Advancing U.S. Public Health Surveillance: Leveraging Generative AI and Agentic Systems for the National HIV Behavioral Surveillance Program

This comprehensive technical paper responds to the Centers for Disease Control and Prevention's Federal Register notice (Docket No. CDC-2025-0753) concerning the revision of the National HIV Behavioral Surveillance System (NHBS). We propose an integrated framework leveraging Generative AI (GenAI) and agentic systems to enhance the NHBS data collection methodology across 21 Metropolitan Statistical Areas (MSAs). Our approach addresses all five evaluation criteria specified by the Office of Management and Budget: (1) necessity and practical utility, (2) accuracy of burden estimates, (3) enhancement of data quality, utility, and clarity, (4) minimization of respondent burden through technology, and (5) assessment of information collection costs. Drawing on recent research in AI-assisted surveying, we demonstrate how Large Language Models (LLMs), adaptive interviewing systems, and human-AI hybrid frameworks can transform NHBS from a periodic cross-sectional survey into a dynamic, real-time surveillance tool while reducing the estimated 3,398-hour annual burden. We provide detailed implementation recommendations for the proposed three-year cycle, addressing ethical considerations, validation requirements, and quality assurance protocols for deployment in public health settings. This expanded framework includes comprehensive technical specifications, cost-benefit analyses, and risk mitigation strategies to support evidence-based decision-making for CDC leadership.