Indirect interactions influence contact network structure and diffusion dynamics (1906.02405v1)

Abstract: Interaction patterns at the individual level influence the behaviour of diffusion over contact networks. Most of the current diffusion models only consider direct interactions among individuals to build underlying infectious items transmission networks. However, delayed indirect interactions, where a susceptible individual interacts with infectious items after the infected individual has left the interaction space, can also cause transmission events. We define a diffusion model called the same place different time transmission (SPDT) based diffusion that considers transmission links for these indirect interactions. Our SPDT model changes the network dynamics where the connectivity among individuals varies with the decay rates of link infectivity. We investigate SPDT diffusion behaviours by simulating airborne disease spreading on data-driven contact networks. The SPDT model significantly increases diffusion dynamics (particularly for networks with low link densities where indirect interactions create new infection pathways) and is capable of producing realistic disease reproduction number. Our results show that the SPDT model is significantly more likely to lead to outbreaks compared to current diffusion models with direct interactions. We find that the diffusion dynamics with including indirect links are not reproducible by the current models, highlighting the importance of the indirect links for predicting outbreaks.