Assessing the Impact of Mask Usage on COVID-19 Transmission Using a Computer Simulation

Background: COVID-19, caused by SARS-CoV-2, is highly contagious and causes substantial morbidity and mortality. Mask usage has been advocated by health professionals to minimize its spread. Thus, it is important to develop a simulation that models SARS-CoV-2 spread in indoor environments to evaluate mask usage effectiveness. Methods: A visual computer simulation was developed with Pygame in Python 3. A virtual indoor supermarket is simulated by a given flow of customers with an initial infection percentage and mask usage percentage who enter, move around, and exit a supermarket with shelves, tables and cashiers to demonstrate a systems dynamic complexity, i.e. nonlinear interactions of system elements over time. A supermarket was simulated with initial infection rates of 5%, 10%, and 20% and mask use percentages of 0%, 25%, 50% 75%, and 100%. The environmental settings (e.g. shelf number and location) and total customers (N=200) were kept constant. Results: The number of infected customers increased as the percentage of mask usage decreased (p < 0.01). At 5% initial infection, almost no infections were observed at 50% mask usage, with a logarithmic best-fit model (R2 = 0.947). At 10% initial infection, the association between mask usage and decrease in number of infections was best fit with a linear model (R2 = 0.924). For 20% initial infection, a quadratic model was the best fit (R2 = 0.934). While a linear model suggests proportional decreases in infection, the quadratic model suggests more significant reductions in infections at higher rates of mask use (i.e. increasing mask usage from 5% to 10% is less impactful than from 65% to 70%). Conclusion: The results suggest that mask usage has a significant impact on decreasing COVID-19 transmission. Ideally, mask usage should be as high as possible to achieve more significant reductions in COVID-19 infections. Various parameters can be adjusted during simulation as we learn more about SARS-CoV-2 to guide policies for minimizing COVID-19 transmission.