AI Insight
This study presents CAST (Cascade-Aware Suppression of Transmission), a polynomial-time optimization algorithm designed to strategically allocate limited healthcare resources to virally unsuppressed HIV-positive individuals in order to minimize downstream chains of new infections within transmission networks. The authors formalize the resource allocation problem mathematically, connecting it to the Minimum-k-Union computational problem, and derive a 2 times the square root of the unsuppressed population approximation ratio using Hoeffding-style concentration bounds. Evaluations on real-world HIV transmission networks show CAST outperforms standard public health and computer science baseline approaches, with demonstrated robustness across varied network structures, edge probability assumptions, and imperfect data conditions.
Why it matters
Given that antiretroviral therapy can effectively eliminate individual transmission risk, an algorithm that identifies the highest-impact targets for intervention could meaningfully improve HIV prevention outcomes where health systems face resource constraints. This approach has potential applicability beyond HIV to other infectious disease networks where cascade suppression under budgetary limits is a priority.
arXiv:2605.20218v1 Announce Type: new
Abstract: 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 $mathbf{P}$ 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 $(delta, epsilon)$-approximation algorithm that achieves a $2sqrt{|mathbf{P}|}$ 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.
Source: Network-Based Interventions for HIV Prevention via Cascade-Aware Suppression of Transmission