@inproceedings{9bc1124c48fd40f08e266010f8ebd4bf,
title = "Consistent Approximation of Epidemic Dynamics on Degree-Heterogeneous Clustered Networks",
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.",
keywords = "Clustering, Epidemiology, Moment Closure, Networks, SIR",
author = "A. Bishop and Kiss, {I. Z.} and T. House",
year = "2018",
month = dec,
day = "2",
doi = "10.1007/978-3-030-05411-3_31",
language = "English",
isbn = "9783030054106",
series = "Studies in Computational Intelligence",
publisher = "Springer Nature",
pages = "376--391",
editor = "Renaud Lambiotte and Rocha, {Luis M.} and Pietro Li{\'o} and Hocine Cherifi and Aiello, {Luca Maria} and Chantal Cherifi",
booktitle = "Complex Networks and Their Applications VII - Volume 1 Proceedings The 7th International Conference on Complex Networks and their Applications COMPLEX NETWORKS 2018",
address = "United States",
note = "7th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2018 ; Conference date: 11-12-2018 Through 13-12-2018",
}