Generative Frontier Planning for Adaptive Peer-Referral Recruitment under Covariate-Dependent Arrivals
arXiv:2606.08360v1 Announce Type: new Abstract: Peer-referral recruitment systems such as respondent-driven sampling are critical for studying and intervening on hidden populations affected by infectious diseases. To accelerate recruitment, public health agencies must adaptively allocate limited referral resources across multiple rounds, where current decisions shape both the number and the covariates of future recruits. Prior work makes this problem tractable by assuming that referrals are drawn i.i.d.\ from a homogeneous population, an assumption that ignores the homophily and shared context
The paper leverages recent advancements in AI, specifically generative planning, to address a long-standing challenge in public health recruitment, indicating a maturation of AI application in complex adaptive systems.
This development allows for more efficient and targeted recruitment strategies in hidden populations, critical for public health interventions and resource allocation, especially concerning infectious diseases.
The ability to account for covariate-dependent arrivals and homophily in peer-referral systems makes recruitment methods more adaptive and effective, moving beyond simplistic i.i.d. assumptions.
- · Public Health Agencies
- · Population Health Researchers
- · AI/ML Developers
- · At-Risk Populations
Improved efficiency and reach of public health interventions in hard-to-access populations.
Better understanding of social networks and disease transmission dynamics in hidden communities leading to more effective policy making.
Ethical considerations around data privacy and potential for algorithmic bias in AI-driven recruitment could become more prominent, requiring robust regulatory frameworks.
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Read at arXiv cs.LG