Policy-Embedded Graph Expansion: Networked HIV Testing with Diffusion-Driven Network Samples

TL;DR

This paper introduces a novel framework called Policy-Embedded Graph Expansion (PEGE) combined with Dynamics-Driven Branching (DDB) to improve HIV testing efficiency by leveraging network diffusion models, outperforming existing methods in real-world scenarios.

Contribution

The paper proposes PEGE and DDB, innovative approaches that embed graph expansion distributions into testing policies, enabling better decision-making in network-based HIV testing with limited data.

Findings

PEGE + DDB outperforms baselines with 13% higher reward.

Achieves 9% more HIV detections with 25% fewer tests.

Effective in real-world HIV transmission networks.

Abstract

HIV is a retrovirus that attacks the human immune system and can lead to death without proper treatment. In collaboration with the WHO and Wits University, we study how to improve the efficiency of HIV testing with the goal of eventual deployment, directly supporting progress toward UN Sustainable Development Goal 3.3. While prior work has demonstrated the promise of intelligent algorithms for sequential, network-based HIV testing, existing approaches rely on assumptions that are impractical in our real-world implementations. Here, we study sequential testing on incrementally revealed disease networks and introduce Policy-Embedded Graph Expansion (PEGE), a novel framework that directly embeds a generative distribution over graph expansions into the decision-making policy rather than attempting explicit topological reconstruction. We further propose Dynamics-Driven Branching (DDB), a diffusion-based graph expansion model that supports decision making in PEGE and is designed for data-limited settings where forest structures arise naturally, as in our real-world referral process. Experiments on real HIV transmission networks show that the combined approach (PEGE + DDB) consistently outperforms existing baselines (e.g., 13% improvement in discounted reward and 9% more HIV detections with 25% of the population tested) and explore key tradeoffs that drive decision quality.