Burstiness in activity-driven networks and the epidemic threshold

We study the effect of heterogeneous temporal activations on epidemic spreading in temporal networks. We focus on the susceptible-infected-susceptible 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.

• Publication year

  2019

• Venue

  Journal of Statistical Mechanics: Theory and Experiment

• Publication date

  2019-03-27

• Fields of study

  Mathematics, Physics, Computer Science

• Identifiers
  DOI 10.1088/1742-5468/ab16c4arXiv 1903.11308
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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