SARS-CoV-2 Spread and Infection Risk in Public Transit Scenes

Abstract

Two years ago, a new virus named Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) emerged. In the ensuing race to contain the virus, many non-pharmaceutical interventions (NPIs) have been introduced. Yet questions like “What is the risk of SARS-CoV-2 infection in a particular scenario?” and “Which NPIs limit virus transmission most effectively?” remain. Crowd and epidemiological simulation models can help formulate an answer to these questions. This paper studies virus spread and infection risk using a newly developed hybrid virus spread model PeDViS (Pedestrian Dynamics–Virus Spread model), which links an existing validated crowd movement dynamics model (NOMAD) with a new virus spread model (QVEmod). In particular, five common public transit scenarios are simulated: walking through a corridor, buying a ticket, moving through the ticket gates, waiting at a platform, and traveling by train. The relative impact of four variables (i.e., demand, waiting time, facial masks, and ventilation) was studied. This study illustrates that PeDViS can provide comprehensive insights into virus spread and the relative differences in infection risk. Furthermore, it corroborates the impacts featured in literature for all public transit scenarios. That is, ventilation and facial masks limit the probability of infecting other individuals. Moreover, waiting time and higher demand levels increase the probability of infecting other travelers. Second, especially large impacts of the NPIs facial masks and ventilation are found for the more “dangerous” scenarios; that is, long queues, delays, or waiting times coincide with high demands and crowd densities.