System Dynamics of a Refined Epidemic Model for Infection Propagation Over Complex Networks

The ability to predict future epidemic threats, both in real and digital worlds, and to develop effective containment strategies heavily leans on the availability of reliable infection spreading models. The stochastic behavior of such processes makes them even more demanding to scrutinize over structured networks. This paper concerns the dynamics of a new susceptible-infected-susceptible (SIS) epidemic model incorporated with multistage infection (infection delay) and an infective medium (propagation vector) over complex networks. In particular, we investigate the critical epidemic thresholds and the infection spreading pattern using mean-field approximation (MFA) and results obtained through extensive numerical simulations. We further generalize the model for any arbitrary number of infective media to mimic existing scenarios in biological and social networks. Our analysis and simulation results reveal that the inclusion of multiple infective medium and multiple stages of infection significantly alleviates the epidemic threshold and, thus, accelerates the process of infection spreading in the population.