Concurrency-Induced Transitions in Epidemic Dynamics on Temporal Networks
Concurrency-Induced Transitions in Epidemic Dynamics on Temporal Networks
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DOI：
10.1103/physrevlett.119.108301
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发表时间：
2017-09-06
影响因子：
8.6
通讯作者：
Masuda, Naoki
中科院分区：
文献类型：
--
作者：
Onaga, Tomokatsu;Gleeson, James P.;Masuda, Naoki
人类和动物中流行过程背后的社会联系网络是高度动态的。感染在这种时间网络上的传播可能与在静态网络上的传播有很大不同。我们从理论上研究了并发性，即节点在给定时间点上的邻居数量，对时间网络模型上随机易感-感染-易感动力学中的流行阈值的影响。我们表明，当节点的并发性较低时，网络动态可以抑制流行病（即产生更高的流行病阈值），但当并发性较高时，网络动态也可以增强流行病。我们分析确定了这种并发诱导转变的不同阶段，并通过数值模拟证实了我们的结果。
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.