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Modelling COVID-19 contagion: risk assessment and targeted mitigation policies

We use a spatial epidemic model with demographic and geographical heterogeneity to study the regional dynamics of COVID-19 across 133 regions in England. Our model emphasizes the role of variability of regional outcomes and heterogeneity across age groups and geographical locations, and provides a f...

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Detalles Bibliográficos
Autores principales: Cont, Rama, Kotlicki, Artur, Xu, Renyuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8101016/
https://www.ncbi.nlm.nih.gov/pubmed/34035936
http://dx.doi.org/10.1098/rsos.201535
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author Cont, Rama
Kotlicki, Artur
Xu, Renyuan
author_facet Cont, Rama
Kotlicki, Artur
Xu, Renyuan
author_sort Cont, Rama
collection PubMed
description We use a spatial epidemic model with demographic and geographical heterogeneity to study the regional dynamics of COVID-19 across 133 regions in England. Our model emphasizes the role of variability of regional outcomes and heterogeneity across age groups and geographical locations, and provides a framework for assessing the impact of policies targeted towards subpopulations or regions. We define a concept of efficiency for comparative analysis of epidemic control policies and show targeted mitigation policies based on local monitoring to be more efficient than country-level or non-targeted measures. In particular, our results emphasize the importance of shielding vulnerable subpopulations and show that targeted policies based on local monitoring can considerably lower fatality forecasts and, in many cases, prevent the emergence of second waves which may occur under centralized policies.
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spelling pubmed-81010162021-05-24 Modelling COVID-19 contagion: risk assessment and targeted mitigation policies Cont, Rama Kotlicki, Artur Xu, Renyuan R Soc Open Sci Mathematics We use a spatial epidemic model with demographic and geographical heterogeneity to study the regional dynamics of COVID-19 across 133 regions in England. Our model emphasizes the role of variability of regional outcomes and heterogeneity across age groups and geographical locations, and provides a framework for assessing the impact of policies targeted towards subpopulations or regions. We define a concept of efficiency for comparative analysis of epidemic control policies and show targeted mitigation policies based on local monitoring to be more efficient than country-level or non-targeted measures. In particular, our results emphasize the importance of shielding vulnerable subpopulations and show that targeted policies based on local monitoring can considerably lower fatality forecasts and, in many cases, prevent the emergence of second waves which may occur under centralized policies. The Royal Society 2021-03-31 /pmc/articles/PMC8101016/ /pubmed/34035936 http://dx.doi.org/10.1098/rsos.201535 Text en © 2021 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited.
spellingShingle Mathematics
Cont, Rama
Kotlicki, Artur
Xu, Renyuan
Modelling COVID-19 contagion: risk assessment and targeted mitigation policies
title Modelling COVID-19 contagion: risk assessment and targeted mitigation policies
title_full Modelling COVID-19 contagion: risk assessment and targeted mitigation policies
title_fullStr Modelling COVID-19 contagion: risk assessment and targeted mitigation policies
title_full_unstemmed Modelling COVID-19 contagion: risk assessment and targeted mitigation policies
title_short Modelling COVID-19 contagion: risk assessment and targeted mitigation policies
title_sort modelling covid-19 contagion: risk assessment and targeted mitigation policies
topic Mathematics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8101016/
https://www.ncbi.nlm.nih.gov/pubmed/34035936
http://dx.doi.org/10.1098/rsos.201535
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