Abstract
Since the outbreak of COVID-19 in early March 2020, supermarkets around the world 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 (which we call the exposure time). We apply our model to synthetic store and shopping data to show how one can use our model to estimate exposure time and thereby 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 under 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.
Highlights
As the main provider of food and essential goods, supermarkets remained open in many countries throughout the COVID-19 pandemic in 2020, while the majority of other businesses shut down during periods of government-mandated lockdowns [1, 2]
We propose an agent-based model for customer dynamics which we use to estimate the total amount of exposure time, which we define as the total amount of time that customers are in close proximity to infected customers
We demonstrate how to use our model, what metrics we can record in it, and compare different interventions on our synthetic store
Summary
As the main provider of food and essential goods, supermarkets remained open in many countries throughout the COVID-19 pandemic in 2020, while the majority of other businesses (such as general retail stores) shut down during periods of government-mandated lockdowns [1, 2]. Supermarkets represent one of the main hubs where a large number of people mix indoors throughout the pandemic and are a potential risk area where the virus SARSCoV-2, which causes COVID-19, may be transmitted. It is vital to find safe ways for customers to shop and minimize virus transmission. Models for customer dynamics and virus transmission are useful towards that goal, as they can be used to estimate the infection risk and assess how different interventions affect the risk. We propose an agent-based model for customer dynamics which we use to estimate the total amount of exposure time, which we define as the total amount of time that customers are in close proximity to infected customers. Using a simple virus transmission model, we estimate the number of infections from exposure time.
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