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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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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. |
format | Online Article Text |
id | pubmed-9877769 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
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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