Heterogeneous epidemic model for assessing data dissemination in opportunistic networks

TL;DR

This paper develops a heterogeneous SIS epidemic model for data dissemination in opportunistic networks, analyzing how different heterogeneity levels in susceptibility and infectivity affect epidemic severity, with implications for general epidemic theory.

Contribution

It introduces a novel heterogeneous epidemic model considering Pareto-distributed transmission parameters for opportunistic networks, providing insights into epidemic progression under different heterogeneity scenarios.

Findings

Greater heterogeneity in susceptibility reduces epidemic severity.

Increased heterogeneity in infectivity amplifies epidemic severity.

Model applicability extends to diseases with no acquired immunity and Pareto-distributed transmission.

Abstract

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 general epidemic theory for estimating the epidemic progression for diseases with no significant acquired immunity -- in the cases where Pareto distribution holds.