https://github.com/epiforecasts/covid19.sgene.utla.rt
Local area reproduction numbers and S-gene target failure.
https://github.com/epiforecasts/covid19.sgene.utla.rt
covid-19 open-science reproduction-number
Last synced: 12 months ago
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Local area reproduction numbers and S-gene target failure.
- Host: GitHub
- URL: https://github.com/epiforecasts/covid19.sgene.utla.rt
- Owner: epiforecasts
- License: other
- Created: 2021-01-04T15:20:15.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2021-12-11T00:34:18.000Z (over 4 years ago)
- Last Synced: 2025-06-14T13:06:26.472Z (12 months ago)
- Topics: covid-19, open-science, reproduction-number
- Language: R
- Homepage: https://github.com/epiforecasts/covid19.sgene.utla.rt/blob/main/report.pdf
- Size: 64.2 MB
- Stars: 7
- Watchers: 6
- Forks: 3
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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](https://github.com/epiforecasts/covid19.sgene.utla.rt/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](https://www.medrxiv.org/content/10.1101/2021.11.30.21267056v1.full).
## 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):
```r
install.packages("devtools")
devtools::install_deps()
```
### Code
Rt estimates from the [EpiForecasts](http://epiforecasts.io/covid) web site can be updated using (here and following in the working directory of the analysis):
```r
source(here::here("R/extract_rt.r"))
```
All publicly available covariates can be re-extracted using:
```r
source(here::here("R/extract_public_data.r"))
```
All data sources can then be combined into the analysis dataset using:
```r
source(here::here("R/combine_data.r"))
```
The statistical models considered can be refit using,
```r
source(here::here("R/fit_models.r"))
```
and compared with
```r
source(here::here("R/compare_models.r"))
```
The report can be regenerated (once the models have been refit and compared) using:
```r
rmarkdown::render("report.Rmd")
```
Alternatively all steps can be reproduced using the following bash script:
```bash
bash bin/update_analysis.sh
```
### Archived results
Instead of rerunning the code from scratch archived results can be retrieved from the [OSF](https://osf.io/h6e8n/) which also contains an archive of the code used for the analysis. Note that this will overwrite any files saved in the `output` folder.
```r
source(here::here("R/download_output.r"))
```