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
Across the world, governments have introduced non-pharmaceutical interventions (NPI) to try and control the spread of COVID-19 through a reduction in the number of contacts between susceptible members of the population and those with the disease (Desvars-Larrive et al, 2020)
We combine the power of well-established methods within the social and behavioural sciences, namely spatial microsimulation and spatial interaction models, within a dynamic Susceptible – Exposed – Infection – Removed (SEIR) to offer the best approximation of (i) the daily, individual-level mobilities that charac terise many of the interactions which lead to COVID-19 transmission and (ii) the impact of different NPI based on the complex health, socioeconomic and behavioural attributes of the British population
This paper presents a novel, data-driven modelling framework that reflects the complexities of the British population to model the trans mission of COVID-19 within communities and to assess the effect of policy interventions
Summary
Governments have introduced non-pharmaceutical interventions (NPI) to try and control the spread of COVID-19 through a reduction in the number of contacts between susceptible members of the population and those with the disease (Desvars-Larrive et al, 2020). We combine the power of well-established methods within the social and behavioural sciences, namely spatial microsimulation and spatial interaction models, within a dynamic SEIR to offer the best approximation of (i) the daily, individual-level mobilities that charac terise many of the interactions which lead to COVID-19 transmission and (ii) the impact of different NPI based on the complex health, socioeconomic and behavioural attributes of the British population This framework provides the much-needed ability to assess the effects of past interventions and simulate the effects of future policy decisions on different population groups at a variety of spatial scales.
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