Modelling COVID-19 transmission in supermarkets using an agent-based model

TL;DR

This paper presents an agent-based model to simulate COVID-19 transmission in supermarkets, allowing assessment of mitigation strategies like store layout changes and mask policies to reduce infection risk.

Contribution

The study introduces a novel agent-based modeling approach for supermarket COVID-19 transmission, incorporating store layout and customer movement data.

Findings

Model estimates infection counts based on customer proximity and time.

Simulation shows effectiveness of different mitigation strategies.

Open-source code available for practical use by retailers.

Abstract

Since the outbreak of COVID-19 in early March 2020, UK supermarkets have implemented different policies to reduce the virus transmission in stores to protect both customers and staff, such as restricting the maximum number of customers in a store, changes to the store layout, or enforcing a mandatory face covering policy. To quantitatively assess these mitigation methods, we formulate an agent-based model of customer movement in a supermarket (which we represent by a network) with a simple virus transmission model based on the amount of time a customer spends in close proximity to infectious customers. We apply our model to synthetic store and shopping data to show how one can use our model to estimate the number of infections due to human-to-human contact in stores and how to model different store interventions. The source code is openly available at https://github.com/fabianying/covid19-supermarket-abm. We encourage retailers to use the model to find the most effective store policies that reduce virus transmission in stores and thereby protect both customers and staff.