AI Agents and Epidemic Intelligence on Respiratory Infectious Diseases: Toward a Conceptual Framework Integrating Decision Support

TL;DR

This paper proposes using AI agents to enhance epidemic intelligence by integrating decision support into surveillance, risk evaluation, and early warning systems.

Contribution

The novel contribution is a conceptual framework extending epidemic intelligence with AI-driven decision support and intervention optimization.

Findings

AI agents can continuously process multisource data for real-time risk evaluation and forecasting.

Multiagent systems can generate tailored warnings and actionable recommendations for epidemic control.

Embedding interpretability and human oversight improves trust and accountability in AI systems.

Abstract

Traditional epidemic intelligence relies heavily on human epidemiologists for data interpretation and reporting, which makes it resource intensive, slow to respond, and vulnerable to variability in professional expertise. To overcome these limitations, we propose an expanded conceptual epidemic intelligence quadripartite framework that extends the classical trinity of (1) surveillance, (2) risk evaluation, and (3) early warning with a fourth pillar, (4) decision support and intervention optimization through AI agents. Acting as 24/7 digital epidemiologists, multiagent systems can integrate heterogeneous signals from multisource surveillance systems, conduct contextual risk evaluation and adaptive forecasting, generate tailored early warnings, and provide actionable recommendations for targeted control—closing the loop between detection and response. Embedding interpretability and mandatory human-in-the-loop oversight enhances trust and accountability. Nonetheless, real-world deployment requires addressing context-specific challenges of data quality, interoperability, robustness, governance, circular reporting, and equity. If designed with transparency, inclusiveness, and resilience, AI agents have the potential to transform epidemic intelligence into a continuously adaptive and globally connected system.

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