ArticleNature communications2026
Evaluating spatially targeted HIV interventions and harm reduction services among people who inject drugs in a high-burden setting.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Geometric deep learning enables high-fidelity network imputation for HIV transmission modeling.Communications medicine · 2026Article
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10 authors.
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Abstract
People who inject drugs (PWID) in India continue to experience high HIV incidence while coverage of HIV and harm reduction services within this population remains suboptimal in many settings, highlighting the need to identify novel service delivery points. To evaluate the effectiveness of spatially focused upscaling of interventions at observed venues where PWID injected drugs together, we developed an individual-based dynamic transmission model of HIV informed by detailed injection network, service engagement, and injection venue attendance data collected in a sociometric study of PWID (n = 2512) in New Delhi, India. HIV incidence was simulated for different spatial targeting strategies and with increasing service coverage at injection venues according to UNAIDS/UNODC goals. We identified significant decreases in predicted HIV incidence when deploying interventions at frequently visited injection venues (from 6.8 cases/100 person-years to 2.7/100PY for full service coverage at the most-visited venue, and further down to 1.3/100PY for 12 most-visited venues). Prioritizing the most visited venues stratified by spatial clusters provided services to a larger number of individuals versus prioritizing the overall most visited venues, suggesting that service expansion at venues that are spatially distinct with minimal population overlap has a slightly larger impact on reducing HIV incidence.
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