Dynamics of infectious disease spread between transportation hubs and surrounding communities.

Urban transit systems, particularly those in major metropolitan areas, are becoming increasingly interconnected, making it essential to better understand passenger mobility and its implications for the spread of infectious diseases. Respiratory infectious diseases, including COVID-19 (as a case study), can spread rapidly in densely populated urban environments, particularly within public transportation systems. This study investigates the dynamics of infection spread at a subway station, and its surrounding community, focusing on both short-term and long-term transmission. Using two deterministic ordinary differential equation (ODE) models, we simulate disease transmission over a single business day and examine how daily encounters impact infection numbers over the subsequent three months. The short-term model captures localized interactions between transit passengers and community residents, while the long-term model evaluates the cumulative impact of these encounters on community infections. Parameter estimation was performed using ridership data and the least squares method. Results suggest that mobility-related factors, particularly inflow and outflow rates, have a greater impact on controlling disease spread than transmission rates in both short- and long-term dynamics. Reducing inflow eases congestion and lowers encounters in the hub but increases encounters in the community, whereas reducing outflow increases crowding in the hub while decreasing encounters in the community. Joint reductions in inflow and outflow decrease encounters in both settings. In the long term, changes in transmission rates have only a limited effect on peak infections, while reducing outflow notably decreases infections and reducing inflow slightly increases community infections. Overall, simultaneously reducing inflow and outflow is the most effective strategy for limiting encounters in the short term and infections in the long term.