README.Rmd

---
output: github_document
---

```{r opts, echo = FALSE}
knitr::opts_chunk$set(
  fig.path = "images/"
)
```



# Prevelence of SARS-CoV-2 Variants of Concern in Aoteoroa New Zealand

The `data/` directory contains a single file,  `all_variant_counts.csv`. This file contains 
the 7-day rolling average for the number of genomes from a given variant sequenced by 
ESR. Data in the most recent weeks will be updated as new cases reported in this time 
are added to the sequencing dataset  Data begins at the start of the August 2021 Delta outbreak. 

It should be noted that the cases contributing to this data are not a random or representative
sample of all cases. Information on the demographic samplig of cases is provided in
the June 2023 Covid Genomics Insight report [pdf](https://www.esr.cri.nz/assets/HEALTH-CONTENT/COVID-Genomics-Insights-Dashboard-CGID/CGID_38_Report.pdf).

Changes to the set of lineages tracked in this repository are described in `changes.md`

In addition the `data/archived` subdirectory contians no-longer updated files


* `rbd_levels.csv`, which  includes information on the number of key
mutations observed in the receptor binding domain of the spike protein. Higher values
of this measure are [associated with increased immune evasiveness](https://virological.org/t/sars-cov-2-evolution-post-omicron/911). This data was
part of ESR's reporting in late 2022 but is no longer useful.


* `community_variant_counts.csv` and  `border_variant_counts.csv` break down the 
lineage data into those cases associated with community-acquired infections and 
border-associated infections respectively. Border and community cases can no
longer be reliably differentiated, so this data is no longer updated.


* `May_23_variant_classes.csv`, which includes variant classified following 
reporting criteria used prior to May 2023 (inducing BQ.1.1, XBF and XBC which 
are no longer tracked separately)

## Example

A brief example of how the data might be used, using the R language.

## Since Delta

```{r plot, message=FALSE, warning=FALSE}
library(lubridate)
library(tidyr)
library(ggplot2)
library(forcats)

voc <- read.csv("data/all_variant_counts.csv")
voc$report_date <- ymd(voc$report_date)
tidy_voc <- pivot_longer(voc, !report_date, names_to = "variant", values_to = "n_genomes")

stack_order <- c("Delta", 
                 "BA.1", 
                 "BA.2", 
                 "BA.5", 
                 "BA.4", 
                 "BA.2.75", 
                 "CH.1.1", 
                 "FK.1.1",
                 "XBB", 
                 "XBB.1.5", 
                 "XBB.1.16",
                 "EG.5",
                 "HK.3",
                 "BA.2.86",
                 "JN.1",
                 "JN.1.4",
                 "Recombinant",
                 "XBC"
)

tidy_voc$variant <- fct_relevel(tidy_voc$variant, stack_order)




voc_pal <-c(Delta        = "#3182bd", 
            BA.1         = "#bd0026", 
            BA.2         = "#fd8d3c", 
            BA.4         = "#B14B02",
            BA.5         = "#FD3A6B",
            BA.2.75      = "#fecc5c", 
            CH.1.1       = "#bd0026",
            FK.1.1       = "#E81634",
            XBB          = "#b6db3a",
            XBB.1.5      = "#5CCC34", 
            XBB.1.16     = "#9CC33C",
            EG.5         = "#599945",
            HK.3         = "#3e5836",
            BA.2.86      = "#B399D4",
            JN.1         = "#6f4f97",
            JN.1.4         = "#422f5a",
            Recombinant  = "#57badb", 
            XBC          = "#9AD6EA"
            
)



ggplot(tidy_voc, aes(report_date, n_genomes, fill=variant)) + 
    geom_area(position = 'fill', colour='black', size=0.3) +
    scale_fill_manual(values=voc_pal)+
    theme_bw(base_size = 16)  

```



## Recent


```{r, recent_plot}
library(lubridate)
recent <-  filter(tidy_voc, report_date >= today() - months(3), n_genomes > 0)
recent_pal <- voc_pal[ as.character(unique(recent$variant)) ]
ggplot(recent, aes(report_date, n_genomes, fill=variant)) + 
    geom_area(position = 'fill', colour='black', size=0.3) +
    scale_fill_manual(values=recent_pal)+
    theme_bw(base_size = 16)
```

## License 

The data is released under a [CC-BY 4.0 international license](https://creativecommons.org/licenses/by/4.0/). You are free to copy, distribute or adapt this data as long as you acknowledge ESR.