An Agent-Based Model of COVID-19 Epidemic: A Case of Barking and Dagenham, London, UK
2021
Li, Bayi | Feng, Zhiqiang
The novel coronavirus disease 2019 (COVID-19) has swept across the globe, taken countless lives and grievously wounded the economy. Nonpharmaceutical interventions (NPIs) have been implemented worldwide, and lockdown has been regarded as the most effective NPI. To assess the effectiveness of lockdown control, in this project we proposed an agent-based model (ABM) that simulates the spread of COVID-19 with and without lockdown intervention in Barking and Dagenham, London. In the study, we integrated geographical information with ABM technology to simulate individuals’ interactions in various activities via a geospatial context. By doing so, we could provide infection and death case numbers simulated from the individual-based susceptible-exposed-infected-recovered epidemic framework, where each person is part of the transmission chain. The results suggested lockdown could effectively flatten the COVID-19 curve but was not effective for long periods. This prototype could be accommodated easily for other diseases or locations by adjusting parameters or changing the input spatial data set, which could help promote the public’s understanding of disease spread dynamics and urge others to take better steps towards pandemic prevention and control.
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