--- title: "VPD-COVID phase I figures" author: "Katy Gaythorpe" date: "`r format(Sys.Date(), '%d %m %y')`" output: html_document --- ```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE, dev = "png", fig.height = 10, fig.width = 12, fig.path = "figures/", message = FALSE, warning = FALSE) options(dplyr.summarise.inform = FALSE) ``` This report is constructed purely to produce figures for the VPD-COVID phase I modelling work. ```{r load_data} d_pop <- readRDS("d_pop.rds") ``` ```{r deaths_by_year_YF, fig.cap="Deaths per year up to 2030 for Yellow Fever." } figure_maker_burden_per_mod_dis(d_pop, burden_t = "deaths", scenario_pal, year_end = 2030, dis_name = "Yellow fever") ``` ```{r deaths_by_year_Measles, fig.cap="Deaths per year up to 2030 for Measles." } figure_maker_burden_per_mod_dis(d_pop, burden_t = "deaths", scenario_pal, year_end = 2030, dis_name = "Measles") ``` ```{r deaths_by_year_MeningitisA, fig.cap="Deaths per year up to 2030 for Yellow Fever." } figure_maker_burden_per_mod_dis(d_pop, burden_t = "deaths", scenario_pal, year_end = 2030, dis_name = "Meningitis A") ``` ```{r deaths_by_year_pop_YF, fig.cap="Deaths per 100,000 per year up to 2030 for Yellow Fever." } figure_maker_burden_per_mod_dis_pop(d_pop, burden_t = "deaths", scenario_pal, year_end = 2030, dis_name = "Yellow fever") ``` ```{r deaths_by_year_pop_Measles, fig.cap="Deaths per 100,000 per year up to 2030 for Measles." } figure_maker_burden_per_mod_dis_pop(d_pop, burden_t = "deaths", scenario_pal, year_end = 2030, dis_name = "Measles") ``` ```{r deaths_by_year_pop_MeningitisA, fig.cap="Deaths per 100,000 per year up to 2030 for Yellow Fever." } figure_maker_burden_per_mod_dis_pop(d_pop, burden_t = "deaths", scenario_pal, year_end = 2030, dis_name = "Meningitis A") ``` ```{r deaths_by_year, fig.cap="Deaths per year up to 2030 for the model averaged predictions for (A) Measles, (B) Meningitis A and (C) Yellow Fever. Dotted lines indicate each modelling group whereas the solid line represents the mean.", fig.width=16 } figure_maker_burden(d_pop, burden_t = "deaths", scenario_pal) ``` ```{r dalys_by_year, fig.cap="DALYs per year up to 2030 for the model averaged predictions for (A) Measles, (B) Meningitis A and (C) Yellow Fever. Dotted lines indicate each modelling group whereas the solid line represents the mean.", fig.width=16 } figure_maker_burden(d_pop, burden_t = "dalys", scenario_pal) ``` ```{r deaths_by_year_ribbon, fig.cap="Deaths per year up to 2030 for the model averaged predictions for (A) Measles, (B) Meningitis A and (C) Yellow Fever. Grey ribbon represents the envelope of all model predictions - this should not be interpreted as uncertainty.", fig.width=16 } figure_maker_burden_ribbon(d_pop, burden_t = "deaths", scenario_pal) ``` ```{r dalys_by_year_ribbon, fig.cap="DALYs per year up to 2030 for the model averaged predictions for (A) Measles, (B) Meningitis A and (C) Yellow Fever. Grey ribbon represents the envelope of all model predictions - this should not be interpreted as uncertainty.", fig.width=16 } figure_maker_burden_ribbon(d_pop, burden_t = "dalys", scenario_pal) ``` ```{r deaths_by_year_tab} d_burden <- d_pop %>% filter(burden_outcome == "deaths") %>% filter(year %in% c(2020, 2030)) %>% group_by(country, year, modelling_group, simple_scenario, disease) %>% summarise(value = sum(focal_burden, na.rm = TRUE)) %>% bind_rows(d %>% filter(burden_outcome == "deaths", simple_scenario == "Postpone 2020 SIAs -> 2021", year %in% c(2020, 2030)) %>% group_by(country, year, disease, modelling_group) %>% summarise(value = sum(baseline_burden, na.rm = TRUE)) %>% mutate(simple_scenario = "BAU")) d_burden %>% filter(country == "NGA") %>% arrange(year, disease) %>% mutate(value = round(value)) %>% pivot_wider(names_from = simple_scenario, values_from = value) %>% flextable::flextable(cwidth = 1.5) ``` ```{r deaths_per_pop_by_year, fig.cap="Deaths per 100,000 per year up to 2030 for the model averaged predictions for Measles, Meningitis A and Yellow Fever. Dotted lines indicate each modelling group whereas the solid line represents the mean." } figure_maker_burden_pop(d_pop, burden_t = "deaths", scenario_pal) ``` ```{r dalys_per_pop_by_year, fig.cap="DALYs per 100,000 per year up to 2030 for the model averaged predictions for Measles, Meningitis A and Yellow Fever. Dotted lines indicate each modelling group whereas the solid line represents the mean." } figure_maker_burden_pop(d_pop, burden_t = "dalys", scenario_pal) ``` ```{r deaths_per_pop_by_year_ribbon, fig.cap="Deaths per 100,000 per year up to 2030 for the model averaged predictions for Measles, Meningitis A and Yellow Fever. Grey ribbon represents the envelope of all model predictions - this should not be interpreted as uncertainty." } figure_maker_burden_per_pop_ribbon(d_pop, burden_t = "deaths", scenario_pal) ``` ```{r dalys_per_pop_by_year_ribbon, fig.cap="DALYs per 100,000 per year up to 2030 for the model averaged predictions for Measles, Meningitis A and Yellow Fever. Grey ribbon represents the envelope of all model predictions - this should not be interpreted as uncertainty." } figure_maker_burden_per_pop_ribbon(d_pop, burden_t = "dalys", scenario_pal) ``` # Effect by country ```{r excess_deaths_by_country, message=FALSE, fig.cap="Excess deaths per year from 2020 to 2030 for the model averaged predictions for Measles, Meningitis A and Yellow Fever by country. The error bars range from max to min group preditions."} figure_maker_excess_country(d_pop, "deaths", scenario_pal) ``` ```{r excess_dalys_by_country, message=FALSE, fig.cap="Excess DALYs per year from 2020 to 2030 for the model averaged predictions for Measles, Meningitis A and Yellow Fever by country. The error bars range from max to min group preditions."} figure_maker_excess_country(d_pop, "dalys", scenario_pal) ``` ```{r excess_deaths_per_pop_by_country, message=FALSE, fig.cap="Excess deaths per 100,000 population per year from 2020 to 2030 for the model averaged predictions for Measles, Meningitis A and Yellow Fever by country.The error bars range from max to min group preditions."} figure_maker_excess_country_pop(d_pop, "deaths", scenario_pal) ``` ```{r excess_dalys_per_pop_by_country, message=FALSE, fig.cap="Excess DALYs per 100,000 population per year from 2020 to 2030 for the model averaged predictions for Measles, Meningitis A and Yellow Fever by country.The error bars range from max to min group preditions."} figure_maker_excess_country_pop(d_pop, "dalys", scenario_pal) ``` # Normalised impact ```{r norm_deaths, message=FALSE, fig.cap="Normalised excess deaths from 2020 to 2030 for the model averaged predictions for Measles, Meningitis A and Yellow Fever."} figure_maker_norm(d_pop, "deaths", scenario_pal) ``` ```{r norm_deaths_per_country, message=FALSE, fig.cap="Normalised excess deaths from 2020 to 2030 for the model averaged predictions for Measles, Meningitis A and Yellow Fever per country"} figure_maker_norm_per_country(d_pop, "deaths", scenario_pal) ``` ```{r norm_dalys, message=FALSE, fig.cap="Normalised excess DALYs from 2020 to 2030 for the model averaged predictions for Measles, Meningitis A and Yellow Fever."} figure_maker_norm(d_pop, "dalys", scenario_pal) ``` ```{r norm_dalys_per_country, message=FALSE, fig.cap="Normalised excess DALYs from 2020 to 2030 for the model averaged predictions for Measles, Meningitis A and Yellow Fever per country"} figure_maker_norm_per_country(d_pop, "dalys", scenario_pal) ``` ```{r norm_deaths_2100, message=FALSE, fig.cap="Normalised excess deaths from 2020 to 2100 for the model averaged predictions for Measles, Meningitis A and Yellow Fever."} figure_maker_norm(d_pop, "deaths", scenario_pal, year_end = 2100) ``` ```{r norm_deaths_2100_per_country, message=FALSE, fig.cap="Normalised excess deaths from 2020 to 2100 for the model averaged predictions for Measles, Meningitis A and Yellow Fever per country"} figure_maker_norm_per_country(d_pop, "deaths", scenario_pal, year_end = 2100) ``` ```{r norm_dalys_2100, message=FALSE, fig.cap="Normalised excess DALYs from 2020 to 2100 for the model averaged predictions for Measles, Meningitis A and Yellow Fever."} figure_maker_norm(d_pop, "dalys", scenario_pal, year_end = 2100) ``` ```{r norm_dalys_2100_per_country, message=FALSE, fig.cap="Normalised excess DALYs from 2020 to 2100 for the model averaged predictions for Measles, Meningitis A and Yellow Fever per country"} figure_maker_norm_per_country(d_pop, "dalys", scenario_pal, year_end = 2100) ``` # Adding under 5 burden ```{r under_5s_deaths} d_pop %>% filter(year<=2030) %>% filter(grepl("deaths", burden_outcome)) %>% mutate(under5 = ifelse(grepl("under5", burden_outcome), "<5", "All ages")) %>% group_by(modelling_group_tidy, simple_scenario, disease, under5) %>% summarise(Excess = -sum(impact, na.rm = TRUE)) %>% mutate(Excess = round(Excess)) %>% mutate(disease = factor(disease, levels = rev(c("Measles", "Meningitis A", "Yellow fever")))) %>% mutate(mod_grp_dis = paste0(disease, ", ",modelling_group_tidy)) %>% ggplot()+ geom_col(position = "dodge", color = "black")+ aes(x = mod_grp_dis, y = Excess, fill = under5)+ coord_flip()+ facet_wrap(simple_scenario~., nrow = 2, scales = "free_y")+ theme_minimal()+ scale_fill_manual(values = c("black", "white"))+ labs(y = "Excess deaths", x = "", fill = "Age" ) ```