A dynamic microsimulation model for epidemics

Fiona Spooner; Jesse F Abrams; Karyn Morrissey; Gavin Shaddick; Michael Batty; Richard Milton; Adam Dennett; Nik Lomax; Nick Malleson; Natalie Nelissen; Alex Coleman; Jamil Nur; Ying Jin; Rory Greig; Charlie Shenton; Mark Birkin

doi:10.1016/j.socscimed.2021.114461

A dynamic microsimulation model for epidemics

Soc Sci Med. 2021 Dec:291:114461. doi: 10.1016/j.socscimed.2021.114461. Epub 2021 Oct 18.

Authors

Fiona Spooner¹, Jesse F Abrams², Karyn Morrissey³, Gavin Shaddick⁴, Michael Batty⁵, Richard Milton⁵, Adam Dennett⁵, Nik Lomax⁶, Nick Malleson⁶, Natalie Nelissen⁶, Alex Coleman⁷, Jamil Nur⁸, Ying Jin⁸, Rory Greig⁹, Charlie Shenton⁹, Mark Birkin¹⁰

Affiliations

¹ Our World in Data at the Global Change Lab, London, UK.
² Institute for Data Science and Artificial Intelligence \& Global Systems Institute, University of Exeter, UK; Joint Centre for Excellence in Environmental Intelligence, Exeter, UK.
³ Department of Technology, Management and Economics, Technical University of Denmark, Lyngby, Denmark.
⁴ Joint Centre for Excellence in Environmental Intelligence, Exeter, UK; Alan Turing Institute, London, UK.
⁵ Bartlett Centre for Advanced Spatial Analysis, University College London, London, UK.
⁶ School of Geography and Leeds Institute for Data Analytics, University of Leeds, Leeds, UK; Alan Turing Institute, London, UK.
⁷ Research Computing, University of Leeds, Leeds, UK.
⁸ Martin Centre for Architectural and Urban Studies, University of Cambridge, 1 Scroope Terrace, Cambridge, UK.
⁹ Improbable, London, UK.
¹⁰ School of Geography and Leeds Institute for Data Analytics, University of Leeds, Leeds, UK; Alan Turing Institute, London, UK. Electronic address: m.h.birkin@leeds.ac.uk.

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.

Keywords: COVID-19; Coronavirus; Dynamics; Microsimulation; SEIR; Spatial-interaction.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

COVID-19*
Communicable Disease Control
Epidemics*
Humans
Policy
SARS-CoV-2