Consistent Approximation of Epidemic Dynamics on Degree-Heterogeneous Clustered Networks

  • A. Bishop
  • I. Z. Kiss
  • T. HouseEmail author
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 812)


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.


Networks Epidemiology Moment Closure SIR Clustering 


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Authors and Affiliations

  1. 1.Centre for Complexity ScienceUniversity of WarwickCoventry, CV4 7ALUK
  2. 2.Department of Mathematics, School of Mathematical and Physical SciencesUniversity of SussexBrighton, BN1 9QHUK
  3. 3.School of MathematicsUniversity of ManchesterManchester, M13 9PLUK

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