i]]$s2[, 1]
+
+ S1_M[i ,] <- HM[[i]]$s1[, 2]
+ S2_M[i ,] <- HM[[i]]$s2[, 2]
+
+ S1_Y[i ,] <- HM[[i]]$s1[, 3]
+ S2_Y[i ,] <- HM[[i]]$s2[, 3]
+}
+
+S1_Inew <- S2_Inew <- S1_Mnew <- S2_Mnew <- S1_Ynew <- S2_Ynew <- matrix(NA, num, 50)
+
+#adults = 1 (20-49)
+#twentyplus = 2
+#elderly = 3
+#kids = 4
+#all = 5
+### I Matrices
+for(i in 1){
+ S1_Inew[S1_I == ""adults""] = 1
+ S1_Inew[S1_I == ""twentyplus""] = 2
+ S1_Inew[S1_I == ""elderly""] = 3
+ S1_Inew[S1_I == ""kids""] = 4
+ S1_Inew[S1_I == ""all""] = 5
+
+ S2_Inew[S2_I == ""adults""] = 1
+ S2_Inew[S2_I == ""twentyplus""] = 2
+ S2_Inew[S2_I == ""elderly""] = 3
+ S2_Inew[S2_I == ""kids""] = 4
+ S2_Inew[S2_I == ""all""] = 5
+
+ ### M matrices
+ S1_Mnew[S1_M == ""adults""] = 1
+ S1_Mnew[S1_M == ""twentyplus""] = 2
+ S1_Mnew[S1_M == ""elderly""] = 3
+ S1_Mnew[S1_M == ""kids""] = 4
+ S1_Mnew[S1_M == ""all""] = 5
+
+ S2_Mnew[S2_M == ""adults""] = 1
+ S2_Mnew[S2_M == ""twentyplus""] = 2
+ S2_Mnew[S2_M == ""elderly""] = 3
+ S2_Mnew[S2_M == ""kids""] = 4
+ S2_Mnew[S2_M == ""all""] = 5
+
+ ### Y matrices
+ S1_Ynew[S1_Y == ""adults""] = 1
+ S1_Ynew[S1_Y == ""twentyplus""] = 2
+ S1_Ynew[S1_Y == ""elderly""] = 3
+ S1_Ynew[S1_Y == ""kids""] = 4
+ S1_Ynew[S1_Y == ""all""] = 5
+
+ S2_Ynew[S2_Y == ""adults""] = 1
+ S2_Ynew[S2_Y == ""twentyplus""] = 2
+ S2_Ynew[S2_Y == ""elderly""] = 3
+ S2_Ynew[S2_Y == ""kids""] = 4
+ S2_Ynew[S2_Y == ""all""] = 5
+}
+
+## R0
+pS1_I <- plot_heatmap(reshape2::melt(S1_Inew), num, param) +
+ ylab(""Cumulative\nincidence\n\n"") +
+ onlyy_theme +
+ ggtitle(""R0 = 1.15"")
+pS2_I <- plot_heatmap(reshape2::melt(S2_Inew), num, param) +
+ nolabels_theme +
+ ggtitle(""R0 = 1.5"")
+
+pS1_M <- plot_heatmap(reshape2::melt(S1_Mnew), num, param)+
+ ylab(""Mortality\n\nRollout speed (% per day)"") +
+ onlyy_theme
+pS2_M <- plot_heatmap(reshape2::melt(S2_Mnew), num, param) +
+ nolabels_theme
+
+pS1_Y <- plot_heatmap(reshape2::melt(S1_Ynew), num, param) +
+ ylab(""Years of\nlife lost\n\n"")
+pS2_Y <- plot_heatmap(reshape2::melt(S2_Ynew), num, param) +
+ onlyx_theme
+
+# export as 8.5"" x 6""
+g <- ggarrange(pS1_I, pS2_I, pS1_M, pS2_M, pS1_Y, pS2_Y,
+ bottom = textGrob(""Total vaccine supply (% of population)"", gp = gpar(fontsize = 12)),
+ padding = unit(0.5, ""line""))
+
+","R"
+"Vaccine","kbubar/vaccine_prioritization","helper_functions.R",".R","49054","1401","library(grid)
+library(dplyr)
+library(data.table)
+
+move_vaccinated_event <- function(t, x, parms){
+ v_e <- parms$v_e
+ v_e_type <- parms$v_e_type
+ num_perday <- parms$num_perday
+ vax_proportion <- parms$vax_proportion
+ groups <- parms$groups
+ sp <- parms$sp
+ se <- parms$se
+ pop_total <- parms$pop_total
+
+ # move those who are vaccinated in a given day
+ num_compartment <- 13
+ num_groups <- (length(x)-1)/num_compartment
+ S <- as.matrix(x[1:num_groups])
+ Sv <- as.matrix(x[(1*num_groups+1):(2*num_groups)])
+ Sx <- as.matrix(x[(2*num_groups+1):(3*num_groups)])
+ E <- as.matrix(x[(3*num_groups+1):(4*num_groups)])
+ Ev <- as.matrix(x[(4*num_groups+1):(5*num_groups)])
+ Ex <- as.matrix(x[(5*num_groups+1):(6*num_groups)])
+ I <- as.matrix(x[(6*num_groups+1):(7*num_groups)])
+ Iv <- as.matrix(x[(7*num_groups+1):(8*num_groups)])
+ Ix <- as.matrix(x[(8*num_groups+1):(9*num_groups)])
+ R <- as.matrix(x[(9*num_groups+1):(10*num_groups)])
+ Rv <- as.matrix(x[(10*num_groups+1):(11*num_groups)])
+ Rx <- as.matrix(x[(11*num_groups+1):(12*num_groups)])
+ D <- as.matrix(x[(12*num_groups+1):(13*num_groups)])
+ vax_supply <- x[13*num_groups+1]
+
+ if (vax_supply >= num_perday*pop_total){
+ nvax <- num_perday*pop_total
+ vax_supply <- vax_supply - num_perday*pop_total
+ } else {
+ nvax <- vax_supply
+ vax_supply <- 0
+ }
+
+ vax_distribution <- nvax*vax_proportion
+ vax_eligible <- (S+E)*sp + R*(1-se)
+ if (any(vax_distribution > vax_eligible)){
+ # make sure everyone in the specificed age groups are vaccinated
+ if (!all(vax_distribution[groups] > vax_eligible[groups])){
+ temp <- vax_distribution
+ temp[vax_distribution > vax_eligible] <- vax_eligible[vax_distribution > vax_eligible]
+ leftover_vax <- sum(vax_distribution - temp)
+
+ full_groups <- 1:9
+ full_groups <- full_groups[vax_distribution > vax_eligible]
+ leftover_groups <- groups[!groups %in% full_groups]
+ people_to_vax <- sum(vax_eligible[leftover_groups])
+ vax_proportion <- rep(0, num_groups)
+ vax_proportion[leftover_groups] <- vax_eligible[leftover_groups]/people_to_vax
+
+ vax_leftover_dist <- leftover_vax*vax_proportion
+ vax_distribution <- temp + vax_leftover_dist
+ }
+ #distribute vaccines to all other age groups if there's doses left over after strategy specified
+ #age groups
+ if (any(vax_distribution > vax_eligible)){
+ temp <- vax_distribution
+ temp[vax_distribution > vax_eligible] <- vax_eligible[vax_distribution > vax_eligible]
+ leftover_vax <- sum(vax_distribution - temp)
+
+ leftover_groups <- 1:9
+ leftover_groups <- leftover_groups[!leftover_groups %in% groups]
+ people_to_vax <- sum(vax_eligible[leftover_groups])
+ vax_proportion <- rep(0, num_groups)
+ vax_proportion[leftover_groups] <- vax_eligible[leftover_groups]/people_to_vax
+
+ vax_leftover_dist <- leftover_vax*vax_proportion
+ vax_distribution <- temp + vax_leftover_dist
+ }
+ # don't go over the number eligible
+ if (any(vax_distribution > vax_eligible)){
+ vax_distribution[vax_distribution > vax_eligible] = vax_eligible
+ }
+ }
+
+ alpha <- vax_distribution/vax_eligible
+ alpha[alpha == Inf] <- 0 # no people in S,E,R
+ alpha[is.nan(alpha)] <- 0 # no vax left and no people in S,E,R
+ if(any(alpha > 1)){print(""WARNING: alpha > 1 in move_vaccinated"")
+ alpha[alpha>1] <- 0} # more vaccines avaliable than eligible people
+
+ if (v_e_type == ""leaky"") {
+ dS <- -(S*alpha*sp) - (S*alpha*(1-sp))
+ dSv <- (S*alpha*sp)
+ dSx <- (S*alpha*(1-sp))
+
+ dE <- -(E*alpha*sp) - (E*alpha*(1-sp))
+ dEv <- (E*alpha*sp)
+ dEx <- (E*alpha*(1-sp))
+ } else {
+ # all-or-nothing
+ dS <- -(S*alpha*sp) - (S*alpha*(1-sp))
+ dSv <- (S*alpha*sp*v_e)
+ dSx <- (S*alpha*sp*(1-v_e)) + (S*alpha*(1-sp))
+
+ dE <- - (E*alpha*sp) - (E*alpha*(1-sp))
+ dEv <- (E*alpha*sp*v_e)
+ dEx <- (E*alpha*sp*(1-v_e)) + (E*alpha*(1-sp))
+ }
+
+ dR <- - (R*alpha*(1-se)) - (R*alpha*se)
+ dRv <- (R*alpha*(1-se))
+ dRx <- (R*alpha*se)
+
+ # update compartments
+ S <- S + dS
+ Sv <- Sv + dSv
+ Sx <- Sx + dSx
+ E <- E + dE
+ Ev <- Ev + dEv
+ Ex <- Ex + dEx
+ R <- R + dR
+ Rv <- Rv + dRv
+ Rx <- Rx + dRx
+
+ # output updated compartments
+ out <- c(S,Sv,Sx,E,Ev,Ex,I,Iv,Ix,R,Rv,Rx,D,vax_supply)
+}
+
+calculate_derivatives = function(t, x, parameters){
+ # x is a vector of length (# model compartment types)*(# age groups)
+ # S, E, I, R etc. are vectors of length num_groups
+ num_compartment <- 13
+ num_groups <- (length(x)-1)/num_compartment
+ S <- as.matrix(x[1:num_groups])
+ Sv <- as.matrix(x[(1*num_groups+1):(2*num_groups)])
+ Sx <- as.matrix(x[(2*num_groups+1):(3*num_groups)])
+ E <- as.matrix(x[(3*num_groups+1):(4*num_groups)])
+ Ev <- as.matrix(x[(4*num_groups+1):(5*num_groups)])
+ Ex <- as.matrix(x[(5*num_groups+1):(6*num_groups)])
+ I <- as.matrix(x[(6*num_groups+1):(7*num_groups)])
+ Iv <- as.matrix(x[(7*num_groups+1):(8*num_groups)])
+ Ix <- as.matrix(x[(8*num_groups+1):(9*num_groups)])
+ R <- as.matrix(x[(9*num_groups+1):(10*num_groups)])
+ Rv <- as.matrix(x[(10*num_groups+1):(11*num_groups)])
+ Rx <- as.matrix(x[(11*num_groups+1):(12*num_groups)])
+ D <- as.matrix(x[(12*num_groups+1):(13*num_groups)])
+ vax_supply <- x[13*num_groups+1]
+
+ S[S < .Machine$double.eps] <- 0
+ Sv[Sv < .Machine$double.eps] <- 0
+ Sx[Sx < .Machine$double.eps] <- 0
+ E[E < .Machine$double.eps] <- 0
+ Ev[Ev < .Machine$double.eps] <- 0
+ Ex[Ex < .Machine$double.eps] <- 0
+ I[I < .Machine$double.eps] <- 0
+ Iv[Iv < .Machine$double.eps] <- 0
+ Ix[Ix < .Machine$double.eps] <- 0
+ R[R < .Machine$double.eps] <- 0
+ Rv[Rv < .Machine$double.eps] <- 0
+ Rx[Rx < .Machine$double.eps] <- 0
+ D[D < .Machine$double.eps] <- 0
+
+ u <- parameters$u
+ C <- parameters$C
+ d_E <- parameters$d_E
+ d_I <- parameters$d_I
+ v_e <- parameters$v_e
+ v_e_type <- parameters$v_e_type
+
+ lambda <- C%*%((I+Iv+Ix)/(N_i-D))*u
+
+ if (v_e_type == ""leaky"") {
+ dSv <- -(Sv*(1-v_e)*lambda)
+ dEv <- (Sv*(1-v_e)*lambda) - d_E*Ev
+ } else {
+ # all-or-nothing
+ dSv <- rep(0, num_groups)
+ dEv <- -d_E*as.matrix(Ev)
+ }
+
+ dS <- -(S*lambda)
+ dSx <- -(Sx*lambda)
+
+ dE <- S*lambda - d_E*E
+ dEx <- Sx*lambda - d_E*Ex
+
+ dI <- E*d_E - I*d_I
+ dIv <- Ev*d_E - Iv*d_I
+ dIx <- Ex*d_E - Ix*d_I
+
+ dR <- I*d_I*(1-IFR)
+ dRv <- Iv*d_I*(1-IFR)
+ dRx <- Ix*d_I*(1-IFR)
+
+ dD <- I*d_I*IFR + Iv*d_I*IFR + Ix*d_I*IFR
+
+ dvax_supply <- 0
+
+ out <- c(dS,dSv,dSx,dE,dEv,dEx,dI,dIv,dIx,dR,dRv,dRx,dD,dvax_supply)
+ list(out)
+}
+
+calculate_derivatives_NTB = function(t, x, parameters){
+ # x is a vector of length (# model compartment types)*(# age groups)
+ # S, E, I, R etc. are vectors of length num_groups
+ num_compartment <- 13
+ num_groups <- (length(x)-1)/num_compartment
+ S <- as.matrix(x[1:num_groups])
+ Sv <- as.matrix(x[(1*num_groups+1):(2*num_groups)])
+ Sx <- as.matrix(x[(2*num_groups+1):(3*num_groups)])
+ E <- as.matrix(x[(3*num_groups+1):(4*num_groups)])
+ Ev <- as.matrix(x[(4*num_groups+1):(5*num_groups)])
+ Ex <- as.matrix(x[(5*num_groups+1):(6*num_groups)])
+ I <- as.matrix(x[(6*num_groups+1):(7*num_groups)])
+ Iv <- as.matrix(x[(7*num_groups+1):(8*num_groups)])
+ Ix <- as.matrix(x[(8*num_groups+1):(9*num_groups)])
+ R <- as.matrix(x[(9*num_groups+1):(10*num_groups)])
+ Rv <- as.matrix(x[(10*num_groups+1):(11*num_groups)])
+ Rx <- as.matrix(x[(11*num_groups+1):(12*num_groups)])
+ D <- as.matrix(x[(12*num_groups+1):(13*num_groups)])
+ vax_supply <- x[13*num_groups+1]
+
+ S[S < .Machine$double.eps] <- 0
+ Sv[Sv < .Machine$double.eps] <- 0
+ Sx[Sx < .Machine$double.eps] <- 0
+ E[E < .Machine$double.eps] <- 0
+ Ev[Ev < .Machine$double.eps] <- 0
+ Ex[Ex < .Machine$double.eps] <- 0
+ I[I < .Machine$double.eps] <- 0
+ Iv[Iv < .Machine$double.eps] <- 0
+ Ix[Ix < .Machine$double.eps] <- 0
+ R[R < .Machine$double.eps] <- 0
+ Rv[Rv < .Machine$double.eps] <- 0
+ Rx[Rx < .Machine$double.eps] <- 0
+ D[D < .Machine$double.eps] <- 0
+
+ u <- parameters$u
+ C <- parameters$C
+ ve_S <- parameters$ve_S
+ ve_I <- parameters$ve_I
+ ve_P <- parameters$ve_P
+
+ lambda <- C %*% ((I+(1-ve_I)*Iv + Ix)/(N_i-D)) * u
+ lambda_V <- (1-ve_S)*C %*% ((I+(1-ve_I)*Iv + Ix)/(N_i-D)) * u
+
+ dSv <- -Sv*lambda_V
+ dEv <- Sv*lambda_V - d_E*Ev
+
+ dS <- -S*lambda
+ dE <- S*lambda - d_E*E
+
+ dSx <- -Sx*lambda
+ dEx <- Sx*lambda - d_E*Ex
+
+ dI <- E*d_E - I*d_I
+ dIv <- Ev*d_E - Iv*d_I
+ dIx <- Ex*d_E - Ix*d_I
+
+ dR <- I*d_I*(1-IFR)
+ dRv <- Iv*d_I*(1 - (1-ve_P)*IFR)
+ dRx <- Ix*d_I*(1-IFR)
+
+ dD <- I*d_I*IFR + Iv*d_I*(1-ve_P)*IFR + Ix*d_I*IFR
+
+ dvax_supply <- 0
+
+ out <- c(dS,dSv,dSx,dE,dEv,dEx,dI,dIv,dIx,dR,dRv,dRx,dD,dvax_supply)
+ list(out)
+}
+
+get_v_e = function(p, y0, hinge_age){
+ # INPUT: p is the final v_e % (as a decimal)
+ # i.e. p = 1 -> perfect vaccine at all ages
+ # y0 is the baseline v_e
+ # hinge_age is the age group that v_e begins decreasing after
+ # ASSUMPTIONS: perfect v_e up to hinge age then linear decline to p
+ # OUTPUT: vector v_e
+
+ x <- c(0, 10, 20, 30, 40, 50, 60, 70, 80)
+ y <- y0 + ((p-y0)/(80-hinge_age))*(x - hinge_age)
+ index <- match(c(hinge_age), x)
+ y[1:index] <- y0
+
+ y
+}
+
+get_best_strat_deaths_new = function(supply, all, kids, adults,
+ elderly, twentyplus){
+ # supply is the vaccine supply of interest as an integer
+
+ total_deaths <- c(compute_total_deaths_new(all[[supply + 1]]),
+ compute_total_deaths_new(kids[[supply + 1]]),
+ compute_total_deaths_new(adults[[supply + 1]]),
+ compute_total_deaths_new(elderly[[supply + 1]]),
+ compute_total_deaths_new(twentyplus[[supply + 1]]))
+
+ baseline_deaths <- compute_total_deaths_new(all$`0`)
+ reduction_in_deaths <- (1 - (total_deaths/baseline_deaths))*100
+
+ strats <- c(""all"", ""kids"", ""adults"", ""elderly"", ""twentyplus"")
+ strats[which.max(reduction_in_deaths)]
+}
+
+get_best_strat_cases_new = function(supply, all, kids, adults,
+ elderly, twentyplus){
+ # supply is the vaccine supply of interest as an integer
+
+ total_cases <- c(compute_total_cases_new(all[[supply + 1]]),
+ compute_total_cases_new(kids[[supply + 1]]),
+ compute_total_cases_new(adults[[supply + 1]]),
+ compute_total_cases_new(elderly[[supply + 1]]),
+ compute_total_cases_new(twentyplus[[supply + 1]]))
+
+ baseline_cases <- compute_total_cases_new(all$`0`)
+ reduction_in_cases <- (1 - (total_cases/baseline_cases))*100
+
+ strats <- c(""all"", ""kids"", ""adults"", ""elderly"", ""twentyplus"")
+ strats[which.max(reduction_in_cases)]
+}
+
+get_best_strat_yll_new = function(supply, all, kids, adults,
+ elderly, twentyplus){
+ # supply is the vaccine supply of interest as an integer
+
+ total_YLL <- c(compute_total_YLL_new(all[[supply + 1]]),
+ compute_total_YLL_new(kids[[supply + 1]]),
+ compute_total_YLL_new(adults[[supply + 1]]),
+ compute_total_YLL_new(elderly[[supply + 1]]),
+ compute_total_YLL_new(twentyplus[[supply + 1]]))
+
+ baseline_YLL <- compute_total_YLL_new(all$`0`)
+ reduction_in_YLL <- (1 - (total_YLL/baseline_YLL))*100
+
+ strats <- c(""all"", ""kids"", ""adults"", ""elderly"", ""twentyplus"")
+ strats[which.max(reduction_in_YLL)]
+}
+
+run_v_e_var = function(p, v_e_baseline, hinge_age, vax_amount, num_perday, v_e_type){
+ this_v_e <- get_v_e(p, y0 = v_e_baseline, hinge_age = hinge_age) # hinge age is age group that v_e begins decreasing after
+
+ all <- kids <- adults <- elderly <- twentyplus <- vector(mode = ""list"")
+
+ if (vax_amount > 0){
+ for (i in seq(0, vax_amount, by = vax_amount)){
+ j <- vax_amount/100
+ all[[paste0(i)]] <- run_sim_new(this_C, j, ""all"", num_perday, v_e_type, this_v_e)
+ kids[[paste0(i)]] <- run_sim_new(this_C, j, ""kids"", num_perday, v_e_type, this_v_e)
+ adults[[paste0(i)]] <- run_sim_new(this_C, j, ""adults"", num_perday, v_e_type, this_v_e)
+ elderly[[paste0(i)]] <- run_sim_new(this_C, j, ""elderly"", num_perday, v_e_type, this_v_e)
+ twentyplus[[paste0(i)]] <- run_sim_new(this_C, j, ""twentyplus"", num_perday, v_e_type, this_v_e)
+ }
+ } else{
+ i <- 0
+ all[[paste0(i)]] <- run_sim_new(this_C, vax_amount, ""all"", num_perday, v_e_type, this_v_e)
+ kids[[paste0(i)]] <- run_sim_new(this_C, vax_amount, ""kids"", num_perday, v_e_type, this_v_e)
+ adults[[paste0(i)]] <- run_sim_new(this_C, vax_amount, ""adults"", num_perday, v_e_type, this_v_e)
+ elderly[[paste0(i)]] <- run_sim_new(this_C, vax_amount, ""elderly"", num_perday, v_e_type, this_v_e)
+ twentyplus[[paste0(i)]] <- run_sim_new(this_C, vax_amount, ""twentyplus"", num_perday, v_e_type, this_v_e)
+ i <- i + 1
+ all[[paste0(i)]] <- all[[1]]
+ kids[[paste0(i)]] <- kids[[1]]
+ adults[[paste0(i)]] <- adults[[1]]
+ elderly[[paste0(i)]] <- elderly[[1]]
+ twentyplus[[paste0(i)]] <- twentyplus[[1]]
+ }
+ return(get_best_strat_for_tipping_point(all, kids, adults, elderly, twentyplus))
+}
+
+v_e_bisection = function(v_e_baseline, hinge_age, vax_amount, num_perday, v_e_type){
+ p_high <- 1
+ p_low <- 0
+
+ temp <- run_v_e_var(p_high, v_e_baseline, hinge_age, vax_amount, num_perday, v_e_type)
+ if (temp != ""elderly""){
+ print(""Warning: elderly is not most strategic to start"")
+ return(list(100, temp))
+ }
+
+ temp <- run_v_e_var(p_low, v_e_baseline, hinge_age, vax_amount, num_perday, v_e_type)
+ if (temp == ""elderly""){
+ print(""Warning: elderly is always most strategic"")
+ return(list(0, temp))
+ }
+
+ running = TRUE
+ while(running){
+ p_next = (p_high + p_low)/2
+ temp <- run_v_e_var(p_next, v_e_baseline, hinge_age, vax_amount, num_perday, v_e_type)
+ if (temp == ""elderly""){
+ p_high <- p_next
+ } else {
+ p_low <- p_next
+ }
+
+ if ((p_high - p_low) < 0.01){
+ strat <- run_v_e_var(p_low, v_e_baseline, hinge_age, vax_amount, num_perday, v_e_type)
+ out <- list(p_next*100, strat)
+ return(out)
+ }
+ }
+}
+
+get_best_strat_for_tipping_point = function(all, kids, adults, elderly, twentyplus){
+ # supply is the vaccine supply of interest as an integer
+
+ total_deaths <- c(compute_total_deaths_new(all[[2]]),
+ compute_total_deaths_new(kids[[2]]),
+ compute_total_deaths_new(adults[[2]]),
+ compute_total_deaths_new(elderly[[2]]),
+ compute_total_deaths_new(twentyplus[[2]]))
+
+ baseline_deaths <- compute_total_deaths_new(all$`0`)
+ reduction_in_deaths <- (1 - (total_deaths/baseline_deaths))*100
+
+ strats <- c(""all"", ""kids"", ""adults"", ""elderly"", ""twentyplus"")
+ strats[which.max(reduction_in_deaths)]
+}
+
+compute_R0 = function(u, C){
+ gamma <- 1/5 # recovery period (I -> R), ref: Davies
+
+ # Davies NGM
+ Du <- diag(u, 9)
+ Dy <- diag(1/gamma, 9)
+
+ NGM <- Du %*% C %*% Dy
+ R0 <- abs(eigen(NGM)$values[1])
+}
+
+compute_R_with_sero = function(u, C, sero){
+ gamma <- 1/5 # recovery period (I -> R), ref: Davies
+
+ # Davies NGM
+ Du <- diag(u, 9)
+ Dy <- diag(1/gamma, 9)
+ D_sero <- diag(1 - sero)
+
+ NGM <- D_sero %*% Du %*% C %*% Dy
+ R <- abs(eigen(NGM)$values[1])
+}
+
+scale_u_for_R0 = function(u, C, wanted_R0){
+ scalehigh <- 10
+ scalelow <- 100
+
+ R0_high <- compute_R0(u/scalehigh, C)
+ R0_low <- compute_R0(u/scalelow, C)
+
+ if (R0_high < wanted_R0 || R0_low > wanted_R0){
+ print(""Error: wanted_R0 is not in the original range of R0 values"")
+ return(-1)
+ }
+
+ running = TRUE
+ while(running){
+ scale_next = (scalehigh + scalelow)/2
+ temp <- compute_R0(u/scale_next, C)
+ if (temp > wanted_R0){
+ scalehigh <- scale_next
+ } else {
+ scalelow <- scale_next
+ }
+
+ if (abs(temp - wanted_R0) < 0.0001){
+ return(scale_next)
+ }
+ }
+}
+
+compute_total_cases_new = function(df){
+ infections <- rep(0,num_groups) # number of age groups
+
+ R_index <- 83 # col number for R
+ Rv_index <- 92 # col number for Rv
+ Rx_index <- 101 # col number for Rx
+ D_index <- 110
+
+ for (i in 1:num_groups) {
+ # infections = total # recovered - initial recovered (seropositive)
+ infections[i] <- df[dim(df)[1], R_index] - df[1, R_index] +
+ df[dim(df)[1], Rv_index] - df[1, Rv_index] +
+ df[dim(df)[1], Rx_index] - df[1, Rx_index] +
+ df[dim(df)[1], D_index]
+ R_index <- R_index + 1
+ Rv_index <- Rv_index + 1
+ Rx_index <- Rx_index + 1
+ D_index <- D_index + 1
+ }
+ tot_infections <- sum(infections)/pop_total * 100
+}
+
+compute_total_deaths_new = function(df){
+ deaths <- rep(0,num_groups)
+ D_index <- 110
+
+ for (i in 1:num_groups) {
+ deaths[i] <- df[dim(df)[1], D_index]
+ D_index <- D_index + 1
+ }
+
+ tot_deaths <- sum(deaths)/pop_total * 100
+}
+
+compute_total_YLL_new = function(df){
+ YLL <- rep(0,num_groups)
+
+ D_index <- 110
+ for (i in 1:num_groups) {
+ deaths_temp <- df[dim(df)[1], D_index]
+ YLL[i] <- YLL_vec[i]*deaths_temp
+ D_index <- D_index + 1
+ }
+
+ tot_YLL <- sum(YLL) # absolute number of YLL
+}
+
+compile_total_cases = function(total){
+ count <- 1
+ for (i in list_all){
+ total[count] <- compute_total_cases_new(i)
+ count <- count + 1
+ }
+ for (i in list_kids){
+ total[count] <- compute_total_cases_new(i)
+ count <- count + 1
+ }
+ for (i in list_adults){
+ total[count] <- compute_total_cases_new(i)
+ count <- count + 1
+ }
+ for (i in list_elderly){
+ total[count] <- compute_total_cases_new(i)
+ count <- count + 1
+ }
+ for (i in list_twentyplus){
+ total[count] <- compute_total_cases_new(i)
+ count <- count + 1
+ }
+
+ total
+}
+
+compile_total_cases_var = function(total){
+ count <- 1
+ for (i in list_all){
+ total[count] <- compute_total_cases_new(i)
+ count <- count + 1
+ }
+ for (i in list_all_var){
+ total[count] <- compute_total_cases_new(i)
+ count <- count + 1
+ }
+ for (i in list_kids){
+ total[count] <- compute_total_cases_new(i)
+ count <- count + 1
+ }
+ for (i in list_kids_var){
+ total[count] <- compute_total_cases_new(i)
+ count <- count + 1
+ }
+ for (i in list_adults){
+ total[count] <- compute_total_cases_new(i)
+ count <- count + 1
+ }
+ for (i in list_adults_var){
+ total[count] <- compute_total_cases_new(i)
+ count <- count + 1
+ }
+ for (i in list_elderly){
+ total[count] <- compute_total_cases_new(i)
+ count <- count + 1
+ }
+ for (i in list_elderly_var){
+ total[count] <- compute_total_cases_new(i)
+ count <- count + 1
+ }
+ for (i in list_twentyplus){
+ total[count] <- compute_total_cases_new(i)
+ count <- count + 1
+ }
+ for (i in list_twentyplus_var){
+ total[count] <- compute_total_cases_new(i)
+ count <- count + 1
+ }
+
+ total
+}
+
+compile_total_deaths = function(total){
+ count <- 1
+ for (i in list_all){
+ total[count] <- compute_total_deaths_new(i)
+ count <- count + 1
+ }
+ for (i in list_kids){
+ total[count] <- compute_total_deaths_new(i)
+ count <- count + 1
+ }
+ for (i in list_adults){
+ total[count] <- compute_total_deaths_new(i)
+ count <- count + 1
+ }
+ for (i in list_elderly){
+ total[count] <- compute_total_deaths_new(i)
+ count <- count + 1
+ }
+ for (i in list_twentyplus){
+ total[count] <- compute_total_deaths_new(i)
+ count <- count + 1
+ }
+
+ total
+}
+
+compile_total_deaths_var = function(total){
+ count <- 1
+ for (i in list_all){
+ total[count] <- compute_total_deaths_new(i)
+ count <- count + 1
+ }
+ for (i in list_all_var){
+ total[count] <- compute_total_deaths_new(i)
+ count <- count + 1
+ }
+ for (i in list_kids){
+ total[count] <- compute_total_deaths_new(i)
+ count <- count + 1
+ }
+ for (i in list_kids_var){
+ total[count] <- compute_total_deaths_new(i)
+ count <- count + 1
+ }
+ for (i in list_adults){
+ total[count] <- compute_total_deaths_new(i)
+ count <- count + 1
+ }
+ for (i in list_adults_var){
+ total[count] <- compute_total_deaths_new(i)
+ count <- count + 1
+ }
+ for (i in list_elderly){
+ total[count] <- compute_total_deaths_new(i)
+ count <- count + 1
+ }
+ for (i in list_elderly_var){
+ total[count] <- compute_total_deaths_new(i)
+ count <- count + 1
+ }
+ for (i in list_twentyplus){
+ total[count] <- compute_total_deaths_new(i)
+ count <- count + 1
+ }
+ for (i in list_twentyplus_var){
+ total[count] <- compute_total_deaths_new(i)
+ count <- count + 1
+ }
+ total
+}
+
+compile_total_YLL = function(total){
+ count <- 1
+ for (i in list_all){
+ total[count] <- compute_total_YLL_new(i)
+ count <- count + 1
+ }
+ for (i in list_kids){
+ total[count] <- compute_total_YLL_new(i)
+ count <- count + 1
+ }
+ for (i in list_adults){
+ total[count] <- compute_total_YLL_new(i)
+ count <- count + 1
+ }
+ for (i in list_elderly){
+ total[count] <- compute_total_YLL_new(i)
+ count <- count + 1
+ }
+ for (i in list_twentyplus){
+ total[count] <- compute_total_YLL_new(i)
+ count <- count + 1
+ }
+
+ total
+}
+
+compile_total_YLL_var = function(total){
+ count <- 1
+ for (i in list_all){
+ total[count] <- compute_total_YLL_new(i)
+ count <- count + 1
+ }
+ for (i in list_all_var){
+ total[count] <- compute_total_YLL_new(i)
+ count <- count + 1
+ }
+ for (i in list_kids){
+ total[count] <- compute_total_YLL_new(i)
+ count <- count + 1
+ }
+ for (i in list_kids_var){
+ total[count] <- compute_total_YLL_new(i)
+ count <- count + 1
+ }
+ for (i in list_adults){
+ total[count] <- compute_total_YLL_new(i)
+ count <- count + 1
+ }
+ for (i in list_adults_var){
+ total[count] <- compute_total_YLL_new(i)
+ count <- count + 1
+ }
+ for (i in list_elderly){
+ total[count] <- compute_total_YLL_new(i)
+ count <- count + 1
+ }
+ for (i in list_elderly_var){
+ total[count] <- compute_total_YLL_new(i)
+ count <- count + 1
+ }
+ for (i in list_twentyplus){
+ total[count] <- compute_total_YLL_new(i)
+ count <- count + 1
+ }
+ for (i in list_twentyplus_var){
+ total[count] <- compute_total_YLL_new(i)
+ count <- count + 1
+ }
+ total
+}
+
+get_reduction_df = function(outcome){
+ total <- rep(NA, 255)
+
+ if (outcome == ""cases""){
+ total <- compile_total_cases(total)
+ baseline <- compute_total_cases_new(list_all$`0`)
+ } else if (outcome == ""deaths""){
+ total <- compile_total_deaths(total)
+ baseline <- compute_total_deaths_new(list_all$`0`)
+ } else if (outcome == ""YLL""){
+ total <- compile_total_YLL(total)
+ baseline <- compute_total_YLL_new(list_all$`0`)
+ }
+
+ num_strategies <- 5
+ vax_avail <- c(rep(seq(0, 50, by = 1), num_strategies))
+ num_per_list <- 51
+ strat <- c(rep(""all"", num_per_list), rep(""kids"", num_per_list),
+ rep(""adults"", num_per_list), rep(""elderly"", num_per_list),
+ rep(""twentyplus"", num_per_list))
+ variable <- c(rep(""constant"", num_per_list))
+
+
+ baseline <- c(rep(baseline, num_per_list))
+
+ reduction <- (1-(total/baseline))*100
+
+ df <- data.frame(vax_avail, strat, reduction, variable)
+}
+
+get_reduction_df_var = function(outcome){
+ total <- rep(NA, 510)
+
+ if (outcome == ""cases""){
+ total <- compile_total_cases_var(total)
+ baseline <- compute_total_cases_new(list_all$`0`)
+ baseline_var <- compute_total_cases_new(list_all_var$`0`)
+ } else if (outcome == ""deaths""){
+ total <- compile_total_deaths_var(total)
+ baseline <- compute_total_deaths_new(list_all$`0`)
+ baseline_var <- compute_total_deaths_new(list_all_var$`0`)
+ } else if (outcome == ""YLL""){
+ total <- compile_total_YLL_var(total)
+ baseline <- compute_total_YLL_new(list_all$`0`)
+ baseline_var <- compute_total_YLL_new(list_all_var$`0`)
+ }
+
+ num_strategies <- 5
+ vax_avail <- c(rep(seq(0, 50, by = 1), num_strategies*2))
+ num_per_list <- 51
+ strat <- c(rep(""all"", num_per_list*2),
+ rep(""kids"", num_per_list*2),
+ rep(""adults"", num_per_list*2),
+ rep(""elderly"", num_per_list*2),
+ rep(""twentyplus"", num_per_list*2))
+ temp <- c(rep(""constant"", num_per_list), rep(""var"", num_per_list))
+ variable <- c(rep(temp, num_strategies))
+
+ temp <- c(rep(baseline, num_per_list), rep(baseline_var, num_per_list))
+ baseline <- c(rep(temp, num_strategies))
+
+ reduction <- (1-(total/baseline))*100
+
+ df <- data.frame(vax_avail, strat, reduction, variable)
+}
+
+# Plot trajectories ----
+get_rowindex <- function(df, rName) {
+ which(match(colnames(df), rName) == 1)
+ }
+
+gather_compartments_overtime <- function(df, compartment, strat){
+ final_time <- as.numeric(dim(df)[1])-1
+
+ if (compartment == ""I""){
+ start <- ""I1""
+ end <- ""Ix9""
+ } else if (compartment == ""R""){
+ start <- ""R1""
+ end <- ""D9""
+ } else if (compartment == ""D""){
+ start <- ""D1""
+ end <- ""D9""
+ }
+
+ total_df <- data.frame(time = 0:final_time,
+ percent = ((apply(df[, get_rowindex(df, start):get_rowindex(df, end)],
+ 1, sum))/pop_total)*100,
+ strat = rep(strat, final_time + 1))
+}
+
+plot_strat_overtime = function(compartment, df_baseline, df_all, df_adults, df_kids, df_twentyplus,
+ df_elderly, this_time) {
+ df_baseline <- gather_compartments_overtime(df_baseline, compartment, ""no vax"")
+ df_all <- gather_compartments_overtime(df_all, compartment, ""all"")
+ df_adults <- gather_compartments_overtime(df_adults, compartment, ""adults"")
+ df_kids <- gather_compartments_overtime(df_kids, compartment, ""kids"")
+ df_twentyplus <- gather_compartments_overtime(df_twentyplus, compartment, ""twentyplus"")
+ df_elderly <- gather_compartments_overtime(df_elderly, compartment, ""elderly"")
+
+ df <- rbind(df_adults, df_all, df_elderly, df_kids, df_twentyplus)
+
+ p <- ggplot(df, aes(x = time, y = percent)) +
+ theme_minimal(base_size = 12) +
+ annotate(""rect"", xmin=0, xmax=this_time, ymin=0, ymax=Inf, alpha=0.5, fill= ""#dddddd"") +
+ geom_line(data = df_baseline, aes(x = time, y = percent), col = ""#4F4F4F"", size = 0.5,
+ linetype = ""dashed"") +
+ geom_line(aes(color = strat), size = 0.5) +
+ xlab(""Time (days)"") +
+ scale_x_continuous(expand = c(0,0), limit = c(0, 250)) +#, breaks = c(0, 100, 200, 300)) +
+ scale_color_brewer(palette = ""Dark2"", name = ""Allocation Strategy"",
+ labels = c(""Adults 20-49"", ""All Ages"", ""Adults 60+"",
+ ""Under 20"", ""Adults 20+"")) +
+ theme(legend.position = ""none"")
+
+ if (compartment == ""I"") {
+ ymax <- 4
+ p <- p + ylab(""\nInfected (%)"")
+ } else if (compartment == ""R"") {
+ ymax <- 60
+ p <- p + ylab(""Cumulative\nincidence (%)"")
+ } else if (compartment == ""D"") {
+ ymax <- 0.6
+ p <- p + ylab(""Cumulative\nmortality (%)"")
+ }
+
+ p <- p + scale_y_continuous(expand = c(0,0), limit = c(0, ymax)) +
+ coord_fixed(250*4/(5*ymax))
+}
+
+gather_compartments_byage <- function(df, compartment, age_group){
+ final_time <- as.numeric(dim(df)[1])-1
+
+ if (compartment == ""I""){
+ this_comp <- paste0(""I"", age_group)
+ this_comp_v <- paste0(""Iv"", age_group)
+ this_comp_x <- paste0(""Ix"", age_group)
+
+ total_df <- data.frame(time = 0:final_time,
+ percent = ((df[, get_rowindex(df, this_comp)] +
+ df[, get_rowindex(df, this_comp_v)] +
+ df[, get_rowindex(df, this_comp_x)])/pop_total)*100,
+ age_group = rep(age_group, final_time + 1))
+ } else if (compartment == ""R""){
+ this_comp <- paste0(""R"", age_group)
+ this_comp_v <- paste0(""Rv"", age_group)
+ this_comp_x <- paste0(""Rx"", age_group)
+ this_comp_d <- paste0(""D"", age_group)
+
+ total_df <- data.frame(time = 0:final_time,
+ percent = ((df[, get_rowindex(df, this_comp)] +
+ df[, get_rowindex(df, this_comp_v)] +
+ df[, get_rowindex(df, this_comp_x)] +
+ df[, get_rowindex(df, this_comp_d)])/pop_total)*100,
+ age_group = rep(age_group, final_time + 1))
+ } else if (compartment == ""D""){
+ this_comp <- paste0(""D"", age_group)
+
+ total_df <- data.frame(time = 0:final_time,
+ percent = ((df[, get_rowindex(df, this_comp)])/pop_total)*100,
+ age_group = rep(age_group, final_time + 1))
+ }
+}
+
+plot_allages_onestrategy = function(this_df, compartment, this_time, groups, this_col){
+ # INPUTS:
+ # df: simulation to plot
+ # compartment: character of compartment to plot i.e. ""I"", ""R""
+ #
+ # OUTPUT:
+ # p: ggplot object of plot
+
+ df_toplot <- data.frame()
+ for (i in 1:9) {
+ temp <- gather_compartments_byage(this_df, compartment, i)
+ df_toplot <- rbind(df_toplot, temp)
+ }
+
+ # Plot
+ p <- ggplot(df_toplot[!(df_toplot$age_group %in% groups),], aes(x = time, y = percent)) +
+ annotate(""rect"", xmin=0, xmax=this_time, ymin=0, ymax=Inf, alpha=0.5, fill=""#dddddd"") +
+ geom_line(aes(group = factor(age_group)), size = 0.4, col = ""#cccccc"") +
+ xlab(""Time (Days)"") +
+ scale_x_continuous(expand = c(0,0),limit = c(0, 250), breaks = c(0,100,200)) +
+ # col_grey +
+ theme(legend.position = ""none"")
+
+ p <- p + geom_line(data = df_toplot[df_toplot$age_group %in% groups,],
+ aes(x = time, y = percent, group = factor(age_group)),
+ color = this_col, size = 0.4)
+ if (compartment == ""I"") {
+ ymax <- 0.7
+ p <- p + ylab(""\nInfected (%)"") +
+ scale_y_continuous(expand = c(0,0), limit = c(0, ymax))#E, breaks = c(0, 1, 2))
+ } else if (compartment == ""R"") {
+ ymax <- 10
+ p <- p + ylab(""Cumulative\nincidence (%)"") +
+ scale_y_continuous(expand = c(0,0), limit = c(0, ymax), breaks = c(0, 5, 10, 15))
+ } else if (compartment == ""D"") {
+ ymax <- 0.3
+ p <- p + ylab(""Cumulative\nmortality (%)"") +
+ scale_y_continuous(expand = c(0,0), limit = c(0, ymax))#, breaks = c(0, 0.25, 0.5))
+ }
+ p <- p + coord_fixed(250*2/(3.5*ymax))
+}
+
+plot_supp_dynamics_panel = function(compartment){
+ t_one <- 11
+ infect_10 <- plot_strat_overtime(compartment, list_all[[1]], list_all[[t_one]], list_adults[[t_one]],
+ list_kids[[t_one]], list_twentyplus[[t_one]], list_elderly[[t_one]], 50) +
+ alllabels_theme +
+ theme(axis.title.x = element_blank()) +
+ theme(axis.title.y = element_blank()) +
+ ggtitle(""10% vaccine supply"") +
+ theme(plot.title = element_text(color = ""black""))
+
+ t_two <- 31
+ infect_30 <- plot_strat_overtime(compartment, list_all[[1]], list_all[[t_two]], list_adults[[t_two]],
+ list_kids[[t_two]], list_twentyplus[[t_two]], list_elderly[[t_two]], 150) +
+ onlyx_theme +
+ theme(axis.title.x = element_blank()) +
+ ggtitle(""30% vaccine supply"") +
+ theme(plot.title = element_text(color = ""black""))
+
+ ###
+ shading1 <- 50
+ shading2 <- 150
+ infect_none1 <- plot_allages_onestrategy(list_elderly[[1]], compartment, 0, NA, ""#cccccc"") +
+ onlyy_theme +
+ theme(axis.title.y = element_blank())
+ infect_adults1 <- plot_allages_onestrategy(list_adults[[t_one]], compartment, shading1, 3:5, col_youngadults) +
+ onlyy_theme +
+ theme(axis.title.y = element_blank())
+ infect_elderly1 <- plot_allages_onestrategy(list_elderly[[t_one]], compartment, shading1, 7:9, col_elderly) +
+ alllabels_theme +
+ theme(axis.title.x = element_blank()) +
+ theme(axis.title.y = element_blank())
+ infect_all1 <- plot_allages_onestrategy(list_all[[t_one]], compartment, shading1, 1:9, col_all) +
+ onlyx_theme +
+ theme(axis.title.x = element_blank())
+ infect_twentyplus1 <- plot_allages_onestrategy(list_twentyplus[[t_one]], compartment, shading1, 3:9, col_adults) +
+ nolabels_theme
+ infect_kids1 <- plot_allages_onestrategy(list_kids[[t_one]], compartment, shading1, 1:2, col_kids) +
+ nolabels_theme
+
+ infect_none2 <- plot_allages_onestrategy(list_elderly[[1]], compartment, 0, NA, ""#cccccc"") +
+ nolabels_theme
+ infect_adults2 <- plot_allages_onestrategy(list_adults[[t_two]], compartment, shading2, 3:5, col_youngadults) +
+ nolabels_theme
+ infect_elderly2 <- plot_allages_onestrategy(list_elderly[[t_two]], compartment, shading2, 7:9, col_elderly) +
+ alllabels_theme +
+ theme(axis.title.x = element_blank()) +
+ theme(axis.title.y = element_blank()) +
+ theme(axis.text.y = element_blank())
+ infect_all2 <- plot_allages_onestrategy(list_all[[t_two]], compartment, shading2, 1:9, col_all) +
+ onlyx_theme +
+ theme(axis.title.x = element_blank())
+ infect_twentyplus2 <- plot_allages_onestrategy(list_twentyplus[[t_two]], compartment, shading2, 3:9, col_adults) +
+ nolabels_theme
+ infect_kids2 <- plot_allages_onestrategy(list_kids[[t_two]], compartment, shading2, 1:2, col_kids) +
+ nolabels_theme
+
+ dynamics <- ggarrange(infect_none1, infect_kids1, infect_none2, infect_kids2,
+ infect_adults1, infect_twentyplus1, infect_adults2, infect_twentyplus2,
+ infect_elderly1, infect_all1, infect_elderly2, infect_all2,
+ nrow = 3)
+
+ panel_infect_strat <- ggarrange(infect_10, infect_30,
+ nrow = 1)
+ # export as 8x4.25
+ if (compartment == ""I"") {
+ grid.arrange(panel_infect_strat, dynamics,
+ heights = c(1, 1),
+ bottom = ""Time (days)"",
+ left = ""Infected (% of total population)"",
+ right = """")
+ } else if (compartment == ""R""){
+ grid.arrange(panel_infect_strat, dynamics,
+ heights = c(1, 1),
+ bottom = ""Time (days)"",
+ left = ""Cumulative incidence (% of total population)"",
+ right = """")
+ } else {
+ grid.arrange(panel_infect_strat, dynamics,
+ heights = c(1, 1),
+ bottom = ""Time (days)"",
+ left = ""Cumulative mortality since initial vaccine rollout\n(% of total population)"",
+ right = """")
+ }
+}
+
+# Bar plots ----
+plot_vaxdist_hist = function(){
+ p1 <- barplot_vax_strat(""kids"") +
+ theme(axis.title.y = element_blank())
+ p2 <- barplot_vax_strat(""adults"") +
+ theme(axis.title.y = element_blank())
+ p3 <- barplot_vax_strat(""20+"") +
+ theme(axis.title.y = element_blank())
+ p4 <- barplot_vax_strat(""elderly"") +
+ theme(axis.title.y = element_blank())
+ p5 <- barplot_vax_strat(""all"") +
+ theme(axis.title.y = element_blank())
+
+ panel <- ggarrange(p1, p2, p3, p4, p5,
+ nrow = 5,
+ left = textGrob(""Distribution of vaccines (%)"", rot = 90, hjust = 0.5),
+ bottom = textGrob(""Age (years)"", vjust = 0))
+}
+
+barplot_vax_strat = function(strategy){
+ if (strategy == ""no vax""){
+ vaccinated <- rep(0,num_groups) * 100
+ this_color <- ""#808080""
+ plot_title <- ""No Vaccines""
+ } else {
+ if (strategy == ""all""){
+ groups <- 1:9
+ this_color <- col_all
+ plot_title <- ""All ages""
+ }
+ else if (strategy == ""kids""){
+ groups <- 1:2
+ this_color <- col_kids
+ plot_title <- ""Under 20""
+ }
+ else if (strategy == ""adults""){
+ groups <- 3:5
+ this_color <- col_youngadults
+ plot_title <- ""Adults 20-49""
+ }
+ else if (strategy == ""elderly""){
+ groups <- 7:9
+ this_color <- col_elderly
+ plot_title <- ""Adults 60+""
+ }
+ else if (strategy == ""20+""){
+ groups <- 3:9
+ this_color <- col_adults
+ plot_title <- ""Adults 20+""
+ }
+ people_to_vax <- sum(N_i[groups])
+
+ vax_proportion <- rep(0, num_groups)
+ vax_proportion[groups] <- N_i[groups]/people_to_vax
+ }
+ #age_groups <- c(""0"",""10"", ""20"", ""30"", ""40"", ""50"", ""60"", ""70"", ""80"")
+ age_groups <- seq(0, 80, by = 10)
+ vax_proportion <- vax_proportion*100
+
+ df <- data.frame(age_groups, vax_proportion, age_demo)
+
+ # plot
+ p <- ggplot(df, aes(x=age_groups)) +
+ #geom_bar(aes(y=age_demo), fill=""white"",color=""black"", position=""stack"", stat=""identity"") +
+ geom_bar(aes(y=vax_proportion), position=""stack"", stat=""identity"", fill = this_color) +
+ xlab(""Age group"") +
+ ggtitle(plot_title) +
+ scale_y_continuous(expand = c(0,0), limit = c(0, 52), breaks = c(0, 25, 50), labels = c(0, """", 50))
+
+ if (strategy == ""all""){
+ p <- p + scale_x_continuous(limits = c(-5, NA), expand = c(0,0),
+ breaks=seq(-5, 75, by = 10),
+ labels=c(""0"", ""10"", ""20"", ""30"", ""40"", ""50"", ""60"", ""70"", ""80"")) +
+ theme(plot.title = element_text(color = this_color, size = 10),#, face = ""bold""),
+ axis.title.x = element_blank(),
+ axis.text.x = element_text(size = 9))
+ } else {
+ p <- p + theme(plot.title = element_text(color = this_color, size = 10),
+ axis.title.x =element_blank(),
+ axis.text.x = element_blank(),
+ axis.title.y = element_blank()) +
+ scale_x_continuous(limits = c(-5, NA), expand = c(0,0),
+ breaks=seq(-5, 75, by = 10),
+ labels=c("""", """", """", """", """", """", """", """", """"))
+ }
+ p <- p + theme(panel.grid.minor = element_blank(),
+ panel.grid.major = element_blank(),
+ axis.ticks = element_line(size = 0.35),
+ axis.line = element_line(size = 0.35),
+ plot.title = element_text(vjust = -1),
+ plot.margin = unit(c(-0.06,0,0,0), ""cm""))
+ return(p)
+}
+
+when_strat_switch = function(list_df, groups){
+ # find the vaccine supply where you switch from strategy to vaccinating everyone else
+ # input: list_df is a list with df for a strategy for vax supply 0-50
+ # groups: vector index of the strategy
+ # output: vax supply where switch occurs
+
+ other_groups <- 1:9
+ other_groups <- other_groups[!other_groups %in% groups]
+ x_switch <- -1
+
+ for (i in 1:length(list_df)){
+ temp <- list_df[[i]]
+ if (any(temp[dim(temp)[1], other_groups + 10] > 0)){
+ x_switch <- i-1
+ break
+ }
+ }
+ return(x_switch)
+}
+
+plot_over_vax_avail = function(outcome, var = FALSE){
+ library(ggplot2)
+ theme_set(theme_minimal(base_size = 12))
+
+ x_adults_switch <- when_strat_switch(list_adults, 3:5)
+ x_kids_switch <- when_strat_switch(list_kids, 1:2)
+ x_elderly_switch <- when_strat_switch(list_elderly, 7:9)
+ x_twentyplus_switch <- when_strat_switch(list_twentyplus, 3:9)
+
+ # get dataframe for specific outcome
+ if (var){
+ df <- get_reduction_df_var(outcome)
+
+ x_adults_switch_var <- when_strat_switch(list_adults_var, 3:5)
+ x_kids_switch_var <- when_strat_switch(list_kids_var, 1:2)
+ x_elderly_switch_var <- when_strat_switch(list_elderly_var, 7:9)
+ x_twentyplus_switch_var <- when_strat_switch(list_twentyplus_var, 3:9)
+ } else {df <- get_reduction_df(outcome)}
+
+ points_x <- c(x_kids_switch, x_elderly_switch)
+ points_y <- c(df[df$strat == ""kids"" & df$vax_avail == x_kids_switch & df$variable == ""constant"", ]$reduction,
+ df[df$strat == ""elderly"" & df$vax_avail == x_elderly_switch & df$variable == ""constant"", ]$reduction)
+
+ if (x_adults_switch > 0){
+ points_x <- c(points_x, x_adults_switch)
+ points_y <- c(points_y, df[df$strat == ""adults"" & df$vax_avail == x_adults_switch & df$variable == ""constant"", ]$reduction)
+ }
+ if (x_twentyplus_switch > 0){
+ points_x <- c(points_x, x_twentyplus_switch)
+ points_y <- c(points_y, df[df$strat == ""twentyplus"" & df$vax_avail == x_twentyplus_switch & df$variable == ""constant"", ]$reduction)
+ }
+
+ if (var){
+ points_x <- c(points_x, x_kids_switch_var, x_elderly_switch_var)
+ points_y <- c(points_y, df[df$strat == ""kids"" & df$vax_avail == x_kids_switch_var & df$variable == ""var"", ]$reduction,
+ df[df$strat == ""elderly"" & df$vax_avail == x_elderly_switch_var & df$variable == ""var"", ]$reduction)
+
+ if (x_adults_switch_var > 0){
+ points_x <- c(points_x, x_adults_switch_var)
+ points_y <- c(points_y, df[df$strat == ""adults"" & df$vax_avail == x_adults_switch_var & df$variable == ""var"", ]$reduction)
+ }
+ if (x_twentyplus_switch_var > 0){
+ points_x <- c(points_x, x_twentyplus_switch_var)
+ points_y <- c(points_y, df[df$strat == ""twentyplus"" & df$vax_avail == x_twentyplus_switch_var & df$variable == ""var"", ]$reduction)
+ }
+ }
+
+ df_var <- df[df$variable == ""var"", ]
+ df_const <- df[df$variable == ""constant"", ]
+
+ # plot
+ p <- ggplot() +
+ geom_line(aes(x = df_const$vax_avail, y = df_const$reduction, col = df_const$strat),
+ size = 0.5, alpha = 0.9) +
+ geom_line(aes(x = df_var$vax_avail, y = df_var$reduction, col = df_var$strat),
+ linetype = ""dashed"", size = 1, alpha = 0.9) +
+ xlab(""Total vaccine supply (% of population)"") +
+ scale_color_brewer(palette = ""Dark2"", name = ""Allocation Strategy"",
+ labels = c(""Adults 20-49"", ""All Ages"", ""Adults 60+"",
+ ""Under 20"", ""Adults 20+"")) +
+ scale_y_continuous(expand = c(0,0), limit = c(0, 75), breaks = c(0, 25, 50, 75)) +
+ scale_x_continuous(expand = c(0,0), limit = c(0, 50)) +#, breaks = c(0,25,50)) +
+ coord_fixed(50*4/(5*75)) +
+ theme(legend.position = ""none"") +
+ guides(colour = guide_legend(override.aes = list(size=3)))
+
+ p <- p + geom_point(aes(x = points_x, y = points_y), size = 1)
+
+ if (outcome == ""cases""){ p <- p + ylab(""Reduction in\ninfections (%)"")}
+ else if (outcome == ""deaths""){ p <- p + ylab(""Reduction in\ndeaths (%)"")}
+ else if (outcome == ""YLL""){ p <- p + ylab(""Reduction in\nYLL (%)"")}
+ return(p)
+}
+
+# Other plotting fn ----
+plot_age_dep_ve = function() {
+# age-dependent ve plot
+ groups <- c(0,10,20,30,40,50,60,70,80,90)
+ v_e_plot <- v_e_var
+ v_e_plot[10] <- v_e_var[9]
+
+ df <- data.frame(groups, v_e_plot)
+
+ age_dep_ve <- ggplot(df, aes(x = groups, y = v_e_plot*100)) +
+ geom_step(size = 0.8, linetype = ""dashed"") +
+ geom_hline(yintercept = 90, size = 0.5) +
+ geom_point(aes(x = 60, y = 90), size = 2) +
+ geom_point(aes(x = 89.5, y = 50), size = 2) +
+ ylab(""Efficacy (%)"") +
+ scale_y_continuous(expand = c(0,0), limit = c(0, 100), breaks = c(0, 25, 50, 75, 100)) +
+ scale_x_continuous(expand = c(0,0), limit = c(0, 90.2), breaks = c(0,20,40,60,80),
+ labels = c(0,20,40,60,80)) +
+ #theme(panel.grid.minor = element_blank()) +
+ xlab(""Age (years)"") +
+ theme(#axis.text = element_text(size = 9),
+ axis.title.y = element_text(vjust = 0.2)) +
+ coord_fixed(90*4/(5*100))
+}
+
+plot_heatmap = function(data, num, param){
+
+ p <- ggplot(data, aes(x = factor(Var2), y = factor(Var1), fill = factor(value)))+
+ geom_tile() +
+ scale_fill_manual(breaks = seq(0, 5, by = 1),
+ values = c(""#E6E6E6"", col_youngadults, col_adults, col_elderly, col_kids, col_all)) +
+ theme(legend.position = ""none"",
+ axis.title.x = element_blank(),
+ axis.text = element_text(size = 10)) +
+ scale_x_discrete(expand = c(0,0), breaks = seq(0, 50, by = 10)) +
+ coord_fixed(50*4/(6*num))
+
+ if (param == ""R0""){
+ p <- p + scale_y_discrete(expand = c(0,0), breaks = seq(1, num, by = 2),
+ labels = seq(1.1, 2.0, by = 0.1))
+ } else if (param == ""ve""){
+ p <- p + scale_y_discrete(expand = c(0,0), breaks = seq(1, num, by = 2),
+ labels = c("" 30"", "" 50"", "" 70"", "" 90""))
+ } else if (param == ""country"") {
+ p <- p + scale_y_discrete(expand = c(0,0), breaks = seq(1, num, by = 1),
+ labels = c(""BEL"", ""USA"", ""IND"", ""ESP"", ""ZWE"", ""BRA"", ""CHN"", ""ZAF"", ""POL""))
+ } else if (param == ""rollout"") {
+ p <- p + scale_y_discrete(expand = c(0,0), breaks = seq(1, num, by = 1),
+ labels = c(0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.75, 1))
+ } else if (param == ""ve_I""){
+ p <- p + scale_y_discrete(expand = c(0,0), breaks = seq(1, num, by = 2),
+ labels = c("" 0"","" 20"", "" 40"", "" 60"", "" 80"", ""100""))
+ }
+
+
+ for (i in seq(1.5, num + 0.5, by = 1)){
+ p <- p + geom_hline(yintercept = i, col = ""white"")
+ }
+
+ for (i in seq(10.5, 40.5, by = 10)){
+ p <- p + geom_vline(xintercept = i, col = ""white"")
+ }
+
+ p
+}
+
+plot_tipping_point_heatmap = function(list_tp){
+ baselines <- c(rep(baseline_vec[1], 50),
+ rep(baseline_vec[2], 50),
+ rep(baseline_vec[3], 50),
+ rep(baseline_vec[4], 50),
+ rep(baseline_vec[5], 50),
+ rep(baseline_vec[6], 50))
+ vaxsupply <- c(rep(1:50, 6))
+
+ strats <- c(list_tp[[1]][[2]],
+ list_tp[[2]][[2]],
+ list_tp[[3]][[2]],
+ list_tp[[4]][[2]],
+ list_tp[[5]][[2]],
+ list_tp[[6]][[2]])
+
+ df <- data.frame(vaxsupply, baselines, strats)
+
+ p <- ggplot(df, aes(x = vaxsupply, y = baselines*100, fill = factor(strats)))+
+ geom_tile() +
+ # scale_fill_manual(breaks = seq(1, 5, by = 1),
+ # values = c(col_youngadults, col_adults, col_elderly, col_kids, col_all)) +
+ colFill +
+ theme(legend.position = ""none"",
+ axis.title.x = element_blank(),
+ axis.text = element_text(size = 10)) +
+ #axis.title.y = element_blank()) +
+ scale_x_continuous(expand = c(0,0), breaks = seq(0, 50, by = 10)) +
+ scale_y_continuous(expand = c(0,0), breaks = seq(50, 100, by = 10))
+
+ p
+}
+
+plot_tipping_point_heatmap_2 = function(list_tp){
+ baselines <- c(rep(baseline_vec[1], 50),
+ rep(baseline_vec[2], 50),
+ rep(baseline_vec[3], 50),
+ rep(baseline_vec[4], 50),
+ rep(baseline_vec[5], 50),
+ rep(baseline_vec[6], 50))
+ vaxsupply <- c(rep(1:50, 6))
+
+ strats <- c(list_tp[[1]][[1]],
+ list_tp[[2]][[1]],
+ list_tp[[3]][[1]],
+ list_tp[[4]][[1]],
+ list_tp[[5]][[1]],
+ list_tp[[6]][[1]])
+
+ breakpoints <- c(-1, 0.000001, 25, 50, 99.999999, 101)
+ names <- c(""None"", ""0-25%"", ""25-50%"", ""50-100%"", ""NA"")
+
+ df <- data.frame(vaxsupply, baselines, strats)
+ df$cat <- cut(df$strats, breaks = breakpoints, labels = names)
+
+ p <- ggplot(df, aes(x = factor(vaxsupply), y = factor(baselines*100), fill = cat))+
+ geom_tile() +
+ colFill2 +
+ theme(axis.title.x = element_blank()) +
+ #axis.text = element_text(size = 10)) +
+ scale_x_discrete(expand = c(0,0), breaks = seq(0, 50, by = 10)) +
+ scale_y_discrete(expand = c(0,0), breaks = seq(50, 100, by = 10))+
+ coord_fixed(50*4/(6*6))
+
+ for (i in seq(1.5, 5 + 0.5, by = 1)){
+ p <- p + geom_hline(yintercept = i, col = ""white"")
+ }
+
+ for (i in seq(10.5, 40.5, by = 10)){
+ p <- p + geom_vline(xintercept = i, col = ""white"")
+ }
+
+ p
+}
+
+plot_opt_histogram = function(this_df, this_percent_vax){
+
+ dist_of_vax <- matrix(nrow = 21, ncol = 9)
+
+ for (i in 1:21){
+ tot <- sum(N_i*this_df[i, 2:10])
+ dist_of_vax[i,] <- as.numeric(((N_i*this_df[i, 2:10])/tot)*100)
+ }
+
+ dist_of_vax <- as.data.frame(dist_of_vax)
+ colnames(dist_of_vax) <- c(""0-9"", ""10-19"", ""20-29"", ""30-39"", ""40-49"", ""50-59"", ""60-69"", ""70-79"", ""80+"")
+
+ dist_of_vax$Percent_vax <- 0:20
+
+ new_df <- dist_of_vax %>%
+ select(Percent_vax, ""0-9"", ""10-19"", ""20-29"", ""30-39"", ""40-49"", ""50-59"", ""60-69"", ""70-79"", ""80+"") %>%
+ gather(key = ""age_group"", value = ""num"", -Percent_vax)
+
+ p <- ggplot(new_df[new_df$Percent_vax == this_percent_vax,], aes(y=num, x=age_group,
+ fill = as.factor(Percent_vax),
+ group = as.factor(Percent_vax))) +
+ geom_bar(position = ""stack"", stat = ""identity"", fill = ""#E6AB02"") +
+ #xlab(""Age group"") +
+ #ylab(""Age distribution of vaccines (%)"")+
+ theme_classic(base_size = 12) +
+ scale_x_discrete(breaks=c(""0-9"", ""10-19"", ""20-29"", ""30-39"", ""40-49"", ""50-59"", ""60-69"", ""70-79"", ""80+""),
+ labels=c("""", """", """", """", """", """", """", """", """")) +
+ scale_y_continuous(expand = c(0,0), limits = c(0, 75), breaks = c(0,25,50,75)) +
+ theme(legend.position = ""none"",
+ axis.text.x = element_text(angle = 45, hjust = 1),
+ axis.title.x =element_blank(),
+ axis.title.y =element_blank())
+
+ return(p)
+}
+
+get_tp_values = function(list_tp){
+ vals <- matrix(NA, nrow = 6, ncol = 50)
+ vals[1,] <- list_tp[[1]][[1]]
+ vals[2,] <- list_tp[[2]][[1]]
+ vals[3,] <- list_tp[[3]][[1]]
+ vals[4,] <- list_tp[[4]][[1]]
+ vals[5,] <- list_tp[[5]][[1]]
+ vals[6,] <- list_tp[[6]][[1]]
+ vals
+}
+
+get_legend = function(myggplot){
+ tmp <- ggplot_gtable(ggplot_build(myggplot))
+ leg <- which(sapply(tmp$grobs, function(x) x$name) == ""guide-box"")
+ legend <- tmp$grobs[[leg]]
+ return(legend)
+}
+","R"
+"Vaccine","kbubar/vaccine_prioritization","setup.R",".R","3452","89","# _____________________________________________________________________
+# IMPORT ----
+# _____________________________________________________________________
+library(tidyverse)
+library(deSolve) # ode solver
+library(gridExtra)
+library(RColorBrewer)
+library(wesanderson)
+library(egg)
+library(foreach)
+library(doParallel)
+library(ggplot2)
+library(gplots)
+
+# ColorBrewer Dark2
+col_youngadults = ""#1B9E77"" # teal green (20-49)
+col_all = ""#D95F02"" # orange
+col_elderly = ""#7570B3"" # purple (60+)
+col_kids = ""#E7298A"" # magenta
+col_adults = ""#66A61E"" # light green (20+ strategy)
+
+tippingpointPal2 <- c(""#E6E6E6"", ""#FFEDA0"", ""#FEB24C"", ""#F03B20"", ""#000000"")
+names(tippingpointPal2) <- c(""None"", ""0-25%"", ""25-50%"",
+ ""50-100%"", ""NA"")
+
+colFill2 <- scale_fill_manual(name = ""Tipping point"", values = tippingpointPal2)
+
+nolabels_theme <- theme(axis.title.x =element_blank(),
+ axis.text.x = element_blank(),
+ axis.title.y = element_blank(),
+ axis.text.y = element_blank(),
+ plot.title = element_text(size = 12, face = ""plain""),
+ legend.position = ""none"")
+onlyx_theme <- theme(axis.title.y = element_blank(),
+ axis.text.y = element_blank(),
+ plot.title = element_text(size = 12, face = ""plain""),
+ legend.position = ""none"")
+onlyy_theme <- theme(axis.title.x = element_blank(),
+ axis.text.x = element_blank(),
+ plot.title = element_text(size = 12, face = ""plain""),
+ legend.position = ""none"")
+alllabels_theme <- theme(plot.title = element_text(size = 12, face = ""plain""),
+ legend.position = ""none"")
+
+theme_set(theme_minimal(base_size = 12))
+
+####
+d_E <- 1/3 # incubation period (E -> I), ref: Davies
+d_I <- 1/5 # recovery period (I -> R), ref: Davies
+
+C <- readRDS(paste0(""C_"", country, ""_bytens_overall.RData""))
+
+age_demo <- readRDS(paste0(""age_demographics_"", country,"".RData""))
+pop_total <- age_demo[10]
+age_demo <- age_demo[1:9]
+
+N_i <- pop_total*age_demo
+num_groups <- length(age_demo) # num age groups
+
+#IFR <- c(9.530595e-04, 3.196070e-03, 1.071797e-02, 3.594256e-02, 1.205328e-01,
+# 4.042049e-01, 1.355495e+00, 4.545632e+00, 1.524371e+01) # old IFR, Ref: Levin2020
+IFR <- c(9.807703e-04, 3.277686e-03, 1.095386e-02, 3.660727e-02, 1.223397e-01, 4.088531e-01, 1.366367e+00,
+ 4.566330e+00, 1.526045e+01) # updated IFR, Ref: Levin2020
+IFR <- IFR/100 # as decimal
+YLL_vec <- readRDS(paste0(""yll_vec_"", country, "".RData""))
+
+u_var <- c(0.4, 0.38, 0.79, 0.86, 0.8, 0.82, 0.88, 0.74, 0.74) # Ref: Davies2020
+
+this_v_e <- get_v_e(p = 0.9, y0 = 0.9, hinge_age = 50)
+v_e_var <- get_v_e(p = 0.5, y0 = 0.9, hinge_age = 50)
+v_e_type <- ""aorn""
+
+sero_none <- rep(0, 9) # no prior immunity
+sero_CT <- c(0.039, 0.0382, 0.031, 0.031, 0.031, 0.037, 0.032, 0.027, 0.027)
+sero_NY <- c(0.32, 0.3129, 0.249, 0.249, 0.264, 0.279, 0.2575, 0.2215, 0.207) # ref: NYC
+
+this_sp <- 0.99
+this_se <- 0.70
+
+num_perday <- 0.002
+list_all <- list_kids <- list_adults <- list_elderly <- list_twentyplus <- vector(mode = ""list"")
+
+scale_115 <- scale_u_for_R0(u_var, C, 1.15)
+scale_15 <- scale_u_for_R0(u_var, C, 1.5)
+scale_26 <- scale_u_for_R0(u_var, C, 2.6)
+
+C_115 <- C/scale_115
+C_15 <- C/scale_15
+C_26 <- C/scale_26","R"
+"Vaccine","kbubar/vaccine_prioritization","run_heatmap_R0.R",".R","6150","172","# _____________________________________________________________________
+# FUNCTIONS ----
+# _____________________________________________________________________
+source(""run_sim.R"")
+source(""helper_functions.R"")
+country <- ""USA""
+source(""setup.R"")
+# _____________________________________________________________________
+# RUN SIM ----
+# Create heatmap for a range of R0 values
+# _____________________________________________________________________
+# scale C to get range of R0
+scale_C <- c()
+R0 <- seq(1.1, 2, by = 0.05)
+count <- 1
+for (i in R0){
+ scale_C[count] <- scale_u_for_R0(u_var, C, i)
+ count <- count + 1
+}
+
+ptm <- proc.time()
+
+# clusters
+cores=detectCores()
+cl <- makeCluster(cores[1]-1) # to not overload your computer
+registerDoParallel(cl)
+
+HM <- foreach(this_scale_C = scale_C) %dopar% {
+ C <- readRDS(paste0(""C_"", country, ""_bytens_overall.RData""))
+ this_C <- C/this_scale_C
+
+ # * Continuous rollout
+ list_all <- list_kids <- list_adults <- list_elderly <- list_twentyplus <- vector(mode = ""list"")
+
+ num_perday <- 0.001
+ for (i in seq(0, 50, by = 1)){
+ j <- i/100
+ list_all[[paste0(i)]] <- run_sim(this_C, j, ""all"", num_perday, v_e_type, this_v_e)
+ list_kids[[paste0(i)]] <- run_sim(this_C, j, ""kids"", num_perday, v_e_type, this_v_e)
+ list_adults[[paste0(i)]] <- run_sim(this_C, j, ""adults"", num_perday, v_e_type, this_v_e)
+ list_elderly[[paste0(i)]] <- run_sim(this_C, j, ""elderly"", num_perday, v_e_type, this_v_e)
+ list_twentyplus[[paste0(i)]] <- run_sim(this_C, j, ""twentyplus"", num_perday, v_e_type, this_v_e)
+ }
+ x_vec <- seq(1, 50, by = 1)
+ I1 <- sapply(x_vec, FUN = function(x) get_best_strat_cases_new(x, list_all,list_kids,list_adults,
+ list_elderly,list_twentyplus))
+ M1 <- sapply(x_vec, FUN = function(x) get_best_strat_deaths_new(x, list_all,list_kids,list_adults,
+ list_elderly,list_twentyplus))
+ Y1 <- sapply(x_vec, FUN = function(x) get_best_strat_yll_new(x, list_all,list_kids,list_adults,
+ list_elderly,list_twentyplus))
+
+ # * Anticipatory rollout
+ num_perday <- 1
+ list_all <- list_kids <- list_adults <- list_elderly <- list_twentyplus <- vector(mode = ""list"")
+ for (i in seq(0, 50, by = 1)){
+ j <- i/100
+ list_all[[paste0(i)]] <- run_sim(this_C, j, ""all"", num_perday, v_e_type, this_v_e)
+ list_kids[[paste0(i)]] <- run_sim(this_C, j, ""kids"", num_perday, v_e_type, this_v_e)
+ list_adults[[paste0(i)]] <- run_sim(this_C, j, ""adults"", num_perday, v_e_type, this_v_e)
+ list_elderly[[paste0(i)]] <- run_sim(this_C, j, ""elderly"", num_perday, v_e_type, this_v_e)
+ list_twentyplus[[paste0(i)]] <- run_sim(this_C, j, ""twentyplus"", num_perday, v_e_type, this_v_e)
+ }
+
+ x_vec <- seq(1, 50, by = 1)
+ I2 <- sapply(x_vec, FUN = function(x) get_best_strat_cases_new(x, list_all,list_kids,list_adults,
+ list_elderly,list_twentyplus))
+ M2 <- sapply(x_vec, FUN = function(x) get_best_strat_deaths_new(x, list_all,list_kids,list_adults,
+ list_elderly,list_twentyplus))
+ Y2 <- sapply(x_vec, FUN = function(x) get_best_strat_yll_new(x, list_all,list_kids,list_adults,
+ list_elderly,list_twentyplus))
+ list(s1 = cbind(I1, M1, Y1),
+ s2 = cbind(I2, M2, Y2))
+}
+stopCluster(cl)
+
+saveRDS(HM, ""HM_USA_R0_001.RData"")
+proc.time() - ptm
+
+# _____________________________________________________________________
+# PLOT ----
+# _____________________________________________________________________
+param <- ""R0""
+
+num <- length(HM)
+
+S1_I <- S2_I <- S1_M <- S2_M <- S1_Y <- S2_Y <- matrix(NA, num, 50)
+
+# Get data in right form for plotting
+for (i in 1:num){
+ S1_I[i ,] <- HM[[i]]$s1[, 1]
+ S2_I[i ,] <- HM[[i]]$s2[, 1]
+
+ S1_M[i ,] <- HM[[i]]$s1[, 2]
+ S2_M[i ,] <- HM[[i]]$s2[, 2]
+
+ S1_Y[i ,] <- HM[[i]]$s1[, 3]
+ S2_Y[i ,] <- HM[[i]]$s2[, 3]
+}
+
+S1_Inew <- S2_Inew <- S1_Mnew <- S2_Mnew <- S1_Ynew <- S2_Ynew <- matrix(NA, num, 50)
+
+#adults = 1 (20-49)
+#twentyplus = 2
+#elderly = 3
+#kids = 4
+#all = 5
+### I Matrices
+for(i in 1){
+ S1_Inew[S1_I == ""adults""] = 1
+ S1_Inew[S1_I == ""twentyplus""] = 2
+ S1_Inew[S1_I == ""elderly""] = 3
+ S1_Inew[S1_I == ""kids""] = 4
+ S1_Inew[S1_I == ""all""] = 5
+
+ S2_Inew[S2_I == ""adults""] = 1
+ S2_Inew[S2_I == ""twentyplus""] = 2
+ S2_Inew[S2_I == ""elderly""] = 3
+ S2_Inew[S2_I == ""kids""] = 4
+ S2_Inew[S2_I == ""all""] = 5
+
+ ### M matrices
+ S1_Mnew[S1_M == ""adults""] = 1
+ S1_Mnew[S1_M == ""twentyplus""] = 2
+ S1_Mnew[S1_M == ""elderly""] = 3
+ S1_Mnew[S1_M == ""kids""] = 4
+ S1_Mnew[S1_M == ""all""] = 5
+
+ S2_Mnew[S2_M == ""adults""] = 1
+ S2_Mnew[S2_M == ""twentyplus""] = 2
+ S2_Mnew[S2_M == ""elderly""] = 3
+ S2_Mnew[S2_M == ""kids""] = 4
+ S2_Mnew[S2_M == ""all""] = 5
+
+ ### Y matrices
+ S1_Ynew[S1_Y == ""adults""] = 1
+ S1_Ynew[S1_Y == ""twentyplus""] = 2
+ S1_Ynew[S1_Y == ""elderly""] = 3
+ S1_Ynew[S1_Y == ""kids""] = 4
+ S1_Ynew[S1_Y == ""all""] = 5
+
+ S2_Ynew[S2_Y == ""adults""] = 1
+ S2_Ynew[S2_Y == ""twentyplus""] = 2
+ S2_Ynew[S2_Y == ""elderly""] = 3
+ S2_Ynew[S2_Y == ""kids""] = 4
+ S2_Ynew[S2_Y == ""all""] = 5
+}
+
+## R0
+pS1_I <- plot_heatmap(reshape2::melt(S1_Inew), num, param) +
+ ylab(""Cumulative\nincidence\n\n"") +
+ onlyy_theme +
+ ggtitle(""Continuous Rollout"")
+pS2_I <- plot_heatmap(reshape2::melt(S2_Inew), num, param) +
+ nolabels_theme +
+ ggtitle(""Anticipatory Rollout"")
+
+pS1_M <- plot_heatmap(reshape2::melt(S1_Mnew), num, param)+
+ ylab(""Mortality\n\nR0"") +
+ onlyy_theme
+pS2_M <- plot_heatmap(reshape2::melt(S2_Mnew), num, param) +
+ nolabels_theme
+
+pS1_Y <- plot_heatmap(reshape2::melt(S1_Ynew), num, param) +
+ ylab(""Years of\nlife lost\n\n"")
+pS2_Y <- plot_heatmap(reshape2::melt(S2_Ynew), num, param) +
+ onlyx_theme
+
+# export as 8.5"" x 6""
+g <- ggarrange(pS1_I, pS2_I, pS1_M, pS2_M, pS1_Y, pS2_Y,
+ bottom = textGrob(""Total vaccine supply (% of population)"", gp = gpar(fontsize = 12)),
+ padding = unit(0.5, ""line""))
+","R"
+"Vaccine","kbubar/vaccine_prioritization","get_sero_from_sim.R",".R","1601","52","# Get synthetic seroprevalence from simulation
+# for Supp Fig 12: US, R0 = 2.6, seroprevalence = 40%
+
+setwd(""~/Vaccine Strategy/Vaccine_Allocation_Project"")
+
+source(""run_sim.R"")
+source(""helper_functions.R"")
+
+country <- ""USA""
+C <- readRDS(paste0(""C_"", country, ""_bytens_overall.RData""))
+age_demo <- readRDS(paste0(""age_demographics_"", country,"".RData""))
+pop_total <- age_demo[10]
+age_demo <- age_demo[1:9]
+N_i <- pop_total*age_demo
+
+IFR <- c(9.530595e-04, 3.196070e-03, 1.071797e-02, 3.594256e-02, 1.205328e-01,
+ 4.042049e-01, 1.355495e+00, 4.545632e+00, 1.524371e+01) # Ref: Levin
+IFR <- IFR/100 # as decimal
+
+age_demo <- readRDS(paste0(""age_demographics_"", country,"".RData""))
+
+pop_total <- age_demo[10]
+age_demo <- age_demo[1:9]
+
+N_i <- pop_total*age_demo
+num_groups <- length(age_demo) # num age groups
+
+u_var <- c(0.4, 0.38, 0.79, 0.86, 0.8, 0.82, 0.88, 0.74, 0.74)
+scale_15 <- scale_u_for_R0(u_var, C, 1.5)
+v_e_constant <- get_v_e(p = 1, y0 = 1, hinge_age = 50)
+sero_none <- rep(0, 9)
+
+# _____________________________________________________________________
+# RUN SIM ----
+# _____________________________________________________________________
+this_C <- C/scale_15
+dat <- run_sim(this_C, percent_vax = 0, strategy = ""all"", num_perday = 0.01, v_e_type = ""aorn"")
+
+R <- dat[,83:91] + dat[,101:109]
+R_tot <- rowSums(R)
+
+R_tot <- floor((R_tot/pop_total)*100)
+
+# store indices for R ~40%
+val <- max(R_tot[R_tot <= 40])
+index <- match(c(val), R_tot)
+
+synthetic_sero <- dat[index, -1]
+row.names(synthetic_sero) <- 1
+
+saveRDS(synthetic_sero, ""synthetic_sero_US.RData"")
+","R"
+"Vaccine","kbubar/vaccine_prioritization","get_IFR.R",".R","378","16","# Get IFR
+
+# meta-regression from Levin et al.
+# https://www.medrxiv.org/content/10.1101/2020.07.23.20160895v4.full.pdf+html
+
+age = seq(0,89,1)
+# IFR_byones = exp(-7.56 + 0.121*age) # old version
+IFR_byones = 10^(-3.27+0.0524*age) # updated version
+IFR_bytens = rep(0, 9)
+
+count = 1
+for (i in 1:9){
+ IFR_bytens[i] = sum(IFR_byones[count:(count+9)])/10
+ count = count + 10
+}
+","R"
+"Vaccine","kbubar/vaccine_prioritization","get_contact_matrix_2020.R",".R","5718","189","# Combine 5 year age-group contact matrix from Prem for 10 years age-groups
+library(grid)
+library(readxl)
+library(""xlsx"")
+library(""tidyverse"")
+library(""RColorBrewer"")
+library(gplots)
+
+setwd(""~/Vaccine Strategy/Vaccine_Allocation_Project"")
+age_df <- read_excel(""WPP2019_POP_F07_1_POPULATION_BY_AGE_BOTH_SEXES.xlsx"", ""ESTIMATES"")
+
+
+# setwd to file with Prem contact matrices
+setwd(""~/Vaccine Strategy/Vaccine_Allocation_Project/Prem_contact_matrices"")
+
+# FUNCTION ----
+# convert C to 10 year age-groups
+convert_bins_5to10 <- function(C_byfives) {
+ l <- dim(C_byfives)[1]
+ C_bytens <- matrix(nrow = l/2, ncol = l/2)
+
+ col_count <- 1
+ for (col in seq(1,l,by = 2)){
+ row_count <- 1
+ for (row in seq(1,l, by = 2)){
+ C_bytens[row_count, col_count] <- C_byfives[row, col] +
+ C_byfives[row, col + 1] +
+ C_byfives[row + 1, col] +
+ C_byfives[row + 1, col + 1]
+ row_count <- row_count + 1
+ }
+ col_count <- col_count + 1
+ }
+
+ colnames(C_bytens) <- c(""0-9"", ""10-19"", ""20-29"", ""30-39"", ""40-49"", ""50-59"", ""60-69"", ""70-79"")
+ rownames(C_bytens) <- colnames(C_bytens)
+
+ C_bytens
+}
+
+convert_bins_5to10_updated <- function(C_byfives) {
+ l <- dim(C_byfives)[1]
+ C_bytens <- matrix(nrow = l/2, ncol = l/2)
+
+ col_count <- 1
+ for (col in seq(1,l,by = 2)){
+ row_count <- 1
+ for (row in seq(1,l, by = 2)){
+ p1 <- pop_demo[row]
+ p2 <- pop_demo[row + 1]
+ C_bytens[row_count, col_count] <- ((C_byfives[row, col] + C_byfives[row, col + 1])*p1 +
+ (C_byfives[row + 1, col] + C_byfives[row + 1, col + 1])*p2)/(p1+p2)
+ row_count <- row_count + 1
+ }
+ col_count <- col_count + 1
+ }
+
+ colnames(C_bytens) <- c(""0-9"", ""10-19"", ""20-29"", ""30-39"", ""40-49"", ""50-59"", ""60-69"", ""70-79"")
+ rownames(C_bytens) <- colnames(C_bytens)
+
+ C_bytens
+}
+
+add_80bin <- function(C_bytens){
+ # INPUT:
+ # C_bytens: Contact matrix with 10 year age bins, with last bin = 70-79
+ #
+ # OUTPUT:
+ # C_new: Same C matrix with new row & col for 80+ age bin
+
+ bin_70 <- dim(C_bytens)[1]
+ bin_80 <- bin_70 + 1
+ bin_60 <- bin_70 - 1
+
+ C_new <- matrix(nrow = bin_80, ncol = bin_80)
+ colnames(C_new) <- c(""0-9"", ""10-19"", ""20-29"", ""30-39"", ""40-49"", ""50-59"", ""60-69"", ""70-79"", ""80+"")
+ rownames(C_new) <- colnames(C_new)
+
+ # fill rows for 80+ with same data from 70-79
+ C_new[1:bin_70, 1:bin_70] <- C_bytens
+ C_new[2:bin_80, bin_80] <- C_bytens[1:bin_70, bin_70]
+ C_new[bin_80, 2:bin_80] <- C_bytens[bin_70, 1:bin_70]
+
+ C_new[1, bin_80] <- C_new[1, bin_70]
+ C_new[bin_80, 1] <- C_new[bin_70, 1]
+
+ # Assumption: 80+ have similar (but less) contact rates as 70-79, with increased contact with 70-80+ (long term living facilities)
+ # Implementation: Decrease contact for bins 0 - 69 for 80+ by 10% then split these contacts between 70-79 & 80+
+
+ # col 80+
+ to_decrease <- 0.1 * C_new[1:bin_60, bin_80]
+ C_new[1:bin_60, bin_80] <- 0.9 * C_new[1:bin_60, bin_80]
+
+ to_increase <- sum(to_decrease)/2
+ C_new[bin_70, bin_80] <- C_new[bin_70, bin_80] + to_increase
+ C_new[bin_80, bin_80] <- C_new[bin_80, bin_80] + to_increase
+
+ # row 80+
+ to_decrease <- 0.1 * C_new[bin_80, 1:bin_60]
+ C_new[bin_80, 1:bin_60] <- 0.9 * C_new[bin_80, 1:bin_60]
+
+ to_increase <- sum(to_decrease)/2
+ C_new[bin_80, bin_70] <- C_new[bin_80, bin_70] + to_increase
+ C_new[bin_80, bin_80] <- C_new[bin_80, bin_80] + to_increase
+
+ C_new
+}
+
+# MAKE CONTACT MATRICES ----
+# Read in contact matrix
+# Convert 5 years age bins to 10 year age bins
+# Add 80+ age bin
+
+# country codes:
+# Belgium: BEL
+# United States: USA
+# India: IND
+# Spain: ESP
+# Zimbabwe: ZWE
+# Brazil: BRA
+# China: CHN
+# South Africa: ZAF
+# Poland: POL
+
+countrycode <- ""USA""
+country <- ""United States""
+
+
+# age demographics by 5 year age bin (to weight C when aggregating)
+popdata <- age_df[age_df$Country == country,]
+popdata <- popdata[popdata$Year == 2020,]
+pop_demo <- rep(0, 18)
+col_num <- 9 # col of 0-4 y.o.
+
+for (i in 0:16){
+ pop_demo[i+1] <- as.numeric(popdata[col_num+i])
+}
+
+pop_demo[18] <- sum(as.numeric(popdata[(col_num+i+1):29]))
+total_pop <- sum(pop_demo)
+pop_demo <- pop_demo/total_pop
+
+setting <- ""overall"" #overall, rural or urban
+#* C for all locations (home, work, school & other) ----
+df <- read.csv(""synthetic_contacts_2020.csv"")
+
+df <- df[df$iso3c == countrycode,]
+df <- df[df$location_contact == ""all"",]
+df <- df[df$setting == setting,]
+
+nage <- 16 # number of age groups
+
+# construct C(i,j) = of age j people that an age i person contacts each day
+C <- matrix(ncol = nage, nrow = nage)
+
+contactor <- c(""0 to 4"" , ""5 to 9"", ""10 to 14"" ,""15 to 19"", ""20 to 24"", ""25 to 29"",
+ ""30 to 34"", ""35 to 39"", ""40 to 44"", ""45 to 49"", ""50 to 54"", ""55 to 59"",
+ ""60 to 64"", ""65 to 69"", ""70 to 74"", ""75+"")
+contactee <- contactor
+
+rownames(C) <- as.list(contactor)
+colnames(C) <- as.list(contactee)
+
+for (i in contactor){
+ for (j in contactee){
+ temp <- df[df$age_contactor == i & df$age_cotactee == j,]
+ C[i,j] = temp$mean_number_of_contacts
+ }
+}
+
+heatmap(C, NA, NA, scale = ""column"", xlab = ""Age of Individual"", ylab = ""Age of Contact"",
+ cexRow = 1.5, cexCol = 1.5,
+ labRow = c(NA, 10, NA, 20, NA, 30, NA, 40, NA, 50, NA, 60, NA, 70, NA, 80),
+ labCol = c(NA, 10, NA, 20, NA, 30, NA, 40, NA, 50, NA, 60, NA, 70, NA, 80))
+
+
+C_bytens <- convert_bins_5to10_updated(C)
+heatmap(C_bytens, NA, NA, scale = ""column"", xlab = ""Age of Individual"", ylab = ""Age of Contact"")
+
+C_bytens <- add_80bin(C_bytens)
+heatmap(C_bytens, NA, NA, scale = ""none"",
+ #xlab = ""Age of Individual"",
+ #ylab = ""Age of Contact"",
+ cexRow = 1.5,
+ cexCol = 1.5)
+
+saveRDS(C_bytens, paste0(""C_"", countrycode,""_bytens_"", setting, "".RData""))
+
+","R"
+"Vaccine","kbubar/vaccine_prioritization","main_heatmap.R",".R","13227","335","
+# _____________________________________________________________________
+# FUNCTIONS ----
+# _____________________________________________________________________
+source(""run_sim.R"")
+source(""helper_functions.R"")
+country <- ""USA""
+source(""setup.R"")
+# _____________________________________________________________________
+# SET UP ----
+# _____________________________________________________________________
+# country codes:
+countries <- c(""BEL"", ""USA"", ""IND"", ""ESP"", ""ZWE"", ""BRA"", ""CHN"", ""ZAF"", ""POL"")
+country <- ""USA""
+
+v_e <- list(rep(0.3, 9),
+ rep(0.4, 9),
+ rep(0.5, 9),
+ rep(0.6, 9),
+ rep(0.7, 9),
+ rep(0.8, 9),
+ rep(0.9, 9),
+ rep(1.0, 9))
+
+this_v_e <- rep(0.9, 9)
+v_e_type <- ""aorn""
+
+C <- readRDS(paste0(""C_"", country, ""_bytens_overall.RData""))
+
+scale_C <- seq(0.5, 1, by = 0.5/13)
+
+sero_none <- rep(0, 9) # no prior immunity
+
+# scale the susceptibility for each country to get R0 = 1.15, 1.5 and 2.6
+countries_scale_u_115 <- c()
+countries_scale_u_15 <- c()
+countries_scale_u_26 <- c()
+count <- 1
+for (i in countries){
+ this_C <- readRDS(paste0(""C_"", i, ""_bytens_overall.RData""))
+ countries_scale_u_115[count] <- scale_u_for_R0(u_var, this_C, 1.15)
+ countries_scale_u_15[count] <- scale_u_for_R0(u_var, this_C, 1.5)
+ countries_scale_u_26[count] <- scale_u_for_R0(u_var, this_C, 2.6)
+ count <- count + 1
+}
+
+# _____________________________________________________________________
+# RUN SIMS ----
+# Uncomment the corresponding parameter you want to make a heatmap for
+# _____________________________________________________________________
+ptm <- proc.time()
+
+# clusters
+cores=detectCores()
+cl <- makeCluster(cores[1]-1) # to not overload your computer
+registerDoParallel(cl)
+
+#HM <- foreach(this_scale_C = scale_C) %dopar% {
+#HM <- foreach(this_v_e = v_e) %dopar% {
+#HM <- foreach(num_perday = rollouts) %dopar% {
+HM <- foreach(country = countries) %dopar% {
+
+ C <- readRDS(paste0(""C_"", country, ""_bytens_overall.RData""))
+
+ index <- match(country, countries)
+ C_115 <- C/countries_scale_u_115[index]
+ C_15 <- C/countries_scale_u_15[index]
+ C_26 <- C/countries_scale_u_26[index]
+
+ age_demo <- readRDS(paste0(""age_demographics_"", country,"".RData""))
+ pop_total <- age_demo[10]
+ age_demo <- age_demo[1:9]
+ N_i <- pop_total*age_demo
+ num_groups <- length(age_demo) # num age groups
+
+ # * SCENARIO 4: R0 - 2.6, anticipatory rollout
+ list_all <- list_kids <- list_adults <- list_elderly <- list_twentyplus <- vector(mode = ""list"")
+ num_perday <- 1
+ for (i in seq(0, 50, by = 1)){
+ j <- i/100
+ list_all[[paste0(i)]] <- run_sim(C_26, j, ""all"", num_perday, v_e_type, this_v_e)
+ list_kids[[paste0(i)]] <- run_sim(C_26, j, ""kids"", num_perday, v_e_type, this_v_e)
+ list_adults[[paste0(i)]] <- run_sim(C_26, j, ""adults"", num_perday, v_e_type, this_v_e)
+ list_elderly[[paste0(i)]] <- run_sim(C_26, j, ""elderly"", num_perday, v_e_type, this_v_e)
+ list_twentyplus[[paste0(i)]] <- run_sim(C_26, j, ""twentyplus"", num_perday, v_e_type, this_v_e)
+ }
+ x_vec <- seq(1, 50, by = 1)
+ I4 <- sapply(x_vec, FUN = function(x) get_best_strat_cases_new(x,
+ list_all,list_kids,list_adults,
+ list_elderly,list_twentyplus))
+ M4 <- sapply(x_vec, FUN = function(x) get_best_strat_deaths_new(x,
+ list_all,list_kids,list_adults,
+ list_elderly,list_twentyplus))
+ Y4 <- sapply(x_vec, FUN = function(x) get_best_strat_yll_new(x,
+ list_all,list_kids,list_adults,
+ list_elderly,list_twentyplus))
+
+ # * SCENARIO 3: R0 = 1.5, anticipatory rollout
+ list_all <- list_kids <- list_adults <- list_elderly <- list_twentyplus <- vector(mode = ""list"")
+ num_perday <- 1
+ for (i in seq(0, 50, by = 1)){
+ j <- i/100
+ list_all[[paste0(i)]] <- run_sim(C_15, j, ""all"", num_perday, v_e_type, this_v_e)
+ list_kids[[paste0(i)]] <- run_sim(C_15, j, ""kids"", num_perday, v_e_type, this_v_e)
+ list_adults[[paste0(i)]] <- run_sim(C_15, j, ""adults"", num_perday, v_e_type, this_v_e)
+ list_elderly[[paste0(i)]] <- run_sim(C_15, j, ""elderly"", num_perday, v_e_type, this_v_e)
+ list_twentyplus[[paste0(i)]] <- run_sim(C_15, j, ""twentyplus"", num_perday, v_e_type, this_v_e)
+ }
+
+ x_vec <- seq(1, 50, by = 1)
+ I3 <- sapply(x_vec, FUN = function(x) get_best_strat_cases_new(x,
+ list_all,list_kids,list_adults,
+ list_elderly,list_twentyplus))
+ M3 <- sapply(x_vec, FUN = function(x) get_best_strat_deaths_new(x,
+ list_all,list_kids,list_adults,
+ list_elderly,list_twentyplus))
+ Y3 <- sapply(x_vec, FUN = function(x) get_best_strat_yll_new(x,
+ list_all,list_kids,list_adults,
+ list_elderly,list_twentyplus))
+ # * SCENARIO 2: R0 = 1.5, continuous rollout
+ list_all <- list_kids <- list_adults <- list_elderly <- list_twentyplus <- vector(mode = ""list"")
+ num_perday <- 0.002
+ for (i in seq(0, 50, by = 1)){
+ j <- i/100
+ list_all[[paste0(i)]] <- run_sim(C_15, j, ""all"", num_perday, v_e_type, this_v_e)
+ list_kids[[paste0(i)]] <- run_sim(C_15, j, ""kids"", num_perday, v_e_type, this_v_e)
+ list_adults[[paste0(i)]] <- run_sim(C_15, j, ""adults"", num_perday, v_e_type, this_v_e)
+ list_elderly[[paste0(i)]] <- run_sim(C_15, j, ""elderly"", num_perday, v_e_type, this_v_e)
+ list_twentyplus[[paste0(i)]] <- run_sim(C_15, j, ""twentyplus"", num_perday, v_e_type, this_v_e)
+ }
+ I2 <- sapply(x_vec, FUN = function(x) get_best_strat_cases_new(x,
+ list_all,list_kids,list_adults,
+ list_elderly,list_twentyplus))
+ M2 <- sapply(x_vec, FUN = function(x) get_best_strat_deaths_new(x,
+ list_all,list_kids,list_adults,
+ list_elderly,list_twentyplus))
+ Y2 <- sapply(x_vec, FUN = function(x) get_best_strat_yll_new(x,
+ list_all,list_kids,list_adults,
+ list_elderly,list_twentyplus))
+
+ # * SCENARIO 1: R0 = 1.15, continuous rollout
+ num_perday <- 0.001
+ list_all <- list_kids <- list_adults <- list_elderly <- list_twentyplus <- vector(mode = ""list"")
+ for (i in seq(0, 50, by = 1)){
+ j <- i/100
+ list_all[[paste0(i)]] <- run_sim(C_115, j, ""all"", num_perday, v_e_type, this_v_e)
+ list_kids[[paste0(i)]] <- run_sim(C_115, j, ""kids"", num_perday, v_e_type, this_v_e)
+ list_adults[[paste0(i)]] <- run_sim(C_115, j, ""adults"", num_perday, v_e_type, this_v_e)
+ list_elderly[[paste0(i)]] <- run_sim(C_115, j, ""elderly"", num_perday, v_e_type, this_v_e)
+ list_twentyplus[[paste0(i)]] <- run_sim(C_115, j, ""twentyplus"", num_perday, v_e_type, this_v_e)
+ }
+ x_vec <- seq(1, 50, by = 1)
+ I1 <- sapply(x_vec, FUN = function(x) get_best_strat_cases_new(x,
+ list_all,list_kids,list_adults,
+ list_elderly,list_twentyplus))
+ M1 <- sapply(x_vec, FUN = function(x) get_best_strat_deaths_new(x,
+ list_all,list_kids,list_adults,
+ list_elderly,list_twentyplus))
+ Y1 <- sapply(x_vec, FUN = function(x) get_best_strat_yll_new(x,
+ list_all,list_kids,list_adults,
+ list_elderly,list_twentyplus))
+ list(s1 = cbind(I1, M1, Y1),
+ s2 = cbind(I2, M2, Y2),
+ s3 = cbind(I3, M3, Y3),
+ s4 = cbind(I4, M4, Y4))
+}
+stopCluster(cl)
+
+saveRDS(HM, ""HM_countries_001.RData"")
+proc.time() - ptm
+
+# _____________________________________________________________________
+# PLOT ----
+# Read in file, reformat the data so it's easier to plot, plot, some may need extra tweaking to get correct
+# axes labels
+# _____________________________________________________________________
+# Read in file of interest
+# HM <- readRDS(""HM_USA_leaky.RData"")
+ HM <- readRDS(""HM_USA_leaky_u_constant.RData"")
+# HM <- readRDS(""HM_USA_aorn.RData"")
+# HM <- readRDS(""HM_USA_aorn_u_constant.RData"")
+param <- ""ve""
+
+HM <- readRDS(""HM_countries_001.RData"")
+param <- ""country""
+
+num <- length(HM)
+
+S1_I <- S2_I <- S3_I <- S4_I <- S1_M <- S2_M <- S3_M <- S4_M <- S1_Y <- S2_Y <- S3_Y <- S4_Y <- matrix(NA, num, 50)
+
+for (i in 1:num){
+ S1_I[i ,] <- HM[[i]]$s1[, 1]
+ S2_I[i ,] <- HM[[i]]$s2[, 1]
+ S3_I[i ,] <- HM[[i]]$s3[, 1]
+ S4_I[i ,] <- HM[[i]]$s4[, 1]
+
+ S1_M[i ,] <- HM[[i]]$s1[, 2]
+ S2_M[i ,] <- HM[[i]]$s2[, 2]
+ S3_M[i ,] <- HM[[i]]$s3[, 2]
+ S4_M[i ,] <- HM[[i]]$s4[, 2]
+
+ S1_Y[i ,] <- HM[[i]]$s1[, 3]
+ S2_Y[i ,] <- HM[[i]]$s2[, 3]
+ S3_Y[i ,] <- HM[[i]]$s3[, 3]
+ S4_Y[i ,] <- HM[[i]]$s4[, 3]
+}
+
+S1_Inew <- S2_Inew <- S3_Inew <- S4_Inew <- S1_Mnew <- S2_Mnew <- S3_Mnew <- S4_Mnew <- S1_Ynew <- S2_Ynew <- S3_Ynew <- S4_Ynew <- matrix(NA, num, 50)
+
+#adults = 1 (20-49)
+#twentyplus = 2
+#elderly = 3
+#kids = 4
+#all = 5
+### I Matrices
+for(i in 1){
+S1_Inew[S1_I == ""adults""] = 1
+S1_Inew[S1_I == ""twentyplus""] = 2
+S1_Inew[S1_I == ""elderly""] = 3
+S1_Inew[S1_I == ""kids""] = 4
+S1_Inew[S1_I == ""all""] = 5
+
+S2_Inew[S2_I == ""adults""] = 1
+S2_Inew[S2_I == ""twentyplus""] = 2
+S2_Inew[S2_I == ""elderly""] = 3
+S2_Inew[S2_I == ""kids""] = 4
+S2_Inew[S2_I == ""all""] = 5
+
+S3_Inew[S3_I == ""adults""] = 1
+S3_Inew[S3_I == ""twentyplus""] = 2
+S3_Inew[S3_I == ""elderly""] = 3
+S3_Inew[S3_I == ""kids""] = 4
+S3_Inew[S3_I == ""all""] = 5
+
+S4_Inew[S4_I == ""adults""] = 1
+S4_Inew[S4_I == ""twentyplus""] = 2
+S4_Inew[S4_I == ""elderly""] = 3
+S4_Inew[S4_I == ""kids""] = 4
+S4_Inew[S4_I == ""all""] = 5
+
+### M matrices
+S1_Mnew[S1_M == ""adults""] = 1
+S1_Mnew[S1_M == ""twentyplus""] = 2
+S1_Mnew[S1_M == ""elderly""] = 3
+S1_Mnew[S1_M == ""kids""] = 4
+S1_Mnew[S1_M == ""all""] = 5
+
+S2_Mnew[S2_M == ""adults""] = 1
+S2_Mnew[S2_M == ""twentyplus""] = 2
+S2_Mnew[S2_M == ""elderly""] = 3
+S2_Mnew[S2_M == ""kids""] = 4
+S2_Mnew[S2_M == ""all""] = 5
+
+S3_Mnew[S3_M == ""adults""] = 1
+S3_Mnew[S3_M == ""twentyplus""] = 2
+S3_Mnew[S3_M == ""elderly""] = 3
+S3_Mnew[S3_M == ""kids""] = 4
+S3_Mnew[S3_M == ""all""] = 5
+
+S4_Mnew[S4_M == ""adults""] = 1
+S4_Mnew[S4_M == ""twentyplus""] = 2
+S4_Mnew[S4_M == ""elderly""] = 3
+S4_Mnew[S4_M == ""kids""] = 4
+S4_Mnew[S4_M == ""all""] = 5
+
+### Y matrices
+S1_Ynew[S1_Y == ""adults""] = 1
+S1_Ynew[S1_Y == ""twentyplus""] = 2
+S1_Ynew[S1_Y == ""elderly""] = 3
+S1_Ynew[S1_Y == ""kids""] = 4
+S1_Ynew[S1_Y == ""all""] = 5
+
+S2_Ynew[S2_Y == ""adults""] = 1
+S2_Ynew[S2_Y == ""twentyplus""] = 2
+S2_Ynew[S2_Y == ""elderly""] = 3
+S2_Ynew[S2_Y == ""kids""] = 4
+S2_Ynew[S2_Y == ""all""] = 5
+
+S3_Ynew[S3_Y == ""adults""] = 1
+S3_Ynew[S3_Y == ""twentyplus""] = 2
+S3_Ynew[S3_Y == ""elderly""] = 3
+S3_Ynew[S3_Y == ""kids""] = 4
+S3_Ynew[S3_Y == ""all""] = 5
+
+S4_Ynew[S4_Y == ""adults""] = 1
+S4_Ynew[S4_Y == ""twentyplus""] = 2
+S4_Ynew[S4_Y == ""elderly""] = 3
+S4_Ynew[S4_Y == ""kids""] = 4
+S4_Ynew[S4_Y == ""all""] = 5
+}
+
+pS1_I <- plot_heatmap(reshape2::melt(S1_Inew), num, param) +
+ ylab(""Cumulative\nincidence\n\n"") +
+ onlyy_theme +
+ ggtitle(""Scenario 1"")
+pS2_I <- plot_heatmap(reshape2::melt(S2_Inew), num, param) +
+ nolabels_theme +
+ ggtitle(""Scenario 2"")
+pS3_I <- plot_heatmap(reshape2::melt(S3_Inew), num, param) +
+ nolabels_theme +
+ ggtitle(""Scenario 3"")
+pS4_I <- plot_heatmap(reshape2::melt(S4_Inew), num, param) +
+ nolabels_theme +
+ ggtitle(""Scenario 4"")
+
+
+pS1_M <- plot_heatmap(reshape2::melt(S1_Mnew), num, param)+
+ ylab(""Mortality\n\nVaccine efficacy (%)"") + # UPDATE AXIS LABEL TO MATCH PARAM
+ onlyy_theme
+pS2_M <- plot_heatmap(reshape2::melt(S2_Mnew), num, param) +
+ nolabels_theme
+pS3_M <- plot_heatmap(reshape2::melt(S3_Mnew), num, param) +
+ nolabels_theme
+pS4_M <- plot_heatmap(reshape2::melt(S4_Mnew), num, param) +
+ nolabels_theme
+
+pS1_Y <- plot_heatmap(reshape2::melt(S1_Ynew), num, param) +
+ ylab(""Years of\nlife lost\n\n"")
+pS2_Y <- plot_heatmap(reshape2::melt(S2_Ynew), num, param) +
+ onlyx_theme
+pS3_Y <- plot_heatmap(reshape2::melt(S3_Ynew), num, param) +
+ onlyx_theme
+pS4_Y <- plot_heatmap(reshape2::melt(S4_Ynew), num, param) +
+ onlyx_theme
+
+# Vaccine Efficacy heatmap, export as 10"" x 6""
+g <- ggarrange(pS1_I, pS2_I, pS3_I, pS4_I,
+ pS1_M, pS2_M, pS3_M, pS4_M,
+ pS1_Y, pS2_Y, pS3_Y, pS4_Y,
+ nrow = 3,
+ bottom = textGrob(""Total vaccine supply (% of population)"", gp = gpar(fontsize = 12),
+ hjust = 0.3),
+ padding = unit(0.5, ""line""))
+
+","R"
+"Vaccine","apalladi/covid_vaccine_model","discesa_ondate.ipynb",".ipynb","228166","447","{
+ ""cells"": [
+ {
+ ""cell_type"": ""code"",
+ ""execution_count"": 13,
+ ""metadata"": {},
+ ""outputs"": [],
+ ""source"": [
+ ""import matplotlib.pyplot as plt \n"",
+ ""import pandas as pd\n"",
+ ""import numpy as np""
+ ]
+ },
+ {
+ ""cell_type"": ""markdown"",
+ ""metadata"": {},
+ ""source"": [
+ ""# Load epidemic italian data""
+ ]
+ },
+ {
+ ""cell_type"": ""code"",
+ ""execution_count"": 14,
+ ""metadata"": {},
+ ""outputs"": [],
+ ""source"": [
+ ""from src.load_data import get_epidemic_data""
+ ]
+ },
+ {
+ ""cell_type"": ""code"",
+ ""execution_count"": 15,
+ ""metadata"": {},
+ ""outputs"": [
+ {
+ ""data"": {
+ ""text/html"": [
+ ""\n"",
+ ""\n"",
+ ""
\n"",
+ "" \n"",
+ "" \n"",
+ "" | \n"",
+ "" Total cases | \n"",
+ "" Active infected | \n"",
+ "" Total deaths | \n"",
+ "" Total recovered | \n"",
+ "" Daily cases (avg 7 days) | \n"",
+ "" Daily deaths (avg 7 days) | \n"",
+ ""
\n"",
+ "" \n"",
+ "" \n"",
+ "" \n"",
+ "" | 2020-01-22 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2020-01-23 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2020-01-24 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2020-01-25 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2020-01-26 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ ""
\n"",
+ "" \n"",
+ ""
\n"",
+ ""
""
+ ]
+ },
+ ""metadata"": {
+ ""needs_background"": ""light""
+ },
+ ""output_type"": ""display_data""
+ }
+ ],
+ ""source"": [
+ ""df_epidemic['Active infected'].plot(marker='.')\n"",
+ ""plt.title('Infetti attivi')\n"",
+ ""plt.legend(['Dati'])\n"",
+ ""plt.show()""
+ ]
+ },
+ {
+ ""cell_type"": ""code"",
+ ""execution_count"": 19,
+ ""metadata"": {},
+ ""outputs"": [
+ {
+ ""data"": {
+ ""image/png"": 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\n"",
+ ""text/plain"": [
+ """"
+ ]
+ },
+ ""metadata"": {
+ ""needs_background"": ""light""
+ },
+ ""output_type"": ""display_data""
+ }
+ ],
+ ""source"": [
+ ""infetti_attivi = np.array(df_epidemic['Active infected'])\n"",
+ ""\n"",
+ ""plt.plot(infetti_attivi[88:180]/108000,label='1° ondata')\n"",
+ ""plt.plot(infetti_attivi[311:400]/790000,label='2° ondata')\n"",
+ ""plt.plot(infetti_attivi[430:]/570000,label='3° ondata')\n"",
+ ""plt.legend()\n"",
+ ""plt.grid()\n"",
+ ""plt.xlabel('Giorni dal picco')\n"",
+ ""plt.ylabel('Numeri normalizzati al picco relativo')\n"",
+ ""plt.title('Infetti attivi')\n"",
+ ""plt.show()""
+ ]
+ },
+ {
+ ""cell_type"": ""markdown"",
+ ""metadata"": {},
+ ""source"": [
+ ""### modelli epidemiologici""
+ ]
+ },
+ {
+ ""cell_type"": ""code"",
+ ""execution_count"": 20,
+ ""metadata"": {},
+ ""outputs"": [
+ {
+ ""name"": ""stdout"",
+ ""output_type"": ""stream"",
+ ""text"": [
+ ""60000000\n"",
+ ""Optimization terminated successfully.\n"",
+ "" Current function value: 1.285082\n"",
+ "" Iterations: 52\n"",
+ "" Function evaluations: 98\n"",
+ ""Error on susceptible 0.0 %\n"",
+ ""Error on infected 3.3 %\n"",
+ ""Error on removed 0.5 %\n"",
+ ""The average error of the model is 1.3 %\n"",
+ ""Parametri ottimizzati: beta 0.011 gamma 0.043\n""
+ ]
+ },
+ {
+ ""data"": {
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\n"",
+ ""text/plain"": [
+ """"
+ ]
+ },
+ ""metadata"": {
+ ""needs_background"": ""light""
+ },
+ ""output_type"": ""display_data""
+ },
+ {
+ ""name"": ""stdout"",
+ ""output_type"": ""stream"",
+ ""text"": [
+ ""60000000\n"",
+ ""Optimization terminated successfully.\n"",
+ "" Current function value: 1.548060\n"",
+ "" Iterations: 46\n"",
+ "" Function evaluations: 88\n"",
+ ""Error on susceptible 0.0 %\n"",
+ ""Error on infected 3.5 %\n"",
+ ""Error on removed 1.1 %\n"",
+ ""The average error of the model is 1.5 %\n"",
+ ""Parametri ottimizzati: beta 0.026 gamma 0.034\n""
+ ]
+ },
+ {
+ ""data"": {
+ ""image/png"": 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\n"",
+ ""text/plain"": [
+ """"
+ ]
+ },
+ ""metadata"": {
+ ""needs_background"": ""light""
+ },
+ ""output_type"": ""display_data""
+ },
+ {
+ ""name"": ""stdout"",
+ ""output_type"": ""stream"",
+ ""text"": [
+ ""60000000\n"",
+ ""Optimization terminated successfully.\n"",
+ "" Current function value: 0.125379\n"",
+ "" Iterations: 55\n"",
+ "" Function evaluations: 102\n"",
+ ""Error on susceptible 0.0 %\n"",
+ ""Error on infected 0.3 %\n"",
+ ""Error on removed 0.1 %\n"",
+ ""The average error of the model is 0.1 %\n"",
+ ""Parametri ottimizzati: beta 0.031 gamma 0.039\n""
+ ]
+ },
+ {
+ ""data"": {
+ ""image/png"": 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\n"",
+ ""text/plain"": [
+ """"
+ ]
+ },
+ ""metadata"": {
+ ""needs_background"": ""light""
+ },
+ ""output_type"": ""display_data""
+ }
+ ],
+ ""source"": [
+ ""model_beta_costante(104,170,'results/discesa_ondata1.png')\n"",
+ ""#model_beta_costante(312,339,'results/discesa_ondata2a.png')\n"",
+ ""#model_beta_costante(354,385,'results/discesa_ondata2b.png')\n"",
+ ""model_beta_costante(312,385,'results/discesa_ondata2.png')\n"",
+ ""model_beta_costante(440,len(df_epidemic),'results/discesa_ondata3.png')""
+ ]
+ },
+ {
+ ""cell_type"": ""code"",
+ ""execution_count"": null,
+ ""metadata"": {},
+ ""outputs"": [],
+ ""source"": []
+ },
+ {
+ ""cell_type"": ""code"",
+ ""execution_count"": null,
+ ""metadata"": {},
+ ""outputs"": [],
+ ""source"": []
+ },
+ {
+ ""cell_type"": ""code"",
+ ""execution_count"": null,
+ ""metadata"": {},
+ ""outputs"": [],
+ ""source"": []
+ }
+ ],
+ ""metadata"": {
+ ""kernelspec"": {
+ ""display_name"": ""Python 3"",
+ ""language"": ""python"",
+ ""name"": ""python3""
+ },
+ ""language_info"": {
+ ""codemirror_mode"": {
+ ""name"": ""ipython"",
+ ""version"": 3
+ },
+ ""file_extension"": "".py"",
+ ""mimetype"": ""text/x-python"",
+ ""name"": ""python"",
+ ""nbconvert_exporter"": ""python"",
+ ""pygments_lexer"": ""ipython3"",
+ ""version"": ""3.6.6""
+ }
+ },
+ ""nbformat"": 4,
+ ""nbformat_minor"": 4
+}
+","Unknown"
+"Vaccine","apalladi/covid_vaccine_model","italy_3rdwave_vaccine.ipynb",".ipynb","362500","944","{
+ ""cells"": [
+ {
+ ""cell_type"": ""code"",
+ ""execution_count"": 23,
+ ""metadata"": {},
+ ""outputs"": [],
+ ""source"": [
+ ""import matplotlib.pyplot as plt \n"",
+ ""import pandas as pd\n"",
+ ""import numpy as np""
+ ]
+ },
+ {
+ ""cell_type"": ""markdown"",
+ ""metadata"": {},
+ ""source"": [
+ ""# Load data""
+ ]
+ },
+ {
+ ""cell_type"": ""code"",
+ ""execution_count"": 24,
+ ""metadata"": {},
+ ""outputs"": [],
+ ""source"": [
+ ""from src.load_data import get_epidemic_data, get_vaccine_data""
+ ]
+ },
+ {
+ ""cell_type"": ""markdown"",
+ ""metadata"": {},
+ ""source"": [
+ ""###Â Epidemic data""
+ ]
+ },
+ {
+ ""cell_type"": ""code"",
+ ""execution_count"": 25,
+ ""metadata"": {},
+ ""outputs"": [
+ {
+ ""data"": {
+ ""text/html"": [
+ ""\n"",
+ ""\n"",
+ ""
\n"",
+ "" \n"",
+ "" \n"",
+ "" | \n"",
+ "" Total cases | \n"",
+ "" Active infected | \n"",
+ "" Total deaths | \n"",
+ "" Total recovered | \n"",
+ "" Daily cases (avg 7 days) | \n"",
+ "" Daily deaths (avg 7 days) | \n"",
+ ""
\n"",
+ "" \n"",
+ "" \n"",
+ "" \n"",
+ "" | 2020-01-22 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2020-01-23 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2020-01-24 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2020-01-25 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2020-01-26 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ ""
\n"",
+ "" \n"",
+ ""
\n"",
+ ""
\n"",
+ ""\n"",
+ ""
\n"",
+ "" \n"",
+ "" \n"",
+ "" | \n"",
+ "" % vaccinated with 1 dose | \n"",
+ "" % vaccinated with 2 doses | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | date | \n"",
+ "" | \n"",
+ "" | \n"",
+ ""
\n"",
+ "" \n"",
+ "" \n"",
+ "" \n"",
+ "" | 2020-12-29 | \n"",
+ "" 0.02 | \n"",
+ "" 0.0 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2020-12-30 | \n"",
+ "" 0.02 | \n"",
+ "" 0.0 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2020-12-31 | \n"",
+ "" 0.07 | \n"",
+ "" 0.0 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2021-01-01 | \n"",
+ "" 0.08 | \n"",
+ "" 0.0 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2021-01-02 | \n"",
+ "" 0.15 | \n"",
+ "" 0.0 | \n"",
+ ""
\n"",
+ "" \n"",
+ ""
\n"",
+ ""
\n"",
+ ""\n"",
+ ""
\n"",
+ "" \n"",
+ "" \n"",
+ "" | \n"",
+ "" % vaccinated with 1 dose | \n"",
+ "" % vaccinated with 2 doses | \n"",
+ "" Total cases | \n"",
+ "" Active infected | \n"",
+ "" Total deaths | \n"",
+ "" Total recovered | \n"",
+ "" Daily cases (avg 7 days) | \n"",
+ "" Daily deaths (avg 7 days) | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | date | \n"",
+ "" | \n"",
+ "" | \n"",
+ "" | \n"",
+ "" | \n"",
+ "" | \n"",
+ "" | \n"",
+ "" | \n"",
+ "" | \n"",
+ ""
\n"",
+ "" \n"",
+ "" \n"",
+ "" \n"",
+ "" | 2021-02-24 | \n"",
+ "" 4.19 | \n"",
+ "" 2.26 | \n"",
+ "" 2848564.0 | \n"",
+ "" 389433.0 | \n"",
+ "" 96666.0 | \n"",
+ "" 2362465.0 | \n"",
+ "" 13843.9 | \n"",
+ "" 303.7 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2021-02-25 | \n"",
+ "" 4.39 | \n"",
+ "" 2.28 | \n"",
+ "" 2868435.0 | \n"",
+ "" 396143.0 | \n"",
+ "" 96974.0 | \n"",
+ "" 2375318.0 | \n"",
+ "" 14717.6 | \n"",
+ "" 298.1 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2021-02-26 | \n"",
+ "" 4.59 | \n"",
+ "" 2.31 | \n"",
+ "" 2888923.0 | \n"",
+ "" 404664.0 | \n"",
+ "" 97227.0 | \n"",
+ "" 2387032.0 | \n"",
+ "" 15434.4 | \n"",
+ "" 284.6 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2021-02-27 | \n"",
+ "" 4.78 | \n"",
+ "" 2.32 | \n"",
+ "" 2907825.0 | \n"",
+ "" 411966.0 | \n"",
+ "" 97507.0 | \n"",
+ "" 2398352.0 | \n"",
+ "" 16004.1 | \n"",
+ "" 288.7 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2021-02-28 | \n"",
+ "" 4.89 | \n"",
+ "" 2.34 | \n"",
+ "" 2925265.0 | \n"",
+ "" 422367.0 | \n"",
+ "" 97699.0 | \n"",
+ "" 2405199.0 | \n"",
+ "" 16574.1 | \n"",
+ "" 283.0 | \n"",
+ ""
\n"",
+ "" \n"",
+ ""
\n"",
+ ""
""
+ ]
+ },
+ ""metadata"": {
+ ""needs_background"": ""light""
+ },
+ ""output_type"": ""display_data""
+ }
+ ],
+ ""source"": [
+ ""# check del modello, con curve di suscettibili, infetti e rimossi\n"",
+ ""\n"",
+ ""plt.figure(figsize=(10,8))\n"",
+ ""plt.subplot(2,2,1)\n"",
+ ""plt.plot(inhabitants-ydata_cases,label='Data')\n"",
+ ""plt.plot(model_check[1],\n"",
+ "" linestyle='--',label='Model, $\\\\beta$='+str(beta)+', $\\gamma$='+str(gamma)+', $\\\\tau$='+str(tau))\n"",
+ ""plt.xlim(0,tmax)\n"",
+ ""plt.ylim(45*10**6,60*10**6)\n"",
+ ""plt.title('Susceptible')\n"",
+ ""plt.legend()\n"",
+ ""plt.subplot(2,2,2)\n"",
+ ""plt.plot(ydata_inf,label='Data')\n"",
+ ""plt.plot(model_check[2],\n"",
+ "" linestyle='--',label='Model, $\\\\beta$='+str(beta)+', $\\gamma$='+str(gamma)+', $\\\\tau$='+str(tau))\n"",
+ ""plt.xlim(0,tmax)\n"",
+ ""plt.ylim(3.5*10**5,6.5*10**5)\n"",
+ ""plt.title('Infected')\n"",
+ ""plt.legend()\n"",
+ ""plt.subplot(2,2,3)\n"",
+ ""plt.plot(ydata_rec,label='Data')\n"",
+ ""plt.plot(model_check[3],\n"",
+ "" linestyle='--',label='Model, $\\\\beta$='+str(beta)+', $\\gamma$='+str(gamma)+', $\\\\tau$='+str(tau))\n"",
+ ""plt.xlim(0,tmax)\n"",
+ ""plt.title('Recovered + Deaths')\n"",
+ ""plt.ylim(2*10**6,3.5*10**6)\n"",
+ ""plt.legend()\n"",
+ ""plt.subplot(2,2,4)\n"",
+ ""plt.plot(ydata_vacc,label='Data')\n"",
+ ""plt.plot(model_check[4],\n"",
+ "" linestyle='--',label='Model, $\\\\beta$='+str(beta)+', $\\gamma$='+str(gamma)+', $\\\\tau$='+str(tau))\n"",
+ ""plt.xlim(0,tmax)\n"",
+ ""plt.title('Vaccinated')\n"",
+ ""plt.ylim(0,10**7)\n"",
+ ""plt.legend()\n"",
+ ""plt.tight_layout()\n"",
+ ""plt.savefig('results/check_model_italy3rd.png',dpi=300)\n"",
+ ""plt.show()""
+ ]
+ },
+ {
+ ""cell_type"": ""markdown"",
+ ""metadata"": {},
+ ""source"": [
+ ""### previsioni del modello epidemiologico""
+ ]
+ },
+ {
+ ""cell_type"": ""code"",
+ ""execution_count"": 39,
+ ""metadata"": {},
+ ""outputs"": [],
+ ""source"": [
+ ""# model that accounts for lockdown (tau) and vaccination (vacc_speed)\n"",
+ ""\n"",
+ ""pred_lockdown_vaccine=SIR2(inhabitants,minpar[0],minpar[1],minpar[2],\n"",
+ "" vacc_eff = vacc_eff,\n"",
+ "" vacc_speed = vacc_speed,\n"",
+ "" t0=0,\n"",
+ "" I0=ydata_inf[0],\n"",
+ "" R0=ydata_rec[0],\n"",
+ "" V0=ydata_vacc[0]) \n"",
+ ""\n"",
+ ""pred_lockdown_vaccine_best=SIR2(inhabitants,minpar[0],minpar[1],minpar[2],\n"",
+ "" vacc_eff = vacc_eff,\n"",
+ "" vacc_speed = vacc_speed,\n"",
+ "" t0=0,\n"",
+ "" I0=ydata_inf[0],\n"",
+ "" R0=ydata_rec[0],\n"",
+ "" V0=ydata_vacc[0],\n"",
+ "" vacc_custom = [len(ydata_cases),0.25]) \n"",
+ ""\n"",
+ ""pred_lockdown_vaccine_zero=SIR2(inhabitants,minpar[0],minpar[1],minpar[2],\n"",
+ "" vacc_eff = 0,\n"",
+ "" vacc_speed = vacc_speed,\n"",
+ "" t0=0,\n"",
+ "" I0=ydata_inf[0],\n"",
+ "" R0=ydata_rec[0],\n"",
+ "" V0=ydata_vacc[0],\n"",
+ "" vacc_custom = [len(ydata_cases),0]) ""
+ ]
+ },
+ {
+ ""cell_type"": ""markdown"",
+ ""metadata"": {},
+ ""source"": [
+ ""# Risultati""
+ ]
+ },
+ {
+ ""cell_type"": ""code"",
+ ""execution_count"": 40,
+ ""metadata"": {},
+ ""outputs"": [],
+ ""source"": [
+ ""# vaccinati con 1 dose e con 2 dosi\n"",
+ ""vaccinated_1dose = np.array(df_merged['% vaccinated with 1 dose'])\n"",
+ ""vaccinated_2dose = np.array(df_merged['% vaccinated with 2 doses'])\n"",
+ ""\n"",
+ ""# incidenza settimanale ogni 100.000 abitanti\n"",
+ ""weekly_incidence = 7/(inhabitants/10**5)*np.array(df_merged['Daily cases (avg 7 days)'])\n"",
+ ""\n"",
+ ""# decessi giornalieri per milione di abitanti\n"",
+ ""daily_deaths = 1/(inhabitants/10**6)*np.array(df_merged['Daily deaths (avg 7 days)'])\n"",
+ ""\n"",
+ ""# dati sugli infetti attivi\n"",
+ ""ydata_inf_complete = np.array(df_merged['Active infected'])\n"",
+ ""\n"",
+ ""xticks_21 = ['24 Feb','10 Mar','24 Mar','7 Apr','21 Apr','5 Mag','19 Mag',\n"",
+ "" '2 Giu','16 Giu','30 Giu','14 Lug','28 Lug','11 Ago','25 Ago']""
+ ]
+ },
+ {
+ ""cell_type"": ""markdown"",
+ ""metadata"": {},
+ ""source"": [
+ ""### grafico in stile Israele""
+ ]
+ },
+ {
+ ""cell_type"": ""code"",
+ ""execution_count"": 42,
+ ""metadata"": {},
+ ""outputs"": [
+ {
+ ""data"": {
+ ""image/png"": 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\n"",
+ ""text/plain"": [
+ """"
+ ]
+ },
+ ""metadata"": {
+ ""needs_background"": ""light""
+ },
+ ""output_type"": ""display_data""
+ }
+ ],
+ ""source"": [
+ ""xgrid = np.arange(0,len(pred_lockdown_vaccine[2]))\n"",
+ ""\n"",
+ ""plt.figure(figsize=(11,9))\n"",
+ ""plt.subplot(2,2,1)\n"",
+ ""plt.plot(ydata_inf_complete,marker='.',label='Dati')\n"",
+ ""#plt.plot(ydata_inf[0:27],marker='.',label='Data used for fitting',color='cyan')\n"",
+ ""plt.plot(pred_lockdown_vaccine_zero[2],label='Modello senza vaccini')\n"",
+ ""plt.plot(pred_lockdown_vaccine[2],label='Modello con vaccini (velocità attuale)')\n"",
+ ""plt.plot(pred_lockdown_vaccine_best[2],label='Modello con vaccini (2.5 x velocità attuale)')\n"",
+ ""#plt.fill_between(xgrid,pred_lockdown_vaccine[2],pred_lockdown_vaccine_best[2],color='black',alpha=0.3)\n"",
+ ""plt.title('Infetti attivi')\n"",
+ ""plt.ylabel('Numero totale')\n"",
+ ""plt.xticks(np.arange(6,250,30.5),['Mar','Apr','Mag','Giu','Lug','Ago','Set','Ott'],rotation=30)\n"",
+ ""plt.xlim(0,180)\n"",
+ ""plt.grid()\n"",
+ ""plt.legend()\n"",
+ ""plt.subplot(2,2,2)\n"",
+ ""plt.title('Frazione di popolazione vaccinata')\n"",
+ ""plt.plot(vaccinated_1dose,label='Dati, con 1 dose')\n"",
+ ""plt.plot(vaccinated_2dose,label='Dati, con 2 dosi',color='red')\n"",
+ ""plt.plot(pred_lockdown_vaccine_zero[4]/inhabitants*100,label='Modello senza vaccini',linestyle='--')\n"",
+ ""plt.plot(pred_lockdown_vaccine[4]/inhabitants*100,label='Modello con vaccini (velocità attuale)',linestyle='--')\n"",
+ ""plt.plot(pred_lockdown_vaccine_best[4]/inhabitants*100,label='Modello con vaccini (2.5 x velocità attuale)',linestyle='--')\n"",
+ ""plt.xticks(np.arange(6,250,30.5),['Mar','Apr','Mag','Giu','Lug','Ago','Set','Ott'],rotation=30)\n"",
+ ""plt.yticks(np.arange(0,102,10),['0%','10%','20%','30%','35%','40%','50%','60%','70%','80%','90%','100%'])\n"",
+ ""plt.ylim(0,100)\n"",
+ ""plt.xlim(0,180)\n"",
+ ""plt.grid()\n"",
+ ""plt.legend()\n"",
+ ""plt.subplot(2,2,3)\n"",
+ ""plt.plot(weekly_incidence,color='blue')\n"",
+ ""plt.title('Incidenza settimanale')\n"",
+ ""plt.ylabel('Numeri per 100.000 abitanti')\n"",
+ ""plt.xticks(np.arange(0,100,14),xticks_21,rotation=30)\n"",
+ ""plt.ylim(0,300)\n"",
+ ""plt.xlim(0,len(vaccinated_1dose)+3)\n"",
+ ""plt.grid()\n"",
+ ""plt.subplot(2,2,4)\n"",
+ ""plt.plot(daily_deaths,color='black')\n"",
+ ""plt.title('Decessi giornalieri (media mobile 7gg)')\n"",
+ ""plt.ylabel('Numeri per milione di abitanti')\n"",
+ ""plt.xticks(np.arange(0,100,14),xticks_21,rotation=30)\n"",
+ ""plt.xlim(0,len(vaccinated_1dose)+3)\n"",
+ ""plt.grid()\n"",
+ ""plt.tight_layout()\n"",
+ ""plt.savefig('results/predicted-italy_3rdwave.png',dpi=300)\n"",
+ ""plt.show()""
+ ]
+ },
+ {
+ ""cell_type"": ""markdown"",
+ ""metadata"": {},
+ ""source"": [
+ ""### grafico best fit""
+ ]
+ },
+ {
+ ""cell_type"": ""code"",
+ ""execution_count"": 43,
+ ""metadata"": {},
+ ""outputs"": [
+ {
+ ""name"": ""stdout"",
+ ""output_type"": ""stream"",
+ ""text"": [
+ ""Tempo di dimezzamento di Rt = 44.643 giorni\n""
+ ]
+ }
+ ],
+ ""source"": [
+ ""t12 = 0.69*tau\n"",
+ ""\n"",
+ ""print('Tempo di dimezzamento di Rt = ',t12,'giorni')\n"",
+ ""\n"",
+ ""xlab = ['Mar','Apr','May','Jun','Jul','Aug','Sep','Oct']""
+ ]
+ },
+ {
+ ""cell_type"": ""code"",
+ ""execution_count"": 45,
+ ""metadata"": {},
+ ""outputs"": [
+ {
+ ""data"": {
+ ""image/png"": 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\n"",
+ ""text/plain"": [
+ """"
+ ]
+ },
+ ""metadata"": {
+ ""needs_background"": ""light""
+ },
+ ""output_type"": ""display_data""
+ }
+ ],
+ ""source"": [
+ ""# check del modello, con curve di suscettibili, infetti e rimossi\n"",
+ ""\n"",
+ ""plt.figure(figsize=(10,8))\n"",
+ ""plt.subplot(2,2,1)\n"",
+ ""plt.plot(inhabitants-ydata_cases,label='Data')\n"",
+ ""plt.plot(pred_lockdown_vaccine[1],linestyle='--',\n"",
+ "" label='Model, $\\\\beta$='+str(beta)+', $\\gamma$='+str(gamma)+', $\\\\tau$='+str(tau))\n"",
+ ""plt.xticks(np.arange(6,250,30.5),xlab,rotation=30)\n"",
+ ""plt.xlim(0,220)\n"",
+ ""plt.yticks(10**7*np.arange(0,6.1,0.5),['0','5M','10M','15M','20M','25M','30M','35M','40M','45M','50M','55M','60M'])\n"",
+ ""plt.ylim(40*10**6,60*10**6)\n"",
+ ""plt.title('Susceptible')\n"",
+ ""plt.legend()\n"",
+ ""plt.subplot(2,2,2)\n"",
+ ""plt.plot(ydata_inf_complete,label='Data')\n"",
+ ""plt.plot(pred_lockdown_vaccine[2],linestyle='--',\n"",
+ "" label='Model, $\\\\beta$='+str(beta)+', $\\gamma$='+str(gamma)+', $\\\\tau$='+str(tau))\n"",
+ ""plt.title('Infected')\n"",
+ ""plt.yticks(10**5*np.arange(0,6.1,1),['0','100k','200k','300k','400k','500k','600k'])\n"",
+ ""plt.ylabel('Total number')\n"",
+ ""plt.xticks(np.arange(6,250,30.5),xlab,rotation=30)\n"",
+ ""plt.xlim(0,220)\n"",
+ ""plt.legend(loc=3)\n"",
+ ""plt.subplot(2,2,3)\n"",
+ ""plt.plot(ydata_rec,label='Data')\n"",
+ ""plt.plot(pred_lockdown_vaccine[3],linestyle='--',\n"",
+ "" label='Model, $\\\\beta$='+str(beta)+', $\\gamma$='+str(gamma)+', $\\\\tau$='+str(tau))\n"",
+ ""plt.xticks(np.arange(6,250,30.5),xlab,rotation=30)\n"",
+ ""plt.xlim(0,220)\n"",
+ ""plt.title('Recovered + Deaths')\n"",
+ ""plt.yticks(10**6*np.arange(0,6.1,0.5),['0','0.5M','1M','1.5M','2M','2.5M','3M','3.5M','4M','4.5M','5M','5.5M','6M'])\n"",
+ ""plt.ylim(2*10**6,5*10**6)\n"",
+ ""plt.legend()\n"",
+ ""plt.subplot(2,2,4)\n"",
+ ""plt.title('Vaccinated')\n"",
+ ""plt.plot(vaccinated_2dose,label='Data (completely vaccinated with 2 doses)')\n"",
+ ""plt.plot(pred_lockdown_vaccine[4]/inhabitants*100,linestyle='--',\n"",
+ "" label='Model, $\\\\beta$='+str(beta)+', $\\gamma$='+str(gamma)+', $\\\\tau$='+str(tau))\n"",
+ ""#plt.plot(pred_lockdown_vaccine_best[4]/inhabitants*100,label='Modello con vaccini (2.5 x velocità attuale)')\n"",
+ ""plt.xticks(np.arange(6,250,30.5),xlab,rotation=30)\n"",
+ ""plt.yticks(np.arange(0,31,5),['0%','5%','10%','15%','20%','25%','30%'])\n"",
+ ""plt.ylim(0,30)\n"",
+ ""plt.xlim(0,220)\n"",
+ ""plt.legend()\n"",
+ ""plt.tight_layout()\n"",
+ ""plt.savefig('results/modello_italia_ondata3_vaccini.png',dpi=300)\n"",
+ ""plt.show()""
+ ]
+ },
+ {
+ ""cell_type"": ""markdown"",
+ ""metadata"": {},
+ ""source"": [
+ ""### grafico con bande""
+ ]
+ },
+ {
+ ""cell_type"": ""code"",
+ ""execution_count"": 53,
+ ""metadata"": {},
+ ""outputs"": [],
+ ""source"": [
+ ""tmax = 220\n"",
+ ""xgrid = np.arange(0,len(pred_lockdown_vaccine[1]))""
+ ]
+ },
+ {
+ ""cell_type"": ""code"",
+ ""execution_count"": 77,
+ ""metadata"": {},
+ ""outputs"": [
+ {
+ ""data"": {
+ ""image/png"": 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\n"",
+ ""text/plain"": [
+ """"
+ ]
+ },
+ ""metadata"": {
+ ""needs_background"": ""light""
+ },
+ ""output_type"": ""display_data""
+ }
+ ],
+ ""source"": [
+ ""plt.figure(figsize=(10,8))\n"",
+ ""plt.subplot(2,2,1)\n"",
+ ""plt.plot(inhabitants-ydata_cases,label='Data',color='blue',marker='.')\n"",
+ ""plt.fill_between(xgrid,pred_lockdown_vaccine[1],pred_lockdown_vaccine_best[1],\n"",
+ "" label='Modello con vaccinazioni',alpha=0.5,color='green')\n"",
+ ""plt.plot(pred_lockdown_vaccine_zero[1],label='Modello senza vaccinazioni',color='red')\n"",
+ ""plt.xticks(np.arange(6,250,30.5),xlab,rotation=30)\n"",
+ ""plt.xlim(0,tmax)\n"",
+ ""plt.yticks(10**7*np.arange(0,6.1,0.5),['0','5M','10M','15M','20M','25M','30M','35M','40M','45M','50M','55M','60M'])\n"",
+ ""plt.ylim(30*10**6,60*10**6)\n"",
+ ""plt.title('Susceptible')\n"",
+ ""plt.legend()\n"",
+ ""plt.subplot(2,2,2)\n"",
+ ""plt.plot(ydata_inf_complete,label='Data',color='blue',marker='.')\n"",
+ ""plt.fill_between(xgrid,pred_lockdown_vaccine[2],pred_lockdown_vaccine_best[2],\n"",
+ "" label='Modello con vaccinazioni',alpha=0.5,color='green')\n"",
+ ""plt.plot(pred_lockdown_vaccine_zero[2],label='Modello senza vaccinazioni',color='red')\n"",
+ ""plt.title('Infected')\n"",
+ ""plt.yticks(10**5*np.arange(0,6.1,1),['0','100k','200k','300k','400k','500k','600k'])\n"",
+ ""plt.ylabel('Total number')\n"",
+ ""plt.xticks(np.arange(6,250,30.5),xlab,rotation=30)\n"",
+ ""plt.xlim(0,tmax)\n"",
+ ""plt.legend(loc=3)\n"",
+ ""plt.subplot(2,2,3)\n"",
+ ""plt.plot(ydata_rec,label='Data',color='blue',marker='.')\n"",
+ ""plt.fill_between(xgrid,pred_lockdown_vaccine[3],pred_lockdown_vaccine_best[3],\n"",
+ "" label='Modello con vaccinazioni',alpha=0.5,color='green')\n"",
+ ""plt.plot(pred_lockdown_vaccine_zero[3],label='Modello senza vaccinazioni',color='red')\n"",
+ ""plt.xticks(np.arange(6,250,30.5),xlab,rotation=30)\n"",
+ ""plt.xlim(0,tmax)\n"",
+ ""plt.title('Recovered + Deaths')\n"",
+ ""plt.yticks(10**6*np.arange(0,6.1,0.5),['0','0.5M','1M','1.5M','2M','2.5M','3M','3.5M','4M','4.5M','5M','5.5M','6M'])\n"",
+ ""plt.ylim(2*10**6,5*10**6)\n"",
+ ""plt.legend()\n"",
+ ""plt.subplot(2,2,4)\n"",
+ ""plt.title('Vaccinated')\n"",
+ ""plt.plot(vaccinated_2dose,label='Data (completely vaccinated with 2 doses)',color='blue',marker='.')\n"",
+ ""plt.fill_between(xgrid,pred_lockdown_vaccine_best[4]/inhabitants*100,\n"",
+ "" pred_lockdown_vaccine[4]/inhabitants*100,\n"",
+ "" label='Modello con vaccinazioni',alpha=0.5,color='green')\n"",
+ ""plt.plot(pred_lockdown_vaccine_zero[4],label='Modello senza vaccinazioni',color='red')\n"",
+ ""plt.xticks(np.arange(6,250,30.5),xlab,rotation=30)\n"",
+ ""plt.yticks(np.arange(0,46,5),['0%','5%','10%','15%','20%','25%','30%','35%','40%','45%'])\n"",
+ ""plt.ylim(0,45)\n"",
+ ""plt.xlim(0,tmax)\n"",
+ ""plt.legend()\n"",
+ ""plt.tight_layout()\n"",
+ ""plt.savefig('results/modello_italia_ondata3_vaccini_bande.png',dpi=300)\n"",
+ ""plt.show()""
+ ]
+ },
+ {
+ ""cell_type"": ""code"",
+ ""execution_count"": null,
+ ""metadata"": {},
+ ""outputs"": [],
+ ""source"": []
+ },
+ {
+ ""cell_type"": ""code"",
+ ""execution_count"": null,
+ ""metadata"": {},
+ ""outputs"": [],
+ ""source"": []
+ }
+ ],
+ ""metadata"": {
+ ""colab"": {
+ ""collapsed_sections"": [],
+ ""name"": ""vaccini.ipynb"",
+ ""provenance"": []
+ },
+ ""kernelspec"": {
+ ""display_name"": ""Python 3"",
+ ""language"": ""python"",
+ ""name"": ""python3""
+ },
+ ""language_info"": {
+ ""codemirror_mode"": {
+ ""name"": ""ipython"",
+ ""version"": 3
+ },
+ ""file_extension"": "".py"",
+ ""mimetype"": ""text/x-python"",
+ ""name"": ""python"",
+ ""nbconvert_exporter"": ""python"",
+ ""pygments_lexer"": ""ipython3"",
+ ""version"": ""3.6.6""
+ }
+ },
+ ""nbformat"": 4,
+ ""nbformat_minor"": 4
+}
+","Unknown"
+"Vaccine","apalladi/covid_vaccine_model","israel_SIR.ipynb",".ipynb","140288","682","{
+ ""cells"": [
+ {
+ ""cell_type"": ""code"",
+ ""execution_count"": 1,
+ ""metadata"": {},
+ ""outputs"": [],
+ ""source"": [
+ ""import matplotlib.pyplot as plt \n"",
+ ""import pandas as pd\n"",
+ ""import numpy as np""
+ ]
+ },
+ {
+ ""cell_type"": ""markdown"",
+ ""metadata"": {},
+ ""source"": [
+ ""# Load data""
+ ]
+ },
+ {
+ ""cell_type"": ""code"",
+ ""execution_count"": 2,
+ ""metadata"": {},
+ ""outputs"": [],
+ ""source"": [
+ ""from src.load_data import get_epidemic_data, get_vaccine_data""
+ ]
+ },
+ {
+ ""cell_type"": ""markdown"",
+ ""metadata"": {},
+ ""source"": [
+ ""###Â Epidemic data""
+ ]
+ },
+ {
+ ""cell_type"": ""code"",
+ ""execution_count"": 3,
+ ""metadata"": {},
+ ""outputs"": [
+ {
+ ""data"": {
+ ""text/html"": [
+ ""\n"",
+ ""\n"",
+ ""
\n"",
+ "" \n"",
+ "" \n"",
+ "" | \n"",
+ "" Total cases | \n"",
+ "" Active infected | \n"",
+ "" Total deaths | \n"",
+ "" Total recovered | \n"",
+ "" Daily cases (avg 7 days) | \n"",
+ "" Daily deaths (avg 7 days) | \n"",
+ ""
\n"",
+ "" \n"",
+ "" \n"",
+ "" \n"",
+ "" | 2020-01-22 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2020-01-23 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2020-01-24 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2020-01-25 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2020-01-26 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ ""
\n"",
+ "" \n"",
+ ""
\n"",
+ ""
\n"",
+ ""\n"",
+ ""
\n"",
+ "" \n"",
+ "" \n"",
+ "" | \n"",
+ "" % vaccinated with 1 dose | \n"",
+ "" % vaccinated with 2 doses | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | date | \n"",
+ "" | \n"",
+ "" | \n"",
+ ""
\n"",
+ "" \n"",
+ "" \n"",
+ "" \n"",
+ "" | 2020-12-21 | \n"",
+ "" 0.37 | \n"",
+ "" 0.0 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2020-12-22 | \n"",
+ "" 0.89 | \n"",
+ "" 0.0 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2020-12-23 | \n"",
+ "" 1.61 | \n"",
+ "" 0.0 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2020-12-24 | \n"",
+ "" 2.46 | \n"",
+ "" 0.0 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2020-12-25 | \n"",
+ "" 2.91 | \n"",
+ "" 0.0 | \n"",
+ ""
\n"",
+ "" \n"",
+ ""
\n"",
+ ""
\n"",
+ ""\n"",
+ ""
\n"",
+ "" \n"",
+ "" \n"",
+ "" | \n"",
+ "" % vaccinated with 1 dose | \n"",
+ "" % vaccinated with 2 doses | \n"",
+ "" Total cases | \n"",
+ "" Active infected | \n"",
+ "" Total deaths | \n"",
+ "" Total recovered | \n"",
+ "" Daily cases (avg 7 days) | \n"",
+ "" Daily deaths (avg 7 days) | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | date | \n"",
+ "" | \n"",
+ "" | \n"",
+ "" | \n"",
+ "" | \n"",
+ "" | \n"",
+ "" | \n"",
+ "" | \n"",
+ "" | \n"",
+ ""
\n"",
+ "" \n"",
+ "" \n"",
+ "" \n"",
+ "" | 2020-12-21 | \n"",
+ "" 0.37 | \n"",
+ "" 0.0 | \n"",
+ "" 378259.0 | \n"",
+ "" 26274.0 | \n"",
+ "" 3111.0 | \n"",
+ "" 348874.0 | \n"",
+ "" 2679.0 | \n"",
+ "" 15.3 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2020-12-22 | \n"",
+ "" 0.89 | \n"",
+ "" 0.0 | \n"",
+ "" 382487.0 | \n"",
+ "" 28164.0 | \n"",
+ "" 3136.0 | \n"",
+ "" 351187.0 | \n"",
+ "" 3122.4 | \n"",
+ "" 17.4 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2020-12-23 | \n"",
+ "" 1.61 | \n"",
+ "" 0.0 | \n"",
+ "" 385022.0 | \n"",
+ "" 29996.0 | \n"",
+ "" 3150.0 | \n"",
+ "" 351876.0 | \n"",
+ "" 2854.3 | \n"",
+ "" 16.6 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2020-12-24 | \n"",
+ "" 2.46 | \n"",
+ "" 0.0 | \n"",
+ "" 389678.0 | \n"",
+ "" 31833.0 | \n"",
+ "" 3171.0 | \n"",
+ "" 354674.0 | \n"",
+ "" 3100.4 | \n"",
+ "" 17.3 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2020-12-25 | \n"",
+ "" 2.91 | \n"",
+ "" 0.0 | \n"",
+ "" 394391.0 | \n"",
+ "" 33337.0 | \n"",
+ "" 3186.0 | \n"",
+ "" 357868.0 | \n"",
+ "" 3462.7 | \n"",
+ "" 18.4 | \n"",
+ ""
\n"",
+ "" \n"",
+ ""
\n"",
+ ""
""
+ ]
+ },
+ ""metadata"": {
+ ""needs_background"": ""light""
+ },
+ ""output_type"": ""display_data""
+ }
+ ],
+ ""source"": [
+ ""# check del modello, con curve di suscettibili, infetti e rimossi\n"",
+ ""\n"",
+ ""plt.figure(figsize=(13,4.5))\n"",
+ ""plt.subplot(1,3,1)\n"",
+ ""plt.plot(inhabitants_israel-ydata_cases,label='Dati')\n"",
+ ""plt.plot(model_check[1],\n"",
+ "" linestyle='--',label='Model, $\\\\beta$='+str(beta)+', $\\gamma$='+str(gamma)+', $\\\\tau$='+str(tau))\n"",
+ ""plt.xlim(0,tmax)\n"",
+ ""plt.ylim(8.2*10**6,8.5*10**6)\n"",
+ ""plt.title('Susceptible')\n"",
+ ""plt.legend()\n"",
+ ""plt.subplot(1,3,2)\n"",
+ ""plt.plot(ydata_inf,label='Dati')\n"",
+ ""plt.plot(model_check[2],\n"",
+ "" linestyle='--',label='Model, $\\\\beta$='+str(beta)+', $\\gamma$='+str(gamma)+', $\\\\tau$='+str(tau))\n"",
+ ""plt.xlim(0,tmax)\n"",
+ ""plt.ylim(2*10**4,10*10**4)\n"",
+ ""plt.title('Infected')\n"",
+ ""plt.legend()\n"",
+ ""plt.subplot(1,3,3)\n"",
+ ""plt.plot(ydata_rec,label='Dati')\n"",
+ ""plt.plot(model_check[3],\n"",
+ "" linestyle='--',label='Model, $\\\\beta$='+str(beta)+', $\\gamma$='+str(gamma)+', $\\\\tau$='+str(tau))\n"",
+ ""plt.xlim(0,tmax)\n"",
+ ""plt.ylim(3*10**5,5*10**5)\n"",
+ ""plt.title('Removed')\n"",
+ ""plt.legend()\n"",
+ ""plt.tight_layout()\n"",
+ ""plt.savefig('results/check_model_israel.png',dpi=300)\n"",
+ ""plt.show()""
+ ]
+ },
+ {
+ ""cell_type"": ""markdown"",
+ ""metadata"": {},
+ ""source"": [
+ ""### previsioni del modello epidemiologico""
+ ]
+ },
+ {
+ ""cell_type"": ""code"",
+ ""execution_count"": 11,
+ ""metadata"": {},
+ ""outputs"": [],
+ ""source"": [
+ ""# lockdown only, vacc_speed = 0 \n"",
+ ""pred_lockdown=SIR2(inhabitants_israel,minpar[0],minpar[1],minpar[2],\n"",
+ "" vacc_eff=0.95,vacc_speed = 0,t0=0,\n"",
+ "" I0=ydata_inf[0],R0=ydata_rec[0],V0=0)\n"",
+ ""\n"",
+ ""# lockdown plus vaccinations, vacc_speed = 1.28 derived from data \n"",
+ ""# t0 = 21 denotes the day in which people starting to receive the 2° dose of vaccine\n"",
+ ""pred_lockdown_vaccine=SIR2(inhabitants_israel,minpar[0],minpar[1],minpar[2],\n"",
+ "" vacc_eff=0.95,vacc_speed = 1.28,t0=21,\n"",
+ "" I0=ydata_inf[0],R0=ydata_rec[0],V0=0) ""
+ ]
+ },
+ {
+ ""cell_type"": ""markdown"",
+ ""metadata"": {},
+ ""source"": [
+ ""# Risultati""
+ ]
+ },
+ {
+ ""cell_type"": ""code"",
+ ""execution_count"": 12,
+ ""metadata"": {},
+ ""outputs"": [],
+ ""source"": [
+ ""# vaccinati con 1 dose e con 2 dosi\n"",
+ ""vaccinated_1dose = np.array(df_merged['% vaccinated with 1 dose'])\n"",
+ ""vaccinated_2dose = np.array(df_merged['% vaccinated with 2 doses'])\n"",
+ ""\n"",
+ ""# incidenza settimanale ogni 100.000 abitanti\n"",
+ ""weekly_incidence = 7/(inhabitants_israel/10**5)*np.array(df_merged['Daily cases (avg 7 days)'])\n"",
+ ""\n"",
+ ""# decessi giornalieri per milione di abitanti\n"",
+ ""daily_deaths = 1/(inhabitants_israel/10**6)*np.array(df_merged['Daily deaths (avg 7 days)'])\n"",
+ ""\n"",
+ ""# dati sugli infetti attivi\n"",
+ ""ydata_inf_complete = np.array(df_merged['Active infected'])\n"",
+ ""\n"",
+ ""xticks_21 = ['21 Dec','4 Jan','18 Jan','1 Feb','15 Feb','1 Mar','15 Mar','29 Mar','12 Apr','26 Apr']""
+ ]
+ },
+ {
+ ""cell_type"": ""code"",
+ ""execution_count"": 13,
+ ""metadata"": {},
+ ""outputs"": [
+ {
+ ""data"": {
+ ""image/png"": 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\n"",
+ ""text/plain"": [
+ """"
+ ]
+ },
+ ""metadata"": {
+ ""needs_background"": ""light""
+ },
+ ""output_type"": ""display_data""
+ }
+ ],
+ ""source"": [
+ ""plt.figure(figsize=(10,5))\n"",
+ ""plt.subplot(1,2,1)\n"",
+ ""plt.plot(ydata_inf_complete,marker='.',label='Data')\n"",
+ ""plt.plot(ydata_inf[0:27],marker='.',label='Data used for fitting',color='cyan')\n"",
+ ""plt.plot(pred_lockdown[2],label='SIR 2.0 without vaccine',color='green')\n"",
+ ""plt.plot(pred_lockdown_vaccine[2],label='SIR 2.0, with vaccine',color='red')\n"",
+ ""plt.title('Active infected')\n"",
+ ""plt.ylabel('Total number')\n"",
+ ""plt.xticks(np.arange(0,120,14),xticks_21,rotation=30)\n"",
+ ""plt.xlim(0,120)\n"",
+ ""plt.ylim(0,2*10**5)\n"",
+ ""plt.legend()\n"",
+ ""plt.subplot(1,2,2)\n"",
+ ""plt.title('Fraction of vaccinated inhabitants')\n"",
+ ""plt.plot(vaccinated_1dose,label='With 1 dose',color='orange')\n"",
+ ""plt.plot(vaccinated_2dose,label='With 2 doses',color='red')\n"",
+ ""plt.plot(pred_lockdown_vaccine[4]/inhabitants_israel*100,label='Vaccinated in the model',color='red',linestyle='--')\n"",
+ ""plt.xticks(np.arange(0,100,14),xticks_21,rotation=30)\n"",
+ ""plt.yticks(np.arange(0,111,10),['0%','10%','20%','30%','40%','50%','60%','70%','80%','90%','100%','110%'])\n"",
+ ""plt.ylim(0,65)\n"",
+ ""plt.xlim(0,len(vaccinated_1dose)+3)\n"",
+ ""plt.legend()\n"",
+ ""#plt.subplot(2,2,3)\n"",
+ ""#plt.plot(weekly_incidence,color='blue')\n"",
+ ""#plt.title('Weekly incidence')\n"",
+ ""#plt.ylabel('Number per 100k inhabitants')\n"",
+ ""#plt.xticks(np.arange(0,100,14),xticks_21,rotation=30)\n"",
+ ""#plt.xlim(0,len(vaccinated_1dose)+3)\n"",
+ ""#plt.ylim(0,700)\n"",
+ ""#plt.grid()\n"",
+ ""#plt.subplot(2,2,4)\n"",
+ ""#plt.plot(daily_deaths,color='black')\n"",
+ ""#plt.title('Daily deaths (7 days moving average)')\n"",
+ ""#plt.ylabel('Number per million')\n"",
+ ""#plt.xticks(np.arange(0,100,14),xticks_21,rotation=30)\n"",
+ ""#plt.xlim(0,len(vaccinated_1dose)+3)\n"",
+ ""#plt.grid()\n"",
+ ""plt.tight_layout()\n"",
+ ""plt.savefig('results/predicted-israel.png',dpi=300)\n"",
+ ""plt.show()""
+ ]
+ },
+ {
+ ""cell_type"": ""code"",
+ ""execution_count"": null,
+ ""metadata"": {},
+ ""outputs"": [],
+ ""source"": []
+ },
+ {
+ ""cell_type"": ""code"",
+ ""execution_count"": null,
+ ""metadata"": {},
+ ""outputs"": [],
+ ""source"": []
+ }
+ ],
+ ""metadata"": {
+ ""colab"": {
+ ""collapsed_sections"": [],
+ ""name"": ""vaccini.ipynb"",
+ ""provenance"": []
+ },
+ ""kernelspec"": {
+ ""display_name"": ""Python 3"",
+ ""language"": ""python"",
+ ""name"": ""python3""
+ },
+ ""language_info"": {
+ ""codemirror_mode"": {
+ ""name"": ""ipython"",
+ ""version"": 3
+ },
+ ""file_extension"": "".py"",
+ ""mimetype"": ""text/x-python"",
+ ""name"": ""python"",
+ ""nbconvert_exporter"": ""python"",
+ ""pygments_lexer"": ""ipython3"",
+ ""version"": ""3.6.10""
+ }
+ },
+ ""nbformat"": 4,
+ ""nbformat_minor"": 4
+}
+","Unknown"
+"Vaccine","apalladi/covid_vaccine_model","italy_3rdwave_vaccine-betacostante.ipynb",".ipynb","180388","766","{
+ ""cells"": [
+ {
+ ""cell_type"": ""code"",
+ ""execution_count"": 1,
+ ""metadata"": {},
+ ""outputs"": [],
+ ""source"": [
+ ""import matplotlib.pyplot as plt \n"",
+ ""import pandas as pd\n"",
+ ""import numpy as np""
+ ]
+ },
+ {
+ ""cell_type"": ""markdown"",
+ ""metadata"": {},
+ ""source"": [
+ ""# Load data""
+ ]
+ },
+ {
+ ""cell_type"": ""code"",
+ ""execution_count"": 2,
+ ""metadata"": {},
+ ""outputs"": [],
+ ""source"": [
+ ""from src.load_data import get_epidemic_data, get_vaccine_data""
+ ]
+ },
+ {
+ ""cell_type"": ""markdown"",
+ ""metadata"": {},
+ ""source"": [
+ ""###Â Epidemic data""
+ ]
+ },
+ {
+ ""cell_type"": ""code"",
+ ""execution_count"": 3,
+ ""metadata"": {},
+ ""outputs"": [
+ {
+ ""data"": {
+ ""text/html"": [
+ ""\n"",
+ ""\n"",
+ ""
\n"",
+ "" \n"",
+ "" \n"",
+ "" | \n"",
+ "" Total cases | \n"",
+ "" Active infected | \n"",
+ "" Total deaths | \n"",
+ "" Total recovered | \n"",
+ "" Daily cases (avg 7 days) | \n"",
+ "" Daily deaths (avg 7 days) | \n"",
+ ""
\n"",
+ "" \n"",
+ "" \n"",
+ "" \n"",
+ "" | 2020-01-22 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2020-01-23 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2020-01-24 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2020-01-25 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2020-01-26 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ "" 0.0 | \n"",
+ ""
\n"",
+ "" \n"",
+ ""
\n"",
+ ""
\n"",
+ ""\n"",
+ ""
\n"",
+ "" \n"",
+ "" \n"",
+ "" | \n"",
+ "" % vaccinated with 1 dose | \n"",
+ "" % vaccinated with 2 doses | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | date | \n"",
+ "" | \n"",
+ "" | \n"",
+ ""
\n"",
+ "" \n"",
+ "" \n"",
+ "" \n"",
+ "" | 2020-12-29 | \n"",
+ "" 0.02 | \n"",
+ "" 0.0 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2020-12-30 | \n"",
+ "" 0.02 | \n"",
+ "" 0.0 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2020-12-31 | \n"",
+ "" 0.07 | \n"",
+ "" 0.0 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2021-01-01 | \n"",
+ "" 0.08 | \n"",
+ "" 0.0 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2021-01-02 | \n"",
+ "" 0.15 | \n"",
+ "" 0.0 | \n"",
+ ""
\n"",
+ "" \n"",
+ ""
\n"",
+ ""
\n"",
+ ""\n"",
+ ""
\n"",
+ "" \n"",
+ "" \n"",
+ "" | \n"",
+ "" % vaccinated with 1 dose | \n"",
+ "" % vaccinated with 2 doses | \n"",
+ "" Total cases | \n"",
+ "" Active infected | \n"",
+ "" Total deaths | \n"",
+ "" Total recovered | \n"",
+ "" Daily cases (avg 7 days) | \n"",
+ "" Daily deaths (avg 7 days) | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | date | \n"",
+ "" | \n"",
+ "" | \n"",
+ "" | \n"",
+ "" | \n"",
+ "" | \n"",
+ "" | \n"",
+ "" | \n"",
+ "" | \n"",
+ ""
\n"",
+ "" \n"",
+ "" \n"",
+ "" \n"",
+ "" | 2021-04-06 | \n"",
+ "" 13.31 | \n"",
+ "" 5.92 | \n"",
+ "" 3686707.0 | \n"",
+ "" 555705.0 | \n"",
+ "" 111747.0 | \n"",
+ "" 3019255.0 | \n"",
+ "" 17956.4 | \n"",
+ "" 409.7 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2021-04-07 | \n"",
+ "" 13.69 | \n"",
+ "" 6.06 | \n"",
+ "" 3700393.0 | \n"",
+ "" 547837.0 | \n"",
+ "" 112374.0 | \n"",
+ "" 3040182.0 | \n"",
+ "" 16499.1 | \n"",
+ "" 432.6 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2021-04-08 | \n"",
+ "" 14.07 | \n"",
+ "" 6.20 | \n"",
+ "" 3717602.0 | \n"",
+ "" 544330.0 | \n"",
+ "" 112861.0 | \n"",
+ "" 3060411.0 | \n"",
+ "" 15788.4 | \n"",
+ "" 430.6 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2021-04-09 | \n"",
+ "" 14.47 | \n"",
+ "" 6.33 | \n"",
+ "" 3736526.0 | \n"",
+ "" 536361.0 | \n"",
+ "" 113579.0 | \n"",
+ "" 3086586.0 | \n"",
+ "" 15360.9 | \n"",
+ "" 464.4 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2021-04-10 | \n"",
+ "" 14.86 | \n"",
+ "" 6.44 | \n"",
+ "" 3754077.0 | \n"",
+ "" 533085.0 | \n"",
+ "" 113923.0 | \n"",
+ "" 3107069.0 | \n"",
+ "" 14832.9 | \n"",
+ "" 459.9 | \n"",
+ ""
\n"",
+ "" \n"",
+ ""
\n"",
+ ""
""
+ ]
+ },
+ ""metadata"": {
+ ""needs_background"": ""light""
+ },
+ ""output_type"": ""display_data""
+ }
+ ],
+ ""source"": [
+ ""# check del modello, con curve di suscettibili, infetti e rimossi\n"",
+ ""\n"",
+ ""plt.figure(figsize=(10,8))\n"",
+ ""plt.subplot(2,2,1)\n"",
+ ""plt.plot(inhabitants-ydata_cases,label='Data')\n"",
+ ""plt.plot(model_check[1][0:ll],\n"",
+ "" linestyle='--',label='Model, $\\\\beta$='+str(beta)+', $\\gamma$='+str(gamma)+', $\\\\tau$='+str(tau))\n"",
+ ""plt.title('Susceptible')\n"",
+ ""plt.legend()\n"",
+ ""plt.subplot(2,2,2)\n"",
+ ""plt.plot(ydata_inf,label='Data')\n"",
+ ""plt.plot(model_check[2][0:ll],\n"",
+ "" linestyle='--',label='Model, $\\\\beta$='+str(beta)+', $\\gamma$='+str(gamma)+', $\\\\tau$='+str(tau))\n"",
+ ""plt.title('Infected')\n"",
+ ""plt.legend()\n"",
+ ""plt.subplot(2,2,3)\n"",
+ ""plt.plot(ydata_rec,label='Data')\n"",
+ ""plt.plot(model_check[3][0:ll],\n"",
+ "" linestyle='--',label='Model, $\\\\beta$='+str(beta)+', $\\gamma$='+str(gamma)+', $\\\\tau$='+str(tau))\n"",
+ ""plt.title('Recovered + Deaths')\n"",
+ ""plt.legend()\n"",
+ ""plt.subplot(2,2,4)\n"",
+ ""plt.plot(ydata_vacc,label='Data')\n"",
+ ""plt.plot(model_check[4][0:ll],\n"",
+ "" linestyle='--',label='Model, $\\\\beta$='+str(beta)+', $\\gamma$='+str(gamma)+', $\\\\tau$='+str(tau))\n"",
+ ""plt.title('Vaccinated')\n"",
+ ""plt.legend()\n"",
+ ""plt.tight_layout()\n"",
+ ""plt.savefig('results/check_model_italy3rd.png',dpi=300)\n"",
+ ""plt.show()""
+ ]
+ },
+ {
+ ""cell_type"": ""markdown"",
+ ""metadata"": {},
+ ""source"": [
+ ""### previsioni del modello epidemiologico""
+ ]
+ },
+ {
+ ""cell_type"": ""code"",
+ ""execution_count"": 12,
+ ""metadata"": {},
+ ""outputs"": [],
+ ""source"": [
+ ""# model that accounts for lockdown (tau) and vaccination (vacc_speed)\n"",
+ ""\n"",
+ ""pred_lockdown_vaccine=SIR2(inhabitants,minpar[0],minpar[1],minpar[2],\n"",
+ "" vacc_eff = vacc_eff,\n"",
+ "" vacc_speed = vacc_speed,\n"",
+ "" t0=0,\n"",
+ "" I0=ydata_inf[0],\n"",
+ "" R0=ydata_rec[0],\n"",
+ "" V0=ydata_vacc[0]) \n"",
+ ""\n"",
+ ""pred_lockdown_vaccine_best=SIR2(inhabitants,minpar[0],minpar[1],minpar[2],\n"",
+ "" vacc_eff = vacc_eff,\n"",
+ "" vacc_speed = vacc_speed,\n"",
+ "" t0=0,\n"",
+ "" I0=ydata_inf[0],\n"",
+ "" R0=ydata_rec[0],\n"",
+ "" V0=ydata_vacc[0],\n"",
+ "" vacc_custom = [len(ydata_cases),0.3]) \n"",
+ ""\n"",
+ ""pred_lockdown_vaccine_zero=SIR2(inhabitants,minpar[0],minpar[1],minpar[2],\n"",
+ "" vacc_eff = vacc_eff,\n"",
+ "" vacc_speed = vacc_speed,\n"",
+ "" t0=0,\n"",
+ "" I0=ydata_inf[0],\n"",
+ "" R0=ydata_rec[0],\n"",
+ "" V0=ydata_vacc[0],\n"",
+ "" vacc_custom = [len(ydata_cases),0]) ""
+ ]
+ },
+ {
+ ""cell_type"": ""markdown"",
+ ""metadata"": {},
+ ""source"": [
+ ""# Risultati""
+ ]
+ },
+ {
+ ""cell_type"": ""code"",
+ ""execution_count"": 15,
+ ""metadata"": {},
+ ""outputs"": [],
+ ""source"": [
+ ""# vaccinati con 1 dose e con 2 dosi\n"",
+ ""vaccinated_1dose = np.array(df_merged['% vaccinated with 1 dose'])\n"",
+ ""vaccinated_2dose = np.array(df_merged['% vaccinated with 2 doses'])\n"",
+ ""\n"",
+ ""# incidenza settimanale ogni 100.000 abitanti\n"",
+ ""weekly_incidence = 7/(inhabitants/10**5)*np.array(df_merged['Daily cases (avg 7 days)'])\n"",
+ ""\n"",
+ ""# decessi giornalieri per milione di abitanti\n"",
+ ""daily_deaths = 1/(inhabitants/10**6)*np.array(df_merged['Daily deaths (avg 7 days)'])\n"",
+ ""\n"",
+ ""# dati sugli infetti attivi\n"",
+ ""ydata_inf_complete = np.array(df_merged['Active infected'])\n"",
+ ""\n"",
+ ""xlab = ['May','Jun','Jul','Aug','Sep','Oct']\n"",
+ ""\n"",
+ ""tmax = 150\n"",
+ ""xgrid = np.arange(0,len(pred_lockdown_vaccine[1]))""
+ ]
+ },
+ {
+ ""cell_type"": ""code"",
+ ""execution_count"": 20,
+ ""metadata"": {},
+ ""outputs"": [
+ {
+ ""data"": {
+ ""image/png"": 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\n"",
+ ""text/plain"": [
+ """"
+ ]
+ },
+ ""metadata"": {
+ ""needs_background"": ""light""
+ },
+ ""output_type"": ""display_data""
+ }
+ ],
+ ""source"": [
+ ""plt.figure(figsize=(10,8))\n"",
+ ""plt.subplot(2,2,1)\n"",
+ ""plt.plot(inhabitants-ydata_cases,label='Data',color='blue',marker='.')\n"",
+ ""plt.fill_between(xgrid,pred_lockdown_vaccine[1],pred_lockdown_vaccine_best[1],\n"",
+ "" label='SIR model with vaccinations',alpha=0.5,color='green')\n"",
+ ""plt.plot(pred_lockdown_vaccine_zero[1],label='SIR model without vaccinations',color='red')\n"",
+ ""plt.xticks(np.arange(25,250,30.5),xlab,rotation=30)\n"",
+ ""plt.xlim(0,tmax)\n"",
+ ""plt.yticks(10**7*np.arange(0,6.1,0.5),['0','5M','10M','15M','20M','25M','30M','35M','40M','45M','50M','55M','60M'])\n"",
+ ""plt.ylim(30*10**6,60*10**6)\n"",
+ ""plt.title('Susceptible')\n"",
+ ""plt.legend()\n"",
+ ""plt.subplot(2,2,2)\n"",
+ ""plt.plot(ydata_inf_complete,label='Data',color='blue',marker='.')\n"",
+ ""plt.fill_between(xgrid,pred_lockdown_vaccine[2],pred_lockdown_vaccine_best[2],\n"",
+ "" label='SIR model with vaccinations',alpha=0.5,color='green')\n"",
+ ""plt.plot(pred_lockdown_vaccine_zero[2],label='SIR model without vaccinations',color='red')\n"",
+ ""plt.title('Infected')\n"",
+ ""plt.yticks(10**5*np.arange(0,6.1,1),['0','100k','200k','300k','400k','500k','600k'])\n"",
+ ""plt.ylabel('Total number')\n"",
+ ""plt.xticks(np.arange(25,250,30.5),xlab,rotation=30)\n"",
+ ""plt.xlim(0,tmax)\n"",
+ ""plt.legend()\n"",
+ ""plt.subplot(2,2,3)\n"",
+ ""plt.plot(ydata_rec,label='Data',color='blue',marker='.')\n"",
+ ""plt.fill_between(xgrid,pred_lockdown_vaccine[3],pred_lockdown_vaccine_best[3],\n"",
+ "" label='SIR model with vaccinations',alpha=0.5,color='green')\n"",
+ ""plt.plot(pred_lockdown_vaccine_zero[3],label='SIR model without vaccinations',color='red')\n"",
+ ""plt.xticks(np.arange(25,250,30.5),xlab,rotation=30)\n"",
+ ""plt.xlim(0,tmax)\n"",
+ ""plt.title('Recovered + Deaths')\n"",
+ ""plt.yticks(10**6*np.arange(0,6.1,0.5),['0','0.5M','1M','1.5M','2M','2.5M','3M','3.5M','4M','4.5M','5M','5.5M','6M'])\n"",
+ ""plt.ylim(2.5*10**6,5.5*10**6)\n"",
+ ""plt.legend()\n"",
+ ""plt.subplot(2,2,4)\n"",
+ ""plt.title('Vaccinated')\n"",
+ ""plt.plot(vaccinated_2dose,label='Data (vaccinated with 2 doses)',color='blue',marker='.')\n"",
+ ""plt.fill_between(xgrid,pred_lockdown_vaccine_best[4]/inhabitants*100,\n"",
+ "" pred_lockdown_vaccine[4]/inhabitants*100,\n"",
+ "" label='SIR model with vaccinations',alpha=0.5,color='green')\n"",
+ ""plt.plot(pred_lockdown_vaccine_zero[4]/inhabitants*100,label='SIR model without vaccinations',color='red')\n"",
+ ""plt.xticks(np.arange(2,250,30.5),xlab,rotation=30)\n"",
+ ""plt.yticks(np.arange(0,101,10),['0%','10%','20%','30%','40%','50%','60%','70%','80%','90%','100%'])\n"",
+ ""plt.ylim(0,50)\n"",
+ ""plt.xlim(0,tmax)\n"",
+ ""plt.legend()\n"",
+ ""plt.tight_layout()\n"",
+ ""plt.savefig('results/modello_italia_ondata3_vaccini_bande.png',dpi=300)\n"",
+ ""plt.show()""
+ ]
+ },
+ {
+ ""cell_type"": ""code"",
+ ""execution_count"": null,
+ ""metadata"": {},
+ ""outputs"": [],
+ ""source"": []
+ },
+ {
+ ""cell_type"": ""code"",
+ ""execution_count"": null,
+ ""metadata"": {},
+ ""outputs"": [],
+ ""source"": []
+ }
+ ],
+ ""metadata"": {
+ ""colab"": {
+ ""collapsed_sections"": [],
+ ""name"": ""vaccini.ipynb"",
+ ""provenance"": []
+ },
+ ""kernelspec"": {
+ ""display_name"": ""Python 3"",
+ ""language"": ""python"",
+ ""name"": ""python3""
+ },
+ ""language_info"": {
+ ""codemirror_mode"": {
+ ""name"": ""ipython"",
+ ""version"": 3
+ },
+ ""file_extension"": "".py"",
+ ""mimetype"": ""text/x-python"",
+ ""name"": ""python"",
+ ""nbconvert_exporter"": ""python"",
+ ""pygments_lexer"": ""ipython3"",
+ ""version"": ""3.6.6""
+ }
+ },
+ ""nbformat"": 4,
+ ""nbformat_minor"": 4
+}
+","Unknown"
+"Vaccine","apalladi/covid_vaccine_model","italy_1stwave.ipynb",".ipynb","70288","311","{
+ ""cells"": [
+ {
+ ""cell_type"": ""code"",
+ ""execution_count"": 1,
+ ""metadata"": {},
+ ""outputs"": [],
+ ""source"": [
+ ""import matplotlib.pyplot as plt \n"",
+ ""import pandas as pd\n"",
+ ""import numpy as np""
+ ]
+ },
+ {
+ ""cell_type"": ""markdown"",
+ ""metadata"": {},
+ ""source"": [
+ ""# Load epidemic italian data""
+ ]
+ },
+ {
+ ""cell_type"": ""code"",
+ ""execution_count"": 2,
+ ""metadata"": {},
+ ""outputs"": [],
+ ""source"": [
+ ""from src.load_data import get_epidemic_data""
+ ]
+ },
+ {
+ ""cell_type"": ""code"",
+ ""execution_count"": 3,
+ ""metadata"": {},
+ ""outputs"": [
+ {
+ ""data"": {
+ ""text/html"": [
+ ""\n"",
+ ""\n"",
+ ""
\n"",
+ "" \n"",
+ "" \n"",
+ "" | \n"",
+ "" Total cases | \n"",
+ "" Active infected | \n"",
+ "" Total deaths | \n"",
+ "" Total recovered | \n"",
+ "" Daily cases (avg 7 days) | \n"",
+ "" Daily deaths (avg 7 days) | \n"",
+ ""
\n"",
+ "" \n"",
+ "" \n"",
+ "" \n"",
+ "" | 2020-02-23 | \n"",
+ "" 155.0 | \n"",
+ "" 150.0 | \n"",
+ "" 3.0 | \n"",
+ "" 2.0 | \n"",
+ "" 21.7 | \n"",
+ "" 0.4 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2020-02-24 | \n"",
+ "" 229.0 | \n"",
+ "" 221.0 | \n"",
+ "" 7.0 | \n"",
+ "" 1.0 | \n"",
+ "" 32.3 | \n"",
+ "" 1.0 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2020-02-25 | \n"",
+ "" 322.0 | \n"",
+ "" 311.0 | \n"",
+ "" 10.0 | \n"",
+ "" 1.0 | \n"",
+ "" 45.6 | \n"",
+ "" 1.4 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2020-02-26 | \n"",
+ "" 453.0 | \n"",
+ "" 438.0 | \n"",
+ "" 12.0 | \n"",
+ "" 3.0 | \n"",
+ "" 64.3 | \n"",
+ "" 1.7 | \n"",
+ ""
\n"",
+ "" \n"",
+ "" | 2020-02-27 | \n"",
+ "" 655.0 | \n"",
+ "" 593.0 | \n"",
+ "" 17.0 | \n"",
+ "" 45.0 | \n"",
+ "" 93.1 | \n"",
+ "" 2.4 | \n"",
+ ""
\n"",
+ "" \n"",
+ ""
\n"",
+ ""