Quantitative Research, G-Research, London, United Kingdom.
Centre for Advanced Spatial Analysis, UCL, London, United Kingdom.
PLoS One. 2021 Apr 9;16(4):e0249821. doi: 10.1371/journal.pone.0249821. eCollection 2021.
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.
自 2020 年 3 月初 COVID-19 爆发以来，世界各地的超市都实施了不同的政策，以减少店内的病毒传播，保护顾客和员工，例如限制店内顾客的最大数量、改变店铺布局，或强制要求佩戴口罩。为了定量评估这些缓解措施，我们制定了一个基于超市顾客移动的基于代理的模型（我们用网络表示），并基于顾客与感染顾客近距离接触的时间（我们称之为暴露时间）建立了一个简单的病毒传播模型。我们将我们的模型应用于合成的商店和购物数据，以展示如何使用我们的模型来估计暴露时间，从而估计由于人与人之间的接触而在商店中感染的人数，以及如何对不同的商店干预措施进行建模。源代码可在 https://github.com/fabianying/covid19-supermarket-abm 上公开获取。我们鼓励零售商使用该模型来找到最有效的商店政策，以减少商店中的病毒传播，从而保护顾客和员工。
