Estimating the increase in reproduction number associated with the Delta variant using local area dynamics in England

This repository is under active development. See the releases for stable analyses.

This repository contains the data and code for analyses exploring the association between upper-tier local area (UTLA) reproduction number estimates in England and the proportion of COVID-19 tests negative/positive for the S-gene as a proxy for the Alpha/Delta variants. A preprint describing the work in more detail can be found on medRxiv.

Reproducibility

Data

All data used in the analysis can be found in the data folder in rds format. Available data include:

• utla_rt_with_covariates.rds: UTLA level weekly reproduction number estimates combined with estimates of the proportion of tests that were S-gene negative, normalised Google mobility data, and tier status by local authority over time.
• rt_weekly.rds: Summarised weekly UTLA reproduction number estimates using both a short and a long generation time.
• utla_cases.rds: UTLA level COVID-19 test positive cases.
• sgene_by_utla.rds: Weekly test positivity data for the S-gene by UTLA.
• mobility.rds: Normalised Google mobility data stratified by context.
• tiers.rds: UTLA level tier level over time.

Dependencies

The dependencies for this analysis can be installed using (in the working directory of the analysis):

install.packages("devtools")
devtools::install_deps()

Code

Rt estimates from the EpiForecasts web site can be updated using (here and following in the working directory of the analysis):

source(here::here("R/extract_rt.r"))

All publicly available covariates can be re-extracted using:

source(here::here("R/extract_public_data.r"))

All data sources can then be combined into the analysis dataset using:

source(here::here("R/combine_data.r"))

The statistical models considered can be refit using,

source(here::here("R/fit_models.r"))

and compared with

source(here::here("R/compare_models.r"))

The report can be regenerated (once the models have been refit and compared) using:

rmarkdown::render("report.Rmd")

Alternatively all steps can be reproduced using the following bash script:

bash bin/update_analysis.sh

Archived results

Instead of rerunning the code from scratch archived results can be retrieved from the OSF which also contains an archive of the code used for the analysis. Note that this will overwrite any files saved in the output folder.

source(here::here("R/download_output.r"))