A dynamic microsimulation model for epidemics. (December 2021) - 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; et al.
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. Highlights: A microsimulation framework for modelling disease transmission within communities. Integration of data from multiple, diverse sources to estimate exposure and risk. Computationally efficient implementation allows rapid assessment of scenarios. Allows the effects of different non-pharmaceutical interventions to be assessed. Results show substantial reduction in infections associated with earlier UK lockdown.