Network Analysis of Injectable Drug Users and the Spread of HIV
This article published in Science Advances in October 2022 analyzes the network of drug users (specifically injectables) and the spread of HIV. This study took place in New Delhi, India. This setting was useful in analyzing networks because it has ‘injection venues,’ defined as sites where multiple injections took place over a six month period (Clipman et al., 2022). These sites were analyzed as central hubs, where people from multiple different communities crossed paths. Because of the popularity of these sites, they were not just injection sites, but became HIV spreading hotspots, reaching into multiple different communities.
Researchers did not stop their study at the analysis of these network structures, but experimented with network intervention. The aim of ‘network intervention’ is to interrupt the transmission of the disease. Researchers identified (using network theory) which hubs would be the most impactful to stage an intervention. They found that when interventions were placed optimally, infectious disease spread could be lessened immensely. They apply their findings about ‘network intervention’ not just to the spread of HIV, but to the spread of other infectious diseases as well.
When thinking of this article in terms of our class, it is helpful to understand interventions as the breaking of a connection between nodes in a network. Also, we can understand intervention as an attempt to cut the network into smaller pieces, where infected nodes cannot easily reach unaffected nodes (cutting the paths available to the infected nodes in the network).
I think this article is a great example of the real-world application of the study of networks. The researchers themselves even said, “Network-based interventions require a detailed understanding of these underlying network structures, extending beyond immediate ties, and incorporating spatial and temporal dynamics to target interventions efficiently” (Clipman et al., 2022). Without the understanding of network structure these interventions, and the findings about the success of interventions in infectious diseases spread, would not have been able to take place.
Clipman, S. J., Mehta, S. H., Mohapatra, S., Srikrishnan, A. K., Zook, K. J. C., Duggal, P., Saravanan, S., Nandagopal, P., Kumar, M. S., Lucas, G. M., Latkin, C. A., & Solomon, S. S. (2022). Deep learning and social network analysis elucidate drivers of HIV transmission in a high-incidence cohort of people who inject drugs. Science Advances, 8(42). https://doi.org/10.1126/sciadv.abf0158