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.
Highlights
Introduction.—Social contact networks—on which infectious diseases occur in humans and animals or viral information spreads online and offline—are mostly dynamic
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 when the node’s concurrency is low, but can enhance epidemics when the concurrency is high
Summary
Introduction.—Social contact networks—on which infectious diseases occur in humans and animals or viral information spreads online and offline—are mostly dynamic. Concurrency-Induced Transitions in Epidemic Dynamics on Temporal Networks
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