Characterizing infection risk in a restaurant environment due to airborne diseases using discrete droplet dispersion simulations

Abstract

The use of masks as a measure to control the spread of respiratory viruses has been widely acknowledged. However, there are instances where wearing a mask is not possible, making these environments potential vectors for virus transmission. Such environments can contain multiple sources of infection and are challenging to characterize in terms of infection risk. To address this issue, we have developed a methodology to investigate the role of ventilation in reducing the infection risk in such environments. We use a restaurant setting as a representative scenario to demonstrate the methodology. Using implicit large eddy simulations along with discrete droplet dispersion modeling we investigate the impact of ventilation and physical distance on the spread of respiratory viruses and the risk of infection. Our findings show that operating ventilation systems, such as mechanical mixing and increasing physical distance between subjects, can significantly reduce the average room infection risk and number of newly infected subjects. However, this observation is subject to the transmissibility of the airborne viruses. In the case of a highly transmissible virus, the use of mechanical mixing may be inconsequential when compared to only fresh air ventilation. These findings provide valuable insights into the mitigation of infection risk in situations where the use of masks is not possible.