  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 nodes' 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.
