An Agent-Based Model of COVID-19 Epidemic: A Case of Barking and Dagenham, London, UK

Keywords: Spatial pattern, Transmission dynamics, COVID-19, Agent-based model, Human mobility
Session Type: Virtual Paper Abstract
Day: Saturday
Session Start / End Time: 2/26/2022 11:20 AM (Eastern Time (US & Canada)) - 2/26/2022 12:40 PM (Eastern Time (US & Canada))
Room: Virtual 5

Abstract

Nonpharmaceutical interventions (NPIs) have been implemented to deal with the COVID-19 pandemic worldwide. And lockdown, known as large-scale physical distancing measures and movement restrictions, has been regarded as the most effective NPI. To assess the effectiveness of lockdown control, we proposed a GIS-ABM that simulates the spread of COVID-19 with and without lockdown intervention in Barking and Dagenham, London. Specifically, it bridged the virtual environment and geographical context to keep the heterogeneities induced by spatial structure. It aims to facilitate an understanding of the complexity of individual-level interactions behind transmissions in specific locations such as workplaces, schools and shopping malls. At the same time, we considered age-specific patterns for infection and death probabilities to simulate trends for different age groups. Theoretically, we could provide infection and death case numbers simulated from the individual-based susceptible-exposed-infected-recovered (SEIR) epidemic framework, where each person is part of the transmission chain. By modelling the temporal network of interactions within the ABM framework, it provides a better understanding of COVID-19 transmission dynamics and to estimate the pandemic’s future trends. The results suggest the dynamic effectiveness changes of lockdown interventions over time and the impact on different age groups. The prototype could be applied to test various scenarios and results for similar transmissible disease outbreaks in urban environments by adjusting parameters or changing the input spatial data set. It could help promote the policy makers’ understanding of disease spread dynamics and urge the public to take better steps towards pandemic prevention and control.