Effects of Heterogeneous and Clustered Contact Patterns on Infectious Disease Dynamics

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dc.creatorVolz, Erik M.en
dc.creatorMiller, Joel C.en
dc.creatorGalvani, Alisonen
dc.creatorMeyers, Lauren Ancelen
dc.date.accessioned2013-06-28T15:53:16Zen
dc.date.available2013-06-28T15:53:16Zen
dc.date.issued2011-06-02en
dc.descriptionErik M. Volz is with University of Michigan, Joel C. Miller is with Harvard University and the National Institutes of Health, Alison Galvani is with Yale University, Lauren Ancel Meyers is with UT Austin and the Santa Fe Institute.en
dc.description.abstractThe spread of infectious diseases fundamentally depends on the pattern of contacts between individuals. Although studies of contact networks have shown that heterogeneity in the number of contacts and the duration of contacts can have far-reaching epidemiological consequences, models often assume that contacts are chosen at random and thereby ignore the sociological, temporal and/or spatial clustering of contacts. Here we investigate the simultaneous effects of heterogeneous and clustered contact patterns on epidemic dynamics. To model population structure, we generalize the configuration model which has a tunable degree distribution (number of contacts per node) and level of clustering (number of three cliques). To model epidemic dynamics for this class of random graph, we derive a tractable, low-dimensional system of ordinary differential equations that accounts for the effects of network structure on the course of the epidemic. We find that the interaction between clustering and the degree distribution is complex. Clustering always slows an epidemic, but simultaneously increasing clustering and the variance of the degree distribution can increase final epidemic size. We also show that bond percolation-based approximations can be highly biased if one incorrectly assumes that infectious periods are homogeneous, and the magnitude of this bias increases with the amount of clustering in the network. We apply this approach to model the high clustering of contacts within households, using contact parameters estimated from survey data of social interactions, and we identify conditions under which network models that do not account for household structure will be biased.en
dc.description.departmentBiological Sciences, School ofen
dc.description.sponsorshipThe authors acknowledge financial support from NIH U01 GM087719. JCM acknowledges support from the RAPIDD program of Department of Homeland Security and the NIH Fogarty International Center; and the United States National Institutes of Health Models of Infectious Disease Agent Study program through cooperative agreement 1U54GM088558. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript,en
dc.identifier.citationVolz EM, Miller JC, Galvani A, Ancel Meyers L (2011) Effects of Heterogeneous and Clustered Contact Patterns on Infectious Disease Dynamics. PLoS Comput Biol 7(6): e1002042. doi:10.1371/journal.pcbi.1002042en
dc.identifier.doi10.1371/journal.pcbi.1002042en
dc.identifier.urihttp://hdl.handle.net/2152/20557en
dc.language.isoengen
dc.publisherPublic Library of Scienceen
dc.rightsAttribution 3.0 United Statesen
dc.rightsCC-BYen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/en
dc.subjectDisease susceptibilityen
dc.subjectEpidemiologyen
dc.subjectGenerating functionsen
dc.subjectGraphsen
dc.subjectInfectious disease epidemiologyen
dc.subjectInfectious diseasesen
dc.subjectPercolationen
dc.subjectPopulation dynamicsen
dc.titleEffects of Heterogeneous and Clustered Contact Patterns on Infectious Disease Dynamicsen
dc.typeArticleen

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