Network typologies predict future molecular linkages in the network of HIV transmission

• Published in: AIDS (London) Vol. 37; no. 11; pp. 1739 - 1746
• Main Authors: Rich, Shannan N, Cook, Robert L, Mavian, Carla N, Garrett, Karen, Spencer, Emma C, Salemi, Marco, Prosperi, Mattia
• Format: Journal Article
• Language: English
• Published: England Lippincott Williams & Wilkins 01.09.2023
• Subjects: Cluster Analysis; Cohort Studies; Epidemiology and Social; HIV Infections - epidemiology; HIV-1 - genetics; Humans; Molecular Epidemiology
• ISSN: 0269-9370; 1473-5571
• DOI: 10.1097/QAD.0000000000003621

HIV molecular transmission network typologies have previously demonstrated associations to transmission risk; however, few studies have evaluated their predictive potential in anticipating future transmission events. To assess this, we tested multiple models on statewide surveillance data from the Florida Department of Health. This was a retrospective, observational cohort study examining the incidence of new HIV molecular linkages within the existing molecular network of persons with HIV (PWH) in Florida. HIV-1 molecular transmission clusters were reconstructed for PWH diagnosed in Florida from 2006 to 2017 using the HIV-TRAnsmission Cluster Engine (HIV-TRACE). A suite of machine-learning models designed to predict linkage to a new diagnosis were internally and temporally externally validated using a variety of demographic, clinical, and network-derived parameters. Of the 9897 individuals who received a genotype within 12 months of diagnosis during 2012-2017, 2611 (26.4%) were molecularly linked to another case within 1 year at 1.5% genetic distance. The best performing model, trained on two years of data, was high performing (area under the receiving operating curve = 0.96, sensitivity = 0.91, and specificity = 0.90) and included the following variables: age group, exposure group, node degree, betweenness, transitivity, and neighborhood. In the molecular network of HIV transmission in Florida, individuals' network position and connectivity predicted future molecular linkages. Machine-learned models using network typologies performed superior to models using individual data alone. These models can be used to more precisely identify subpopulations for intervention.