Complex Sciences. First International Conference, Complex 2009, Shanghai, China, February 23-25, 2009. Revised Papers, Part 1

Research Article

Strong Dependence of Infection Profiles on Grouping Dynamics during Epidemiological Spreading

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  • @INPROCEEDINGS{10.1007/978-3-642-02466-5_96,
        author={Zhenyuan Zhao and Guannan Zhao and Chen Xu and Pak Hui and Neil Johnson},
        title={Strong Dependence of Infection Profiles on Grouping Dynamics during Epidemiological Spreading},
        proceedings={Complex Sciences. First International Conference, Complex 2009, Shanghai, China, February 23-25, 2009. Revised Papers, Part 1},
        proceedings_a={COMPLEX PART 1},
        year={2012},
        month={5},
        keywords={complex systems networks epidemics group dynamics},
        doi={10.1007/978-3-642-02466-5_96}
    }
    
  • Zhenyuan Zhao
    Guannan Zhao
    Chen Xu
    Pak Hui
    Neil Johnson
    Year: 2012
    Strong Dependence of Infection Profiles on Grouping Dynamics during Epidemiological Spreading
    COMPLEX PART 1
    Springer
    DOI: 10.1007/978-3-642-02466-5_96
Zhenyuan Zhao1, Guannan Zhao1, Chen Xu2, Pak Hui3, Neil Johnson1,*
  • 1: University of Miami
  • 2: Suzhou University
  • 3: Chinese University of Hong Kong
*Contact email: njohnson@physics.miami.edu

Abstract

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.