# Network-Based Interventions for HIV Prevention via Cascade-Aware Suppression of Transmission

arXiv: https://arxiv.org/abs/2605.20218 · HTML: https://mlsanity.com/paper/2605.20218/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 53 references that are in the map.
- First posted on arXiv 11 May 2026.

## Builds on (most cited first, 9 of 9)

- [Smart "Predict, then Optimize"](https://mlsanity.com/paper/1710.08005/) (2017)
- [The Densest k-Subhypergraph Problem](https://mlsanity.com/paper/1605.04284/) (2016)
- [Cooperating Graph Neural Networks with Deep Reinforcement Learning for Vaccine Prioritization](https://mlsanity.com/paper/2305.05163/) (2023)
- [Minimizing the Union: Tight Approximations for Small Set Bipartite Vertex Expansion](https://mlsanity.com/paper/1611.07866/) (2016)
- [Active Screening for Recurrent Diseases: A Reinforcement Learning Approach](https://mlsanity.com/paper/2101.02766/) (2021)
- [Efficient Influence Minimization via Node Blocking](https://mlsanity.com/paper/2405.12871/) (2024)
- [Policy-Embedded Graph Expansion: Networked HIV Testing with Diffusion-Driven Network Samples](https://mlsanity.com/paper/2601.16233/) (2026)
- [Hidden population size estimation from respondent-driven sampling: a network approach](https://mlsanity.com/paper/1504.08349/) (2015)
- [Seeing the Unseen Network: Inferring Hidden Social Ties from Respondent-Driven Sampling](https://mlsanity.com/paper/1511.04137/) (2015)

## Abstract (arXiv)

Treating and preventing Human Immunodeficiency Virus (HIV) remains a critical global health challenge. While antiretroviral therapy provides a path toward viral suppression -- effectively eliminating an individual's transmission risk -- systemic resource constraints limit the reach of intervention efforts. This work addresses the strategic distribution of intensive resources among virally unsuppressed individuals to minimize the expected cascade of new infections within a transmission network. We formalize this challenge as a novel constrained optimization problem where we have resources to "treat" k out of a set P of virally unsuppressed individuals, and establish its theoretical connections to existing computational literature. We then propose Cascade-Aware Suppression of Transmission (CAST), a polynomial-time (δ, ε)-approximation algorithm that achieves a 2√|P| approximation ratio by leveraging connections to the Minimum-k-Union (MkU) problem and Hoeffding-style concentration bounds. Extensive evaluations on real-world HIV networks demonstrate that CAST outperforms standard public health and computer science baselines. Furthermore, we show that CAST is empirically robust across diverse infectious disease networks, varied edge probability initializations, and settings involving imperfect network data.