Online Program

Bayesian Network Analysis: HIV Risk in Southern Indian Community

*Daniel Philip Heard, Duke University 
John Schneider, University of Chicago 

Keywords: network, epidemic, HIV

This work examines a community of men who have sex with men (MSM) in southern India with a high HIV rate. The data is from an ego-centric study and includes attributes such as religion, age, marital status, caste, sex position, religion, and whether an individual was a sex worker. The data also included self-reported attributes of interviewed individuals. We use a Bayesian mixed effects model (Hoff 2011) to investigate characteristics of individuals within the network and how these characteristics related to sexual behavior. We found that being a sex worker, social caste, and marital status were strongly related to individuals' sexual behavior and sex position. To investigate the formation of ties and determine if any latent structure existed within the network, we used a mixed-membership stochastic block model (Airoldi 2008). Using this method, we detected 2 latent blocks within the network, which correspond to marital status, and obtained each individual's posterior membership probabilities for each block. This information gives insight into possible medical interventions to mitigate the HIV epidemic in the community.