Consistent Approximation of Epidemic Dynamics on Degree-Heterogeneous Clustered Networks

Abstract

Realistic human contact networks capable of spreading infectious disease, for example studied in social contact surveys, exhibit both significant degree heterogeneity and clustering, both of which greatly affect epidemic dynamics. To understand the joint effects of these two network properties on epidemic dynamics, the effective degree model of Lindquist et al. [30] is reformulated with a new moment closure to apply to highly clustered networks. A simulation study comparing alternative ODE models and stochastic simulations is performed for SIR (Susceptible-Infected-Removed) epidemic dynamics, including a test for the conjectured error behaviour in [42], providing evidence that this novel model can be a more accurate approximation to epidemic dynamics on complex networks than existing approaches.

Assessment of Connectivity-Based Resilience to Attacks Against Multiple Nodes in SDNs

“…Although such models have been originally motivated to cover the different ways of modeling the spread of infections among living species, some of them have also been used in contexts more related to communication networks. Examples of such works are multiple failures propagation in GMPLS-based networks [39], random jamming activities for limited use in wireless sensor networks [40], epidemic dynamics in highly clustered networks [41], [42] and worm propagation in wireless sensor networks [43].…”