Deep learning and social network analysis elucidate drivers of HIV transmission in a high-incidence cohort of people who inject drugs.

ABSTRACT: Globally, people who inject drugs (PWID) experience some of the fastest-growing HIV epidemics. Network-based approaches represent a powerful tool for understanding and combating these epidemics; however, detailed social network studies are limited and pose analytical challenges. We collected longitudinal social (injection partners) and spatial (injection venues) network information from 2512 PWID in New Delhi, India. We leveraged network analysis and graph neural networks (GNNs) to uncover factors associated with HIV transmission and identify optimal intervention delivery points. Longitudinal HIV incidence was 21.3 per 100 person-years. Overlapping community detection using GNNs revealed seven communities, with HIV incidence concentrated within one community. The injection venue most stron

SUBMITTER: Clipman SJ 

PROVIDER: S-EPMC9581475 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature