Concurrency-Induced Transitions in Epidemic Dynamics on Temporal Networks

Tomokatsu Onaga, James P. Gleeson, Naoki Masuda

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

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
JournalPhysical Review Letters
Volume119
Issue number10
DOIs
Publication statusPublished - 2017 Sep 6
Externally publishedYes

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thresholds
infectious diseases
animals
simulation

ASJC Scopus subject areas

  • Physics and Astronomy(all)

Cite this

Concurrency-Induced Transitions in Epidemic Dynamics on Temporal Networks. / Onaga, Tomokatsu; Gleeson, James P.; Masuda, Naoki.

In: Physical Review Letters, Vol. 119, No. 10, 06.09.2017.

Research output: Contribution to journalArticle

Onaga, Tomokatsu ; Gleeson, James P. ; Masuda, Naoki. / Concurrency-Induced Transitions in Epidemic Dynamics on Temporal Networks. In: Physical Review Letters. 2017 ; Vol. 119, No. 10.
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