Consistent Approximation of Epidemic Dynamics on Degree-Heterogeneous Clustered Networks

A. Bishop, I. Z. Kiss, T. House*

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    Realistic human contact networks capable of spreading infectious disease, for example studied in social contact surveys, exhibit both significant degree heterogeneity and clustering, both of which greatly affect epidemic dynamics. To understand the joint effects of these two network properties on epidemic dynamics, the effective degree model of Lindquist et al. [28] is reformulated with a new moment closure to apply to highly clustered networks. A simulation study comparing alternative ODE models and stochastic simulations is performed for SIR (Susceptible–Infected–Removed) epidemic dynamics, including a test for the conjectured error behaviour in [40], providing evidence that this novel model can be a more accurate approximation to epidemic dynamics on complex networks than existing approaches.

    Original languageEnglish
    Title of host publicationComplex Networks and Their Applications VII - Volume 1 Proceedings The 7th International Conference on Complex Networks and their Applications COMPLEX NETWORKS 2018
    EditorsRenaud Lambiotte, Luis M. Rocha, Pietro Lió, Hocine Cherifi, Luca Maria Aiello, Chantal Cherifi
    PublisherSpringer Nature
    Pages376-391
    Number of pages16
    ISBN (Print)9783030054106
    DOIs
    Publication statusE-pub ahead of print - 2 Dec 2018
    Event7th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2018 - Cambridge, United Kingdom
    Duration: 11 Dec 201813 Dec 2018

    Publication series

    NameStudies in Computational Intelligence
    Volume812
    ISSN (Print)1860-949X

    Conference

    Conference7th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2018
    Country/TerritoryUnited Kingdom
    CityCambridge
    Period11/12/1813/12/18

    Keywords

    • Clustering
    • Epidemiology
    • Moment Closure
    • Networks
    • SIR

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