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

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...

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Detalles Bibliográficos
Autores principales: Mishra, Swapnil, Scott, James A., Laydon, Daniel J., Zhu, Harrison, Ferguson, Neil M., Bhatt, Samir, Flaxman, Seth, Gandy, Axel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9877769/
http://dx.doi.org/10.1111/rssa.12988
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author Mishra, Swapnil
Scott, James A.
Laydon, Daniel J.
Zhu, Harrison
Ferguson, Neil M.
Bhatt, Samir
Flaxman, Seth
Gandy, Axel
author_facet Mishra, Swapnil
Scott, James A.
Laydon, Daniel J.
Zhu, Harrison
Ferguson, Neil M.
Bhatt, Samir
Flaxman, Seth
Gandy, Axel
author_sort Mishra, Swapnil
collection PubMed
description 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.
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spelling pubmed-98777692023-01-26 A COVID‐19 model for local authorities of the United Kingdom Mishra, Swapnil Scott, James A. Laydon, Daniel J. Zhu, Harrison Ferguson, Neil M. Bhatt, Samir Flaxman, Seth Gandy, Axel J R Stat Soc Ser A Stat Soc Supplement Articles 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. John Wiley and Sons Inc. 2022-12-13 2022-11 /pmc/articles/PMC9877769/ http://dx.doi.org/10.1111/rssa.12988 Text en © 2022 The Authors. Journal of the Royal Statistical Society: Series A (Statistics in Society) published by John Wiley & Sons Ltd on behalf of Royal Statistical Society. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Supplement Articles
Mishra, Swapnil
Scott, James A.
Laydon, Daniel J.
Zhu, Harrison
Ferguson, Neil M.
Bhatt, Samir
Flaxman, Seth
Gandy, Axel
A COVID‐19 model for local authorities of the United Kingdom
title A COVID‐19 model for local authorities of the United Kingdom
title_full A COVID‐19 model for local authorities of the United Kingdom
title_fullStr A COVID‐19 model for local authorities of the United Kingdom
title_full_unstemmed A COVID‐19 model for local authorities of the United Kingdom
title_short A COVID‐19 model for local authorities of the United Kingdom
title_sort covid‐19 model for local authorities of the united kingdom
topic Supplement Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9877769/
http://dx.doi.org/10.1111/rssa.12988
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