Heterogeneous Epidemic Model for Assessing Data Dissemination in Opportunistic Networks

In this paper we investigate a susceptible-infected-susceptible (SIS) epidemic model describing data dissemination in opportunistic networks with heterogeneous setting of transmission parameters. We obtained the estimation of the final epidemic size assuming that amount of data transferred between network nodes possesses a Pareto distribution, implying scale-free properties. In this context, more heterogeneity in susceptibility means the less severe epidemic progression, and, on the contrary, more heterogeneity in infectivity leads to more severe epidemics — assuming that the other parameter (either heterogeneity or susceptibility) stays fixed. The results are general enough and can be useful in a broader context of epidemic theory, e.g. for estimating the progression for diseases with no significant acquired immunity — in the cases where Pareto distribution holds.