The Library
Systematic approximations to susceptible-infectious-susceptible dynamics on networks
Tools
Tools
Tools
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
|
PDF
WRAP_journal.pcbi.1005296.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (2627Kb) | Preview |
|
![[img]](http://wrap.warwick.ac.uk/style/images/fileicons/application_pdf.png) |
PDF
WRAP_0170476-maths-260117-finalcopy.pdf - Accepted Version Embargoed item. Restricted access to Repository staff only - Requires a PDF viewer. Download (4Mb) |
Official URL: http://doi.org/10.1371/journal.pcbi.1005296
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: |
|
||||||||
| 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) |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
