A dynamic microsimulation model for epidemics

Spooner, Fiona, Abrams, Jesse F., Morrissey, Karyn, Shaddick, Gavin ORCID: https://orcid.org/0000-0002-4117-4264, Batty, Michael, Milton, Richard, Dennett, Adam, Lomax, Nik, Malleson, Nick, Nelissen, Natalie, Coleman, Alex, Nur, Jamil, Jin, Ying, Greig, Rory, Shenton, Charlie and Birkin, Mark 2021. A dynamic microsimulation model for epidemics. Social Science & Medicine 291 , 114461. 10.1016/j.socscimed.2021.114461

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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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	?? VCO ??
Publisher:	Elsevier
ISSN:	0277-9536
Date of Acceptance:	5 October 2021
Last Modified:	30 Jul 2024 14:46
URI:	https://orca.cardiff.ac.uk/id/eprint/170784

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