Strong dependence of infection profiles on grouping dynamics during epidemiological spreading

Zhenyuan Zhao, Guannan Zhao, Chen Xu, Pak Ming Hui, Neil F. Johnson

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The spreading of an epidemic depends on the connectivity of the underlying host population. Because of the inherent difficulties in addressing such a problem, research to date on epidemics in networks has focused either on static networks, or networks with relatively few rewirings per timestep. Here we employ a simple, yet highly non-trivial, model of dynamical grouping to investigate the extent to which the underlying dynamics of tightly-knit communities can affect the resulting infection profile. Individual realizations of the spreading tend to be dominated by large peaks corresponding to infection resurgence, and a generally slow decay of the outbreak. In addition to our simulation results, we provide an analytical analysis of the run-averaged behaviour in the regime of fast grouping dynamics. We show that the true run-averaged infection profile can be closely mimicked by employing a suitably weighted static network, thereby dramatically simplifying the level of difficulty.

Original languageEnglish (US)
Title of host publicationComplex Sciences - First International Conference, Complex 2009, Revised Papers
Number of pages11
EditionPART 1
StatePublished - Dec 1 2009
Event1st International Conference on Complex Sciences: Theory and Applications, Complex 2009 - Shanghai, China
Duration: Feb 23 2009Feb 25 2009

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering
NumberPART 1
Volume4 LNICST
ISSN (Print)1867-8211


Other1st International Conference on Complex Sciences: Theory and Applications, Complex 2009


  • Complex systems
  • Epidemics
  • Group dynamics
  • Networks

ASJC Scopus subject areas

  • Computer Networks and Communications

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