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

2026-05-11

AI Summary

This study addresses the critical challenge of optimally allocating limited intervention resources to disrupt HIV transmission chains. Formulating the problem as a constrained combinatorial optimization task, the work selects k individuals from among virally unsuppressed infected persons to minimize subsequent transmission risk. The authors propose CAST, the first polynomial-time (δ, ε)-approximation algorithm theoretically linked to the Minimum-k-Union problem. CAST integrates Hoeffding-type concentration inequalities with graph optimization techniques, rendering it applicable to incomplete contact networks and generalizable to diverse infectious disease settings. Experimental evaluation on real-world HIV transmission networks demonstrates that CAST significantly outperforms existing public health strategies and computational baselines, while maintaining robust performance across varying data conditions.

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 $(δ, ε)$-approximation algorithm that achieves a $2\sqrt{|\mathbf{P}|}$ 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.

Problem

• HIV prevention
• transmission cascade
• resource allocation
• viral suppression
• network-based intervention

Innovation

• Cascade-Aware Suppression
• constrained optimization
• approximation algorithm
• transmission network
• Minimum-k-Union