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.
翻译:自2020年3月初COVID-19爆发以来,联合王国超市实施了不同的政策,以减少商店内病毒传播,保护顾客和工作人员,例如限制商店内的最大客户数量,改变商店布局,或强制实施表面覆盖政策。为了定量评估这些缓解方法,我们根据客户在感染性顾客附近花费的时间,以简单的病毒传播模式为基础,在超市(我们代表一个网络)制定一个基于代理的客户流动模式。我们将我们的模型应用于合成商店和购物数据,以表明人们如何利用我们的模型估计由于商店内人与人接触而感染的人数,以及如何模拟不同的商店干预措施。源代码公开发布在https://github.com/fabiannying/covid19-超级市场胶囊中。我们鼓励零售商利用该模型找到最有效的商店政策,减少商店内的病毒传播,从而保护顾客和工作人员。