TY - JOUR
T1 - Concurrency-Induced Transitions in Epidemic Dynamics on Temporal Networks
AU - Onaga, Tomokatsu
AU - Gleeson, James P.
AU - Masuda, Naoki
N1 - Funding Information:
We thank Leo Speidel for discussion. We thank the SocioPatterns collaboration ( http:// www.sociopatterns.org ) for providing the data set. T. O. acknowledges the support provided through JSPS Research Fellowship for Young Scientists. J. G. acknowledges the support provided through Science Foundation Ireland (Grants No. 15/SPP/E3125 and No. 11/PI/1026). N. M. acknowledges the support provided through JST, CREST, and JST, ERATO, Kawarabayashi Large Graph Project.
Publisher Copyright:
© 2017 American Physical Society.
PY - 2017/9/6
Y1 - 2017/9/6
N2 - 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.
AB - 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.
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U2 - 10.1103/PhysRevLett.119.108301
DO - 10.1103/PhysRevLett.119.108301
M3 - Article
C2 - 28949155
AN - SCOPUS:85029692938
SN - 0031-9007
VL - 119
JO - Physical Review Letters
JF - Physical Review Letters
IS - 10
M1 - 108301
ER -