A dynamic microsimulation model for epidemics.
COVID-19
Coronavirus
Dynamics
Microsimulation
SEIR
Spatial-interaction
Journal
Social science & medicine (1982)
ISSN: 1873-5347
Titre abrégé: Soc Sci Med
Pays: England
ID NLM: 8303205
Informations de publication
Date de publication:
12 2021
12 2021
Historique:
received:
19
05
2021
revised:
25
08
2021
accepted:
05
10
2021
pubmed:
31
10
2021
medline:
21
12
2021
entrez:
30
10
2021
Statut:
ppublish
Résumé
A large evidence base demonstrates that the outcomes of COVID-19 and national and local interventions are not distributed equally across different communities. The need to inform policies and mitigation measures aimed at reducing the spread of COVID-19 highlights the need to understand the complex links between our daily activities and COVID-19 transmission that reflect the characteristics of British society. As a result of a partnership between academic and private sector researchers, we introduce a novel data driven modelling framework together with a computationally efficient approach to running complex simulation models of this type. We demonstrate the power and spatial flexibility of the framework to assess the effects of different interventions in a case study where the effects of the first UK national lockdown are estimated for the county of Devon. Here we find that an earlier lockdown is estimated to result in a lower peak in COVID-19 cases and 47% fewer infections overall during the initial COVID-19 outbreak. The framework we outline here will be crucial in gaining a greater understanding of the effects of policy interventions in different areas and within different populations.
Identifiants
pubmed: 34717286
pii: S0277-9536(21)00793-0
doi: 10.1016/j.socscimed.2021.114461
pmc: PMC8520832
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
114461Informations de copyright
Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.
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