WSS

Weight scale and shift code for covid epidemic modelling.

The model takes CASES from https://coronavirus.data.gov.uk/ . All predictions are based on a compartment model moving people from CASE through hospitalization, ICU, to recovery or death. Compartments are broken down by age region.

R-numbers are calculated by differentiating the case data, after a little data processing to make this legit.
They reliably agree with "official" estimates published from SPI-M, but are available 16 days earlier.

Medium term projections are made by extending the CASE data with R-based prediction.

Parameterization is taken by fits to death/ICU/hospital data over the entire pandemic. Parameters are (slowly) varying with variant and vaccination. Lockdowns etc are not included directly, because their effect is mainly in CASES.

Model description and application is here...

• https://www.medrxiv.org/content/10.1101/2021.04.14.21255385v1.article-info

Previous version, all calculations were spreadsheet-based

• https://www.medrxiv.org/content/10.1101/2021.01.21.21250264v1

Ongoing version for BMJ

• https://docs.google.com/document/d/1t7GydEG2PLbwHOkLVBnbXEByf5_h2OkZ/edit

Analysis article

Execution

The code is in pure R and developed in and best run through Rstudio. There are no external input files, data is read at runtime from internet. If online data is unavailable, code will fail.

Workflow:

1. Run Covid_trimmed.R. This sets up the global parameters for the UK, calculates all R. parameters, generates plots for interactive monitoring, and does the compartment simulation for England, including medium term predictions.
2. Legacy code from the WSS paper calculating CFR variation with time may be called from medrxiv.R and age_pdfplot.R, but these must be called by editing the calls in the code to:

   medout <- MedrxivPaper()

   and setting:

   pdfpo <- TRUE

3. Run ScottishData.R. This reads data from the Scottish government, reformats it, and runs the compartment model. The code ends with some monitoring plots
4. Run CC_write.R. to obtain R-numbers, growth rates, England and Scotland compartment model data in CrystalCast format
5. Run Regional.R. to read data and obtain compartment model data for 7 NHS England Regions in CrystalCast format.
6. Sanity check. R-numbers can be sensitive to late posting on recent cases, and wrong R-numbers tip over into the medium term predictions. Before believing anything, always check the last few days of input case data for anomalies. The parameter "enddate" can be increased to eliminate incomplete data. If R is sensitive to enddate, there is a data problem.

Licensing

The code is licensed under GPL-3.0. The data used for making the calculations is made available under an Open Government Licence.