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Likelihood-based inference for stochastic models of sexual network formation
Mark S Handcock1, James Holland Jones
1Center for Statistics and the Social Sciences, University of Washington, Box 354320, Seattle, WA 98195-4320, USA. handcock@stat.washington.edu
Theoretical Population Biology
|May 12, 2004
Summary
Mathematical models reveal that sexual partnership network structures are complex. Behavioral heterogeneity is key to understanding sexually-transmitted disease (STD) persistence and network formation.
Area of Science:
- Epidemiology
- Mathematical Biology
- Network Science
Background:
- Sexually-transmitted diseases (STDs) are a significant public health issue.
- Mathematical models highlight the role of sexual activity heterogeneity in STD persistence.
- Understanding sexual partnership network formation is crucial for STD transmission dynamics.
Purpose of the Study:
- To develop and evaluate stochastic process models for sexual partnership network formation.
- To assess the fit of different models to empirical sexual partner count data.
- To identify key drivers of sexual network structure.
Main Methods:
- Development of stochastic process models for network formation.
- Application of likelihood-based model selection.
- Analysis of large sexual partner count datasets from Uganda, Sweden, and the USA.
Main Results:
- Negative binomial and power-law (Yule) models best fit observed sexual networks.
- Model fit varied across different populations and sexes.
- Multiple models often provided similar fits, indicating model uncertainty.
Conclusions:
- No single process fully explains sexual network formation.
- Behavioral heterogeneity is essential for understanding network structure.
- Further research on partnership formation mechanisms is needed to refine epidemiological models.