Likelihood-based inference for stochastic models of sexual network formation.
Sexually-transmitted diseases (STDs) constitute a major public health concern. Mathematical models for the transmission dynamics of STDs indicate that heterogeneity in sexual activity level allow them to persist even when the typical behavior of the population would not support endemicity. This insight focuses attention on the distribution of sexual activity level in a population. In this paper, we develop several stochastic process models for the formation of sexual partnership networks. Using likelihood-based model selection procedures, we assess the fit of the different models to three large distributions of sexual partner counts: (1) Rakai, Uganda, (2) Sweden, and (3) the USA. Five of the six single-sex networks were fit best by the negative binomial model. The American women's network was best fit by a power-law model, the Yule. For most networks, several competing models fit approximately equally well. These results suggest three conclusions: (1) no single unitary process clearly underlies the formation of these sexual networks, (2) behavioral heterogeneity plays an essential role in network structure, (3) substantial model uncertainty exists for sexual network degree distributions. Behavioral research focused on the mechanisms of partnership formation will play an essential role in specifying the best model for empirical degree distributions. We discuss the limitations of inferences from such data, and the utility of degree-based epidemiological models more generally.
Handcock
Mark S
MS
Center for Statistics and the Social Sciences, University of Washington, Box 354320, Seattle, WA 98195-4320, USA. handcock@stat.washington.edu
Jones
James Holland
JH
D019992:Databases as Topic; D005260:Female; D015658:HIV Infections / Q000635:transmission; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D011634:Public Health; D012725:Sexual Behavior / Q000706:statistics & numerical data*; D012747:Sexual Partners; D012749:Sexually Transmitted Diseases / Q000635:transmission; D013269:Stochastic Processes; D013548:Sweden; D014454:Uganda; D006113:United Kingdom