---
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" )
```