Fig 1.
Overview of agent-based model.
(A) A network representation of a small supermarket/convenience store with an example shopping path (in green). We generate each shopping path from a sequence of shelf locations (in blue), which correspond to the shelves from a customer picks up their items during a visit and the entrance and the tills. In this example, the customer picks up K = 4 items at the shelves marked in blue with 2, 3, 4, and 5. (B) Virus transmission model. A susceptible customer (in black) becomes infected at rate β whenever they are in the same zone as an infectious customer (in red).
Table 1.
Parameter values in our agent-based model.
We list uncertainty bounds, when available.
Fig 2.
Exposure times and chance of infection from 1000 simulations.
(A) Histogram of total customer exposure time for all exposed customers (i.e., susceptible customers with positive exposure time) across 1000 simulations. The distribution can be approximated by an exponential distribution. (B) Histogram of chance of infection per susceptible customer across 1000 simulations. (C) Total exposure time per node. Nodes in the centre and near the tills of the store show significantly higher amount of exposure time than others.
Table 2.
We show the mean and standard deviation of each metric across 1000 simulations.
Fig 3.
Change in number of infections and chance of infection by reducing maximum number of customers in store or arrival rate.
(A + B) Mean number of infections (with the shaded area showing the standard deviation) as a function of maximum number Cmax of customers and customer arrival rate λ (respectively). As the number of infections is a linear function of the total exposure time, we also show the total exposure time on the right vertical axis. (C + D) Mean chance of infection for each susceptible customer (with the shaded area showing the standard deviation) as a function of maximum number Cmax of customers and customer arrival rate λ (respectively). As the chance of infection is a linear function of the mean exposure time (per susceptible customer), we also show the mean exposure time on the right vertical axis. In subfigures (A) and (C), the mean number of infections and mean chance of infection plateaus, as the number of customers typically does not exceed 20 in our simulations.
Fig 4.
Effect of one-way aisle layout on infections.
(A) Store layout with one-way aisles. (B + C) Number of infections in a store as a function of the customer arrival time and mean number of customers (respectively). We show on the right vertical axis the total exposure time, as the number of infections is proportional to the total exposure time in our model. The one-way layout increases the number of infections with the same arrival rate (see subfigure D). It appears that the number of infections mainly depends on how many customers are in the store on average (see subfigure C). (D) Mean customer shopping time. The one-way layout increases the time that customers spend in the store, so more customers are in the store and thereby increase the number of infections.
Table 3.
Comparison of interventions and combinations of interventions.
We compare possible interventions and combinations of interventions by their effect on the number of infections and chance of infection based on our agent-based model. We can achieve the largest amount of reduction by combining the face mask policy with reducing arrival rate or the maximum number of customers.