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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society
2021
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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. |
format | Online Article Text |
id | pubmed-8101016 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | The Royal Society |
record_format | MEDLINE/PubMed |
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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