knitr::opts_chunk$set( collapse = TRUE, comment = "#>", gganimate = list( nframes = 0 ), out.width = "100%" ) knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE)
suppressPackageStartupMessages({ library(covmuller) library(COVID19) library(tidyverse) }) theme_set(CovmullerTheme())
gisaid_metadata <- qs::qread("~/data/epicov/metadata_tsv_2024_04_11.qs") gisaid_usa <- gisaid_metadata %>% filter(Country == "USA") %>% filter(Host == "Human") # format metadata gisaid_usa <- FormatGISAIDMetadata(gisaid_usa) gisaid_usa <- gisaid_usa %>% arrange(State, MonthYearCollected) %>% filter(pangolin_lineage != "Unknown") gisaid_NY <- gisaid_usa %>% filter(State == "New York") 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_NY <- gisaid_NY %>% filter(pangolin_lineage != "None") gisaid_NY <- CollapseLineageToVOCs( variant_df = gisaid_NY, vocs = vocs, custom_voc_mapping = custom_voc_mapping, summarize = FALSE )
confirmed <- covid19(country = "USA", level = 2) %>% filter(administrative_area_level_2 == "New York") %>% select(date, confirmed) %>% filter(!is.na(confirmed)) confirmed$daily_cases <- c(confirmed$confirmed[1], diff(confirmed$confirmed)) confirmed$WeekYear <- tsibble::yearweek(confirmed$date) confirmed$MonthYear <- GetMonthYear(confirmed$date) confirmed_subset_dateweekwise_long <- confirmed %>% group_by(WeekYear) %>% summarise(n = ceiling(mean(daily_cases, na.rm = T))) %>% arrange(WeekYear) %>% rename(WeekYearCollected = WeekYear) gisaid_NY_weekwise <- SummarizeVariantsWeekwise(gisaid_NY)
state_month_counts <- SummarizeVariantsMonthwise(gisaid_NY) state_month_counts$State <- "New York" state_month_prevalence <- CountsToPrevalence(state_month_counts) 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
voc_to_keep <- gisaid_NY_weekwise %>% group_by(lineage_collapsed) %>% summarise(n_sum = sum(n)) %>% filter(n_sum > 50) %>% pull(lineage_collapsed) %>% unique() gisaid_NY_weekwise <- gisaid_NY_weekwise %>% filter(lineage_collapsed %in% voc_to_keep) newyork_cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_NY_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(newyork_cases_pred_prob_sel_long, title = "Estimated cases (weekly average) in New York state by variant", caption = "**Source: gisaid.org and covid19nytimes**<br>", date_breaks = "28 days") gganimate::anim_save(filename = here::here("docs/articles/NY_animated.gif"), animation = the_anim)
Look at cases after february, 2022 only:
confirmed$MonthYear <- GetMonthYear(confirmed$date) confirmed_subset_dateweekwise_long <- confirmed %>% filter(MonthYear > "December 2022") %>% group_by(WeekYear) %>% summarise(n = sum(daily_cases)) %>% arrange(WeekYear) %>% rename(WeekYearCollected = WeekYear) gisaid_NY <- gisaid_NY %>% filter(MonthYearCollected > "December 2022") gisaid_weekwise <- SummarizeVariantsWeekwise(gisaid_NY) voc_to_keep <- gisaid_weekwise %>% group_by(lineage_collapsed) %>% summarise(n_sum = sum(n)) %>% filter(n_sum > 50) %>% pull(lineage_collapsed) %>% unique() gisaid_weekwise <- gisaid_weekwise %>% filter(lineage_collapsed %in% voc_to_keep) cases_pred_prob_sel_long <- FitMultinomWeekly(gisaid_weekwise, confirmed_subset_dateweekwise_long) the_anim <- PlotVariantPrevalenceAnimated(cases_pred_prob_sel_long, title = "Estimated cases (weekly average) in New York state by variant", caption = "**Source: gisaid.org and covid19nytimes**<br>") gganimate::anim_save(filename = here::here("docs/articles/NY_animated_2023.gif"), animation = the_anim)
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