covid19uk: Bayesian stochastic spatial modelling for COVID-19 in the UK

This Python package implements a spatial stochastic SEIR model for COVID-19 in the UK, using Local Authority District level positive test data, population data, and mobility information. Details of the model implementation may be found in doc/lancs_space_model_concept.pdf.

Workflow

This repository contains code that produces Monte Carlo samples of the Bayesian posterior distribution given the model and case timeseries data from coronavirus.data.gov.uk, implementing an ETL step, the model itself, and associated inference and prediction steps.

Users requiring an end-to-end pipeline implementation should refer to the covid-pipeline repository.

COVID-19 Lancaster University data statement

Data contained in the data directory is all publicly available from UK government agencies or previous studies. No personally identifiable information is stored.

ONS: Office for National Statistics

PHE: Public Health England

UTLA: Upper Tier Local Authority

LAD: Local Authority District

Example data files

• data/c2019modagepop.csv a file containing local authority population data in the UK, taken from ONS prediction for December 2019. Local authorities [City of Westminster, City of London] and [Cornwall, Isles of Scilly] have been aggregated to meet commute data processing requirements.
• data/mergedflows.csv inter local authority mobility matrix taken from UK Census 2011 commuting data and aggregated up from Middle Super Output Area level (respecting aggregated LADs as above).
• data/UK2019mod_pop.gpkg a geopackage containing UK Local Authority Districts (2019) polygons together with population and areal metrics.