arXiv:1403.5815cs.SI
Heterogeneous epidemic model for assessing data dissemination in opportunistic networks
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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.
Real networks are not lattices; they have varied degree distributions. The famous result of Marián Boguña and Alessandro Vespignani shows, that for scale-free networks (which closely resemble the networks we see in the real world), the epidemic threshold could be arbitrarily…
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