Spooner, F, Abrams, JF, Morrissey, K et al. (13 more authors) (2021) A Dynamic Microsimulation Model for Epidemics. Social Science and Medicine, 291. 114461. ISSN 0277-9536
Abstract
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) |
Keywords: | Coronavirus; COVID-19; Microsimulation; SEIR; Spatial-interaction; Dynamics |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Geography (Leeds) > Centre for Spatial Analysis & Policy (Leeds) |
Funding Information: | Funder Grant number ESRC (Economic and Social Research Council) ES/L011891/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 27 Oct 2021 14:31 |
Last Modified: | 25 Jun 2023 22:48 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.socscimed.2021.114461 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:179504 |
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