A dynamic microsimulation model for epidemics

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

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.