A dynamic microsimulation model for epidemics.
Abrams, Jesse F
Soc Sci Med
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Spooner, F., Abrams, J. F., Morrissey, K., Shaddick, G., Batty, M., Milton, R., Dennett, A., et al. (2021). A dynamic microsimulation model for epidemics.. Soc Sci Med, 291 https://doi.org/10.1016/j.socscimed.2021.114461
Funder: Aerospace Technology Institute
Funder: UK Research and Innovation
Funder: The Alan Turing Institute
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.
Dynamics, Coronavirus, Microsimulation, Seir, Covid-19, Spatial-Interaction, Humans, Communicable Disease Control, Policy, Epidemics, COVID-19, SARS-CoV-2
Engineering and Physical Sciences Research Council (EP/T001569/1, EP/W006022/1)
Economic and Social Research Council (ES/L011891/1)
External DOI: https://doi.org/10.1016/j.socscimed.2021.114461
This record's URL: https://www.repository.cam.ac.uk/handle/1810/332219