Factors Associated with Human Immunodeficiency Virus Infections Linked in Genetic Clusters but Disconnected in Partner Tracing
This article describes a pairwise comparison between dyads in a sociosexual network constructed from HIV surveillance data. Network components were compared among nodes with HIV pathogen genetic data. A set of multivariable generalized estimating equations were applied to the concordance between component and HIV genetic cluster membership; component discordance within cluster concordance is an indicator of incomplete partner elicitation and contact tracing. Read more at: Sex Transm Dis (2020), 47(2), 80-87. doi:10.1097/olq.0000000000001094
Dana K. Pasquale, Irene A. Doherty, William C. Miller, Peter A. Leone, Lynne A. Sampson, Sue Lynn Ledford, Joseph Sebastian, Ann M. Dennis
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IN THE NEWS
Social Networks And Health: It’s Who You Know
Every office has experienced it. One person contracts a cold, and before you know it the entire group is coughing and reaching for the tissues. Our social connections have incredible implications for our health, and not just because they shape the spread of communicable diseases like the common cold, the flu or even HIV.
Sparked by the confluence of a rapid rise in network techniques across the social and physical sciences, the Duke Network Analysis Center seeks to crystallize the latent talent in this area at Duke and around the triangle to build a world-premier source for cutting edge network studies. The rise of network science over the last 10 to 15 years is predicated on building scientific insight by modeling the complex patterns of connections that link primary elements to each other. The range of such work is exceedingly broad, since the unifying network abstraction is virtually content free. Thus, social network studies add relational context to our understandings of human behavior in areas as diverse as health, culture, organizations, science or politics. Similar tools are used to great advantage in biology, physics, and ecology to name just a few.