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The impact of contact tracing in clustered populations
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House, Thomas A. and Keeling, Matthew James (2010) The impact of contact tracing in clustered populations. PLoS Computational Biology, Vol.6 (No.3). Article no. e1000721. doi:10.1371/journal.pcbi.1000721 ISSN 1553-734X.
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Official URL: http://dx.doi.org/10.1371/journal.pcbi.1000721
Abstract
The tracing of potentially infectious contacts has become an important part of the control strategy for many infectious diseases, from early cases of novel infections to endemic sexually transmitted infections. Here, we make use of mathematical models to consider the case of partner notification for sexually transmitted infection, however these models are sufficiently simple to allow more general conclusions to be drawn. We show that, when contact network structure is considered in addition to contact tracing, standard “mass action” models are generally inadequate. To consider the impact of mutual contacts (specifically clustering) we develop an improvement to existing pairwise network models, which we use to demonstrate that ceteris paribus, clustering improves the efficacy of contact tracing for a large region of parameter space. This result is sometimes reversed, however, for the case of highly effective contact tracing. We also develop stochastic simulations for comparison, using simple re-wiring methods that allow the generation of appropriate comparator networks. In this way we contribute to the general theory of network-based interventions against infectious disease.
Item Type: | Journal Article | |||||||||
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Subjects: | R Medicine > RA Public aspects of medicine | |||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Life Sciences (2010- ) > Biological Sciences ( -2010) Faculty of Science, Engineering and Medicine > Science > Mathematics |
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Library of Congress Subject Headings (LCSH): | Communicable diseases -- Transmission -- Mathematical models, Sexually transmitted diseases -- Prevention, Vector control, Social networks -- Mathematical models | |||||||||
Journal or Publication Title: | PLoS Computational Biology | |||||||||
Publisher: | Public Library of Science | |||||||||
ISSN: | 1553-734X | |||||||||
Official Date: | 26 March 2010 | |||||||||
Dates: |
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Volume: | Vol.6 | |||||||||
Number: | No.3 | |||||||||
Number of Pages: | 9 | |||||||||
Page Range: | Article no. e1000721 | |||||||||
DOI: | 10.1371/journal.pcbi.1000721 | |||||||||
Status: | Peer Reviewed | |||||||||
Access rights to Published version: | Open Access (Creative Commons) | |||||||||
RIOXX Funder/Project Grant: |
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