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Persistence time of SIS infections in heterogeneous populations and networks
For a susceptible–infectious–susceptible infection model in a heterogeneous population, we present simple formulae giving the leading-order asymptotic (large population) behaviour of the mean persistence time, from an endemic state to extinction of infection. Our model may be interpreted as describi...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Springer Berlin Heidelberg
2018
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6132977/ https://www.ncbi.nlm.nih.gov/pubmed/29476196 http://dx.doi.org/10.1007/s00285-018-1222-1 |
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author | Clancy, Damian |
author_facet | Clancy, Damian |
author_sort | Clancy, Damian |
collection | PubMed |
description | For a susceptible–infectious–susceptible infection model in a heterogeneous population, we present simple formulae giving the leading-order asymptotic (large population) behaviour of the mean persistence time, from an endemic state to extinction of infection. Our model may be interpreted as describing an infection spreading through either (1) a population with heterogeneity in individuals’ susceptibility and/or infectiousness; or (2) a heterogeneous directed network. Using our asymptotic formulae, we show that such heterogeneity can only reduce (to leading order) the mean persistence time compared to a corresponding homogeneous population, and that the greater the degree of heterogeneity, the more quickly infection will die out. |
format | Online Article Text |
id | pubmed-6132977 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-61329772018-09-18 Persistence time of SIS infections in heterogeneous populations and networks Clancy, Damian J Math Biol Article For a susceptible–infectious–susceptible infection model in a heterogeneous population, we present simple formulae giving the leading-order asymptotic (large population) behaviour of the mean persistence time, from an endemic state to extinction of infection. Our model may be interpreted as describing an infection spreading through either (1) a population with heterogeneity in individuals’ susceptibility and/or infectiousness; or (2) a heterogeneous directed network. Using our asymptotic formulae, we show that such heterogeneity can only reduce (to leading order) the mean persistence time compared to a corresponding homogeneous population, and that the greater the degree of heterogeneity, the more quickly infection will die out. Springer Berlin Heidelberg 2018-02-23 2018 /pmc/articles/PMC6132977/ /pubmed/29476196 http://dx.doi.org/10.1007/s00285-018-1222-1 Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Article Clancy, Damian Persistence time of SIS infections in heterogeneous populations and networks |
title | Persistence time of SIS infections in heterogeneous populations and networks |
title_full | Persistence time of SIS infections in heterogeneous populations and networks |
title_fullStr | Persistence time of SIS infections in heterogeneous populations and networks |
title_full_unstemmed | Persistence time of SIS infections in heterogeneous populations and networks |
title_short | Persistence time of SIS infections in heterogeneous populations and networks |
title_sort | persistence time of sis infections in heterogeneous populations and networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6132977/ https://www.ncbi.nlm.nih.gov/pubmed/29476196 http://dx.doi.org/10.1007/s00285-018-1222-1 |
work_keys_str_mv | AT clancydamian persistencetimeofsisinfectionsinheterogeneouspopulationsandnetworks |