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A dynamic microsimulation model for epidemics.

Published version
Peer-reviewed

Type

Article

Change log

Authors

Spooner, Fiona 
Abrams, Jesse F 
Shaddick, Gavin 
Batty, Michael 

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.

Description

Funder: Aerospace Technology Institute


Funder: UK Research and Innovation


Funder: The Alan Turing Institute

Keywords

COVID-19, Coronavirus, Dynamics, Microsimulation, SEIR, Spatial-interaction, COVID-19, Communicable Disease Control, Epidemics, Humans, Policy, SARS-CoV-2

Journal Title

Soc Sci Med

Conference Name

Journal ISSN

0277-9536
1873-5347

Volume Title

291

Publisher

Elsevier BV
Sponsorship
Engineering and Physical Sciences Research Council (EP/T001569/1, EP/W006022/1)
Economic and Social Research Council (ES/L011891/1)