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Systematic approximations to susceptible-infectious-susceptible dynamics on networks

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Keeling, Matthew James, House, Thomas A., Cooper, A. J. and Pellis, Lorenzo (2016) Systematic approximations to susceptible-infectious-susceptible dynamics on networks. PLoS Computational Biology, 12 (12). e1005296. doi:10.1371/journal.pcbi.1005296

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Official URL: http://doi.org/10.1371/journal.pcbi.1005296

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Abstract

Network-based infectious disease models have been highly effective in elucidating the role of contact structure in the spread of infection. As such, pair- and neighbourhood-based approximation models have played a key role in linking findings from network simulations to standard (random-mixing) results. Recently, for SIR-type infections (that produce one epidemic in a closed population) on locally tree-like networks, these approximations have been shown to be exact. However, network models are ideally suited for Sexually Transmitted Infections (STIs) due to the greater level of detail available for sexual contact networks, and these diseases often possess SIS-type dynamics. Here, we consider the accuracy of three systematic approximations that can be applied to arbitrary disease dynamics, including SIS behaviour. We focus in particular on low degree networks, in which the small number of neighbours causes build-up of local correlations between the state of adjacent nodes that are challenging to capture. By examining how and when these approximation models converge to simulation results, we generate insights into the role of network structure in the infection dynamics of SIS-type infections.

Item Type: Journal Article
Subjects: R Medicine > RA Public aspects of medicine
R Medicine > RC Internal medicine
Divisions: Faculty of Science > Engineering
Faculty of Science > Mathematics
SWORD Depositor: Library Publications Router
Library of Congress Subject Headings (LCSH): Communicable diseases -- Prevention -- Mathematical models, Sexually transmitted diseases, Gonorrhea, Chlamydia, HIV infections
Journal or Publication Title: PLoS Computational Biology
Publisher: Public Library of Science
ISSN: 1553-7358
Official Date: 20 December 2016
Dates:
DateEvent
20 December 2016Published
9 December 2016Accepted
3 June 2016Submitted
Volume: 12
Number: 12
Article Number: e1005296
DOI: 10.1371/journal.pcbi.1005296
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access
Funder: Engineering and Physical Sciences Research Council (EPSRC)
Grant number: (EP/J002437/1)

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