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Eight challenges for network epidemic models

Pellis, L; Ball, F; Bansal, S; Eames, K; House, T; Isham, V; Trapman, P; (2015) Eight challenges for network epidemic models. Epidemics , 10 pp. 58-62. 10.1016/j.epidem.2014.07.003. Green open access

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Abstract

Networks offer a fertile framework for studying the spread of infection in human and animal populations. However, owing to the inherent high-dimensionality of networks themselves, modelling transmission through networks is mathematically and computationally challenging. Even the simplest network epidemic models present unanswered questions. Attempts to improve the practical usefulness of network models by including realistic features of contact networks and of host–pathogen biology (e.g. waning immunity) have made some progress, but robust analytical results remain scarce. A more general theory is needed to understand the impact of network structure on the dynamics and control of infection. Here we identify a set of challenges that provide scope for active research in the field of network epidemic models.

Type: Article
Title: Eight challenges for network epidemic models
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.epidem.2014.07.003
Publisher version: http://dx.doi.org/10.1016/j.epidem.2014.07.003
Language: English
Additional information: © 2014 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/3.0/).
Keywords: Infectious disease models; Transmission dynamics; Contact networks; Random graphs; Dynamic networks; Control measures
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/1457126
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