ARTICLE: Deep learning and social network analysis elucidate drivers of HIV transmission in a high-incidence cohort of people who inject drugs
AUTHORS: Steven J Clipman, Shruti H Mehta, Shobha Mohapatra, Aylur K Srikrishnan, Katie J C Zook, Priya Duggal, Shanmugam Saravanan, Paneerselvam Nandagopal, Muniratnam Suresh Kumar, Gregory M Lucas, Carl A Latkin, Sunil S Solomon
JOURNAL: Sci Adv. 2022 Oct 21;8(42):eabf0158. doi: 10.1126/sciadv.abf0158. Epub 2022 Oct 19.
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 strongly associated with incidence was found to overlap six of the seven communities, suggesting that an intervention deployed at this one location could reach the majority of the sample. These findings highlight the utility of network analysis and deep learning in HIV program design.
For the full article, click here.
For a link to the abstract, click here.