VariantAnimation-UK.Rmd

---
title: "Animation of projected weekly cases - United Kingdom"
output:
  html_document:
    df_print: paged
    code_folding: hide
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  gganimate = list(
    nframes = 0
  ),
  out.width = "100%"
)
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE, fig.dim = c(12, 5))
```



```{r}
suppressPackageStartupMessages({
  library(covmuller)
  library(COVID19)
  library(tidyverse)
})
theme_set(CovmullerTheme())
```

# Get variants data for United Kingdom

```{r, warning=FALSE, message=FALSE}
gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_04_11.qs")
gisaid_United_Kingdom <- gisaid_metadata %>%
  filter(Country == "United Kingdom") %>%
  filter(Host == "Human")
# format metadata
gisaid_United_Kingdom <- FormatGISAIDMetadata(gisaid_United_Kingdom)
gisaid_United_Kingdom <- gisaid_United_Kingdom %>%
  arrange(State, MonthYearCollected) %>%
  filter(pangolin_lineage != "Unknown")

vocs <- GetVOCs()
custom_voc_mapping <- list(
  `JN.1` = "JN.1",
  `JN.1.*` = "JN.1",
  `HV.1` = "HV.1",
  `HV.1.*` = "HV.1",
  `B.1` = "B.1",
  `B.1.1.306` = "B.1",
  `B.1.1.306.*` = "B.1",
  `B.1.1.326` = "B.1",
  `B.1.36.29` = "B.1",
  `B.1.560` = "B.1",
  `B.1.1` = "B.1",
  `B.1.210` = "B.1",
  `B.1.36.8` = "B.1",
  `B.1.36` = "B.1",
  `B.1.36.*` = "B.1"
)
gisaid_United_Kingdom <- gisaid_United_Kingdom %>% filter(pangolin_lineage != "None")

gisaid_United_Kingdom <- CollapseLineageToVOCs(
  variant_df = gisaid_United_Kingdom,
  vocs = vocs,
  custom_voc_mapping = custom_voc_mapping,
  summarize = FALSE
)
```

# Get weekly cases for United Kingdom

```{r, warning=FALSE, message=FALSE}
GetCases <- function() {
  data <- read.csv("https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/cases_deaths/new_cases.csv")
  confirmed <- data %>% select(date, United.Kingdom)
  colnames(confirmed)[2] <- c("cases")
  confirmed$MonthYear <- GetMonthYear(confirmed$date)
  confirmed$WeekYear <- tsibble::yearweek(confirmed$date)
  return(confirmed)
}


GetCasesLong <- function() {
  data <- read.csv("https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/cases_deaths/new_cases.csv")
  confirmed <- data %>% select(date, United.Kingdom)
  colnames(confirmed)[2] <- c("cases")
  confirmed$cases[is.na(confirmed$cases)] <- 0
  confirmed$MonthYear <- GetMonthYear(confirmed$date)
  confirmed$WeekYear <- tsibble::yearweek(confirmed$date)
  confirmed_subset_weekwise <- confirmed %>%
    group_by(WeekYear) %>%
    summarise(cases = mean(cases, na.rm = T)) %>%
    arrange(WeekYear)
  confirmed_subset_weekwise$cases <- ceiling(confirmed_subset_weekwise$cases)
  confirmed_subset_dateweekwise_long_india <- confirmed_subset_weekwise %>%
    rename(n = cases) %>%
    rename(WeekYearCollected = WeekYear)
}


confirmed <- GetCases()
confirmed_subset_dateweekwise_long <- GetCasesLong()
gisaid_United_Kingdom_weekwise <- SummarizeVariantsWeekwise(gisaid_United_Kingdom)
```


# Distribution of variants



```{r, fig.width=12, fig.height=5}
state_month_counts <- SummarizeVariantsMonthwise(gisaid_United_Kingdom)
state_month_counts$State <- "United Kingdom"
state_month_prevalence <- CountsToPrevalence(state_month_counts)
vocs <- GetVOCs()

state_month_prevalence <- CollapseLineageToVOCs(
  variant_df = state_month_prevalence,
  vocs = vocs,
  custom_voc_mapping = custom_voc_mapping, summarize = FALSE
)

p5 <- StackedBarPlotPrevalence(state_month_prevalence)
p5
```


# Project weekly cases to variant prevalence data from GISAID

```{r, warning=FALSE, message=FALSE}
voc_to_keep <- gisaid_United_Kingdom_weekwise %>%
  group_by(lineage_collapsed) %>%
  summarise(n_sum = sum(n)) %>%
  filter(n_sum > 50) %>%
  pull(lineage_collapsed) %>%
  unique()
gisaid_United_Kingdom_weekwise <- gisaid_United_Kingdom_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)

United_Kingdom_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_United_Kingdom_weekwise, confirmed_subset_dateweekwise_long)
the_anim <- PlotVariantPrevalenceAnimated(United_Kingdom_cases_pred_prob_sel_long, title = "Estimated cases (weekly average) in United Kingdom by variant", caption = "**Source: gisaid.org and ourworldindata.org/coronavirus**<br>", date_breaks = "120 days")
gganimate::anim_save(filename = here::here("docs/articles/United_Kingdom_animated.gif"), animation = the_anim)
```

![](United_Kingdom_animated.gif)

Look at cases from 2022,

```{r}
confirmed_subset_dateweekwise_long <- GetCasesLong() %>%
  filter(WeekYearCollected >= tsibble::yearweek("2021 W35"))

gisaid_United_Kingdom_subset <- gisaid_United_Kingdom %>% filter(MonthYearCollected > "Oct 2021")
gisaid_United_Kingdom_weekwise <- SummarizeVariantsWeekwise(gisaid_United_Kingdom_subset)

voc_to_keep <- gisaid_United_Kingdom_weekwise %>%
  group_by(lineage_collapsed) %>%
  summarise(n_sum = sum(n)) %>%
  filter(n_sum > 50) %>%
  pull(lineage_collapsed) %>%
  unique()
gisaid_United_Kingdom_weekwise <- gisaid_United_Kingdom_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)

United_Kingdom_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_United_Kingdom_weekwise, confirmed_subset_dateweekwise_long)
the_anim <- PlotVariantPrevalenceAnimated(United_Kingdom_cases_pred_prob_sel_long, title = "Estimated cases (weekly average) in United Kingdom by variant", caption = "**Source: gisaid.org and ourworldindata.org/coronavirus**<br>", date_breaks = "90 days")
gganimate::anim_save(filename = here::here("docs/articles/United_Kingdom_animated_2021.gif"), animation = the_anim)
```

![](United_Kingdom_animated_2021.gif)
Look at cases from 2023

```{r}
confirmed_subset_dateweekwise_long <- GetCasesLong() %>%
  filter(WeekYearCollected >= tsibble::yearweek("2022 W35"))

gisaid_United_Kingdom_subset <- gisaid_United_Kingdom %>% filter(MonthYearCollected > "October 2022")
gisaid_United_Kingdom_weekwise <- SummarizeVariantsWeekwise(gisaid_United_Kingdom_subset)

voc_to_keep <- gisaid_United_Kingdom_weekwise %>%
  group_by(lineage_collapsed) %>%
  summarise(n_sum = sum(n)) %>%
  filter(n_sum > 50) %>%
  pull(lineage_collapsed) %>%
  unique()
gisaid_United_Kingdom_weekwise <- gisaid_United_Kingdom_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)

United_Kingdom_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_United_Kingdom_weekwise, confirmed_subset_dateweekwise_long)
the_anim <- PlotVariantPrevalenceAnimated(United_Kingdom_cases_pred_prob_sel_long, title = "Estimated cases (weekly average) in United Kingdom by variant", caption = "**Source: gisaid.org and ourworldindata.org/coronavirus**<br>", date_breaks = "90 days")
gganimate::anim_save(filename = here::here("docs/articles/United_Kingdom_animated_2023.gif"), animation = the_anim)
```

![](United_Kingdom_animated_2023.gif)



Look at cases in the past few weeks

```{r}
confirmed_subset_dateweekwise_long <- GetCasesLong() %>%
  filter(WeekYearCollected >= tsibble::yearweek("2023 W23"))

gisaid_United_Kingdom_subset <- gisaid_United_Kingdom %>% filter(MonthYearCollected > "June 2023")
gisaid_United_Kingdom_weekwise <- SummarizeVariantsWeekwise(gisaid_United_Kingdom_subset)

voc_to_keep <- gisaid_United_Kingdom_weekwise %>%
  group_by(lineage_collapsed) %>%
  summarise(n_sum = sum(n)) %>%
  filter(n_sum > 50) %>%
  pull(lineage_collapsed) %>%
  unique()
gisaid_United_Kingdom_weekwise <- gisaid_United_Kingdom_weekwise %>% filter(lineage_collapsed %in% voc_to_keep)

United_Kingdom_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_United_Kingdom_weekwise, confirmed_subset_dateweekwise_long)
the_anim <- PlotVariantPrevalenceAnimated(United_Kingdom_cases_pred_prob_sel_long, title = "Estimated cases (weekly average) in United Kingdom by variant", caption = "**Source: gisaid.org and ourworldindata.org/coronavirus**<br>")
gganimate::anim_save(filename = here::here("docs/articles/United_Kingdom_animated_2024.gif"), animation = the_anim)
```
![](United_Kingdom_animated_2024.gif)