Airborne Disease Propagation on Large Scale Social Contact Networks

Social sensing has received growing interest in a broad range of applications from business to health care. The potential benefits of modeling infectious disease spread through geo-tagged social sensing data has recently been demonstrated, yet it has not considered contagion events that can occur even when co-located individuals are no longer in physical contact, such as for capturing the dynamics of airborne diseases. In this study, we exploit the location updates made by 0.6 million users of the Momo social networking application to characterize airborne disease dynamics. Airborne diseases can transmit through infectious particles exhaled by the infected individuals. We introduce the concept of same-place different-time (SPDT) transmission to capture the persistent effect of airborne particles in their likelihood to spread a disease. Because the survival duration of these infectious particles is dependent on environmental conditions, we investigate through large-scale simulations the effects of three parameters on SPDT-based disease diffusion: the air exchange rate in the proximity of infected individuals, the infectivity decay rates of pathogen particles, and the infection probability of inhaled particles. Our results confirm a complex interplay between the underlying contact network dynamics and these parameters, and highlight the predictive potential of social sensing for epidemic outbreaks.