A COVID‐19 model for local authorities of the United Kingdom

Swapnil Mishra, James A. Scott, Daniel J. Laydon, Harrison Zhu, Neil M. Ferguson, Samir Bhatt, Seth Flaxman and Axel Gandy

Journal of the Royal Statistical Society Series A, 2022, vol. 185, issue S1, S86-S95

Abstract: We propose a new framework to model the COVID‐19 epidemic of the United Kingdom at the local authority level. The model fits within a general framework for semi‐mechanistic Bayesian models of the epidemic based on renewal equations, with some important innovations, including a random walk modelling the reproduction number, incorporating information from different sources, including surveys to estimate the time‐varying proportion of infections that lead to reported cases or deaths, and modelling the underlying infections as latent random variables. The model is designed to be updated daily using publicly available data. We envisage the model to be useful for now‐casting and short‐term projections of the epidemic as well as estimating historical trends. The model fits are available on a public website: https://imperialcollegelondon.github.io/covid19local. The model is currently being used by the Scottish government to inform their interventions.

Date: 2022
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https://doi.org/10.1111/rssa.12988

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