This study leverages a rare set of longitudinal social and spatial network data along with detailed individual- level data and HIV sequences from over 2,500 people who inject drugs (PWID) in New Delhi, India followed from 2016-21. It aims to explore the use of machine learning and viral phylogenetics as a potential avenue to circumvent network enumeration challenges and produce new analytical strategies to monitor epidemics and model the most effective and resource-efficient intervention approach in a city. In practice, this affords the development of network models that simulate the effect of various network-based intervention strategies on HIV incidence and could be used to inform a wide array of social, behavioral, and pharmacologic interventions. Making network data more accessible can lead to new HIV prevention approaches that guide officials in focusing limited resources for the greatest impact and can provide a greater understanding of the epidemic dynamics.