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International Conference on Complex Networks and their Applications

COMPLEX NETWORKS 2018: Complex Networks and Their Applications VII pp 376–391Cite as

Consistent Approximation of Epidemic Dynamics on Degree-Heterogeneous Clustered Networks

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 812))

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.

Work supported by the Engineering and Physical Sciences Research Council Grant numbers EP/I01358X/1 and EP/N033701/1.

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Author information

Authors and Affiliations

  1. Centre for Complexity Science, University of Warwick, Coventry, CV4 7AL, UK

    A. Bishop

  2. Department of Mathematics, School of Mathematical and Physical Sciences, University of Sussex, Brighton, BN1 9QH, UK

    I. Z. Kiss

  3. School of Mathematics, University of Manchester, Manchester, M13 9PL, UK

    T. House

Authors
  1. A. Bishop
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  2. I. Z. Kiss
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  3. T. House
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Corresponding author

Correspondence to T. House .

Editor information

Editors and Affiliations

  1. Nokia Bell Labs, Cambridge, UK

    Luca Maria Aiello

  2. IUT Lumière, University of Lyon, Bron Cedex, France

    Chantal Cherifi

  3. LE2I UMR CNRS 6306 9, University of Burgundy, Dijon Cedex, France

    Hocine Cherifi

  4. Mathematical Institute, University of Oxford, Oxford, UK

    Renaud Lambiotte

  5. Department of Computer Science and Technology, The Computer Laboratory, University of Cambridge, Cambridge, UK

    Pietro Lió

  6. Center for Complex Networks and Systems Research, School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA

    Luis M. Rocha

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Bishop, A., Kiss, I.Z., House, T. (2019). Consistent Approximation of Epidemic Dynamics on Degree-Heterogeneous Clustered Networks. In: Aiello, L., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P., Rocha, L. (eds) Complex Networks and Their Applications VII. COMPLEX NETWORKS 2018. Studies in Computational Intelligence, vol 812. Springer, Cham. https://doi.org/10.1007/978-3-030-05411-3_31

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