Consistent Approximation of Epidemic Dynamics on Degree-Heterogeneous Clustered Networks

Research output: Chapter in Book/Report/Conference proceedingChapter

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.

Bibliographical metadata

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 statusPublished - 2019
Event7th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2018 - Cambridge, United Kingdom
Event 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
CountryUnited Kingdom
CityCambridge
Period11/12/1813/12/18