Concurrency-Induced Transitions in Epidemic Dynamics on Temporal Networks

Tomokatsu Onaga, Naoki Masuda, James P Gleeson

Research output: Contribution to journalArticle (Academic Journal)peer-review

33 Citations (Scopus)
279 Downloads (Pure)


Social contact networks underlying epidemic processes in humans and animals are highly dynamic. The spreading of infections on such temporal networks can differ dramatically from spreading on static networks. We theoretically investigate the effects of concurrency, the number of neighbors that a node has at a given time point, on the epidemic threshold in the stochastic susceptible-infected-susceptible dynamics on temporal network models. We show that network dynamics can suppress epidemics (i.e., yield a higher epidemic threshold) when the node’s concurrency is low, but can also enhance epidemics when the concurrency is high. We analytically determine different phases of this concurrency-induced transition, and confirm our results with numerical simulations.
Original languageEnglish
Article number108301
Number of pages5
JournalPhysical Review Letters
Issue number10
Early online date6 Sept 2017
Publication statusPublished - 8 Sept 2017


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