Incorporating Pedestrian Movement in Computational Models of COVID-19 Spread during Air-travel

COVID-19 pandemic has resulted in an over 60 % reduction in airtravel worldwide according to some estimates. The high economic and public perception costs of potential superspreading during air-travel necessitates research efforts that model, explain and mitigate disease spread. The long-duration exposure to infected passengers and the limited air circulation in the cabin are considered to be responsible for the infection spread during flight. Consequently, recent public health measures are primarily based on these aspects. However, a survey of recent on-flight outbreaks indicates that some aspects of the COVID-19 spread, such as long-distance superspreading, cannot be explained without also considering the movement of people. Another factor that could be influential but has not gained much attention yet is the unpredictable passenger behavior. Here, we use a novel infection risk model that is linked with pedestrian dynamics to accurately capture these aspects of infection spread. The model is parameterized through spatiotemporal analysis of a recent superspreading event in a restaurant in China. The passenger movement during boarding and deplaning, as well as the in-plane movement, are modeled with social force model and agent-based model respectively. We utilize the model to evaluate what-if scenarios on the relative effectiveness of policies and procedures such as masking, social distancing, as well as synergistic effects by combining different approaches in airplanes and other contexts. We find that in certain instances independent strategies can combine synergistically to reduce infection probability, by more than a sum of individual strategies.