Supermarkets are considered one of the main hubs that congregate large groups of people. Ying et al. quantitively assessed mitigation efforts to reduce COVID-19 virus transmission in supermarkets. An agent-based model was applied to shopping data and a synthetically created store layout with 106 paths. The model consisted of two components, a customer mobility model and a virus transmission model, to estimate exposure time and infections due to human-to-human contact. After 1000 iterations, the results showed that an average of 14.96 customers were present in the supermarket at a given time and spent an average 5.97 minutes in the store. Total exposure time was average 94.98 minutes per day and the longest exposure time in the simulation was 3.5 minutes. Restrictions on the maximum number of customers in the store, reduced customer arrival rate, using face masks, and a one-way aisle layout were added to the simulation to measure the varying risk of transmission and identify hotspots and bottlenecks. This model can be implemented for other settings to inform best policies for other retail stores with varying network features and sizes, to measure virus transmission and reduce spread.