Burstiness in activity-driven networks and the epidemic threshold

Abstract

We study the effect of heterogeneous temporal activations on epidemic spreading in temporal networks. We focus on the susceptible-infected-susceptible (SIS) model on activity-driven networks with burstiness. By using an activity-based mean-field approach, we derive a closed analytical form for the epidemic threshold for arbitrary activity and inter-event time distributions. We show that, as expected, burstiness lowers the epidemic threshold while its effect on prevalence is twofold. In low-infective systems burstiness raises the average infection probability, while it weakens epidemic spreading for high infectivity. Our results can help clarify the conflicting effects of burstiness reported in the literature. We also discuss the scaling properties at the transition, showing that they are not affected by burstiness.

arXiv comment23 pages, 11 figures

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Cited by in corpus (11)

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• Sideward contact tracing and the control of epidemics in large gatherings
• Non-Markovian epidemic spreading on temporal networks
• Stochastic sampling effects favor manual over digital contact tracing
• The structural evolution of temporal hypergraphs through the lens of hyper-cores
• Hidden dependence of spreading vulnerability on topological complexity
• Preserving system activity while controlling epidemic spreading in adaptive temporal networks
• Simplicial temporal networks from Wi-Fi data in a University Campus: the effects of restrictions on epidemic spreading
• Emerging Activity Temporal Hypergraph: a model for generating realistic time-varying hypergraphs