Modelling the Propagation of Infectious Disease via Transportation Networks

The dynamics of human mobility have been known to play a critical role in the spread of infectious diseases like COVID-19. In this paper, we present a simple compact way to model the transmission of infectious disease through transportation networks using widely available aggregate mobility data in the form of a zone-level origin-destination (OD) travel flow matrix. A key feature of our model is that it not only captures the propagation of infection via direct connections between zones (that is, first-order effects) as in existing studies, but also transmission effects that are due to subsequent interactions in the remainder of the system (that is, higher-order effects). We demonstrate the importance of capturing higher-order effects in a simulation study. We then apply our model to study the first wave of COVID-19 infections in Italy using real-time data on mobility between Italian provinces (that is, province-level OD data) and daily reported caseloads at the same geographical level. Our empirical results indicate substantial predictive power, particularly during early stages of an outbreak. At least 85-percent of the spatial variation in observed weekly province-level COVID-19 cases can be forecasted using our proposed model. Further, from on our model, we derive metrics that could help identify target areas for intervention, specifically during early outbreaks or resurgences of infectious diseases.