Concurrency-Induced Transitions in Epidemic Dynamics on Temporal Networks

Tomokatsu Onaga, James P. Gleeson, Naoki Masuda

研究成果: Article査読

21 被引用数 (Scopus)

抄録

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.

本文言語English
論文番号108301
ジャーナルPhysical Review Letters
119
10
DOI
出版ステータスPublished - 2017 9 6
外部発表はい

ASJC Scopus subject areas

  • 物理学および天文学(全般)

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