Auditable Context-Aware HFMD Forecasting with Structured LLM Agents

Jul 15, 2026

Researchers introduce a two-agent neuro-symbolic framework for forecasting hand-foot-and-mouth disease (HFMD), combining an LLM-based Event Interpreter for contextual signals (such as school calendars, weather, and policy reports) with a probabilistic Forecast Generator. The system provides competitive accuracy compared to traditional and foundation models, while offering auditable explanations and robust 90% prediction intervals. Evaluations on Hong Kong and Lishui datasets show that LLM agents can integrate domain knowledge for interpretable public-health forecasts.

Why it matters: This work demonstrates that LLM agents can enhance the interpretability and trustworthiness of epidemiological forecasts by providing auditable rationales alongside predictions.

Full story at: arXiv Multiagent Systems ↗