Determinants of sexual network structure and their impact on cumulative network measures.

There are four major quantities that are measured in sexual behavior surveys that are thought to be especially relevant for the performance of sexual network models in terms of disease transmission. These are (i) the cumulative distribution of lifetime number of partners, (ii) the distribution of pa...

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Autores principales:	Boris V Schmid, Mirjam Kretzschmar
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/82cf7a099d9d4827a6a5abe99374c2bf
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spelling	oai:doaj.org-article:82cf7a099d9d4827a6a5abe99374c2bf2021-11-18T05:51:23ZDeterminants of sexual network structure and their impact on cumulative network measures.1553-734X1553-735810.1371/journal.pcbi.1002470https://doaj.org/article/82cf7a099d9d4827a6a5abe99374c2bf2012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22570594/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358There are four major quantities that are measured in sexual behavior surveys that are thought to be especially relevant for the performance of sexual network models in terms of disease transmission. These are (i) the cumulative distribution of lifetime number of partners, (ii) the distribution of partnership durations, (iii) the distribution of gap lengths between partnerships, and (iv) the number of recent partners. Fitting a network model to these quantities as measured in sexual behavior surveys is expected to result in a good description of Chlamydia trachomatis transmission in terms of the heterogeneity of the distribution of infection in the population. Here we present a simulation model of a sexual contact network, in which we explored the role of behavioral heterogeneity of simulated individuals on the ability of the model to reproduce population-level sexual survey data from the Netherlands and UK. We find that a high level of heterogeneity in the ability of individuals to acquire and maintain (additional) partners strongly facilitates the ability of the model to accurately simulate the powerlaw-like distribution of the lifetime number of partners, and the age at which these partnerships were accumulated, as surveyed in actual sexual contact networks. Other sexual network features, such as the gap length between partnerships and the partnership duration, could-at the current level of detail of sexual survey data against which they were compared-be accurately modeled by a constant value (for transitional concurrency) and by exponential distributions (for partnership duration). Furthermore, we observe that epidemiological measures on disease prevalence in survey data can be used as a powerful tool for building accurate sexual contact networks, as these measures provide information on the level of mixing between individuals of different levels of sexual activity in the population, a parameter that is hard to acquire through surveying individuals.Boris V SchmidMirjam KretzschmarPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 8, Iss 4, p e1002470 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Boris V Schmid Mirjam Kretzschmar Determinants of sexual network structure and their impact on cumulative network measures.
description	There are four major quantities that are measured in sexual behavior surveys that are thought to be especially relevant for the performance of sexual network models in terms of disease transmission. These are (i) the cumulative distribution of lifetime number of partners, (ii) the distribution of partnership durations, (iii) the distribution of gap lengths between partnerships, and (iv) the number of recent partners. Fitting a network model to these quantities as measured in sexual behavior surveys is expected to result in a good description of Chlamydia trachomatis transmission in terms of the heterogeneity of the distribution of infection in the population. Here we present a simulation model of a sexual contact network, in which we explored the role of behavioral heterogeneity of simulated individuals on the ability of the model to reproduce population-level sexual survey data from the Netherlands and UK. We find that a high level of heterogeneity in the ability of individuals to acquire and maintain (additional) partners strongly facilitates the ability of the model to accurately simulate the powerlaw-like distribution of the lifetime number of partners, and the age at which these partnerships were accumulated, as surveyed in actual sexual contact networks. Other sexual network features, such as the gap length between partnerships and the partnership duration, could-at the current level of detail of sexual survey data against which they were compared-be accurately modeled by a constant value (for transitional concurrency) and by exponential distributions (for partnership duration). Furthermore, we observe that epidemiological measures on disease prevalence in survey data can be used as a powerful tool for building accurate sexual contact networks, as these measures provide information on the level of mixing between individuals of different levels of sexual activity in the population, a parameter that is hard to acquire through surveying individuals.
format	article
author	Boris V Schmid Mirjam Kretzschmar
author_facet	Boris V Schmid Mirjam Kretzschmar
author_sort	Boris V Schmid
title	Determinants of sexual network structure and their impact on cumulative network measures.
title_short	Determinants of sexual network structure and their impact on cumulative network measures.
title_full	Determinants of sexual network structure and their impact on cumulative network measures.
title_fullStr	Determinants of sexual network structure and their impact on cumulative network measures.
title_full_unstemmed	Determinants of sexual network structure and their impact on cumulative network measures.
title_sort	determinants of sexual network structure and their impact on cumulative network measures.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/82cf7a099d9d4827a6a5abe99374c2bf
work_keys_str_mv	AT borisvschmid determinantsofsexualnetworkstructureandtheirimpactoncumulativenetworkmeasures AT mirjamkretzschmar determinantsofsexualnetworkstructureandtheirimpactoncumulativenetworkmeasures
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Determinants of sexual network structure and their impact on cumulative network measures.

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