Journal article
Modeling concurrency and selective mixing in heterosexual partnership networks with applications to sexually transmitted diseases
The Annals of Applied Statistics, Vol.10(4), pp.2021-2046
2016
Abstract
Network-based models for sexually transmitted disease transmission rely on initial partnership networks incorporating structures that may be related to risk of infection. In particular, initial networks should reflect the level of concurrency and attribute-based selective mixing observed in the population of interest. We consider momentary degree distributions as measures of concurrency and propensities for people of certain types to form partnerships with each other as a measure of attribute-based selective mixing. Estimation of momentary degree distributions and mixing patterns typically relies on cross-sectional survey data, and, in the context of heterosexual networks, we describe how this results in two sets of reports that need not be consistent with each other. The reported momentary degree distributions and mixing totals are related through a series of constraints, however. We provide a method to incorporate those in jointly estimating momentary degree distributions and mixing totals. We develop a method to simulate heterosexual networks consistent with these momentary degree distributions and mixing totals, applying it to data obtained from the National Longitudinal Study of Adolescent Health. We first use the momentary degree distributions and mixing totals as mean value parameters to estimate the natural parameters for an exponential-family random graph model and then use a Markov chain Monte Carlo algorithm to simulate person-level heterosexual partnership networks.
Details
- Title
- Modeling concurrency and selective mixing in heterosexual partnership networks with applications to sexually transmitted diseases
- Authors/Creators
- R. Admiraal (Author/Creator)M.S. Handcock (Author/Creator)
- Publication Details
- The Annals of Applied Statistics, Vol.10(4), pp.2021-2046
- Publisher
- Institute of Mathematical Statistics
- Identifiers
- 991005545342607891
- Copyright
- © 2016 Institute of Mathematical Statistics
- Murdoch Affiliation
- School of Engineering and Information Technology
- Language
- English
- Resource Type
- Journal article
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- Collaboration types
- Domestic collaboration
- International collaboration
- Citation topics
- 1 Clinical & Life Sciences
- 1.66 HIV
- 1.66.11 HIV/AIDS Prevention
- Web Of Science research areas
- Statistics & Probability
- ESI research areas
- Mathematics