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A dynamic microsimulation model for epidemics

Author

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

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.

Suggested Citation

  • Spooner, Fiona & Abrams, Jesse F. & Morrissey, Karyn & Shaddick, Gavin & Batty, Michael & Milton, Richard & Dennett, Adam & Lomax, Nik & Malleson, Nick & Nelissen, Natalie & Coleman, Alex & Nur, Jamil, 2021. "A dynamic microsimulation model for epidemics," Social Science & Medicine, Elsevier, vol. 291(C).
    • Handle: RePEc:eee:socmed:v:291:y:2021:i:c:s0277953621007930
    DOI: 10.1016/j.socscimed.2021.114461
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    References listed on IDEAS

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    5. Michael Batty & Richard Milton, 2021. "A new framework for very large-scale urban modelling," Urban Studies, Urban Studies Journal Limited, vol. 58(15), pages 3071-3094, November.
    6. Alberto Aleta & David Martín-Corral & Ana Pastore y Piontti & Marco Ajelli & Maria Litvinova & Matteo Chinazzi & Natalie E. Dean & M. Elizabeth Halloran & Ira M. Longini Jr & Stefano Merler & Alex Pen, 2020. "Modelling the impact of testing, contact tracing and household quarantine on second waves of COVID-19," Nature Human Behaviour, Nature, vol. 4(9), pages 964-971, September.
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    1. Rosalind Wallace & Rachel Franklin & Susan Grant-Muller & Alison Heppenstall & Victoria Houlden, 2022. "Estimating the social and spatial impacts of Covid mitigation strategies in United Kingdom regions: synthetic data and dashboards [Developing a sustainable exit strategy for COVID-19: health, economic and public policy implications]," Cambridge Journal of Regions, Economy and Society, Cambridge Political Economy Society, vol. 15(3), pages 683-702.
    2. repec:osf:socarx:2duk5_v1 is not listed on IDEAS
    3. Nick Malleson & Rachel Franklin & Daniel Arribas-Bel & Tao Cheng & Mark Birkin, 2024. "Digital twins on trial: Can they actually solve wicked societal problems and change the world for better?," Environment and Planning B, , vol. 51(6), pages 1181-1186, July.
    4. Hussein Mahfouz & Sam F. Greenbury & Bowen Zhang & Stuart Lynn & Tao Cheng, 2025. "A reproducible pipeline for activity-based travel demand generation in England," Environment and Planning B, , vol. 52(9), pages 2326-2339, November.
    5. Yue Lin, 2024. "Synthetic population data for small area estimation in the United States," Environment and Planning B, , vol. 51(2), pages 553-562, February.
    6. Thompson, Jason & McClure, Roderick & Scott, Nick & Hellard, Margaret & Abeysuriya, Romesh & Vidinaarachichi, Rajith & Thwaites, John & Lazarus, Jeffrey & Michie, Susan & Bullen, Chris, 2021. "A framework for communicating the utility of models when facing tough decisions in public health," SocArXiv 2duk5, Center for Open Science.

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