Prospective multi-pathogen disease forecasting using autonomous LLM-guided tree search
A new autonomous system for disease forecasting has been developed using Large Language Model (LLM)-guided tree search. This system aims to improve the scalability and efficiency of infectious disease modeling, particularly for influenza, COVID-19, and RSV. The approach has shown promising results, outperforming traditional human-curated models in real-time evaluations.
- ▪The autonomous system generates and optimizes forecasting software for infectious diseases.
- ▪It successfully discovered diverse models for influenza, COVID-19, and RSV during the 2025-2026 US respiratory season.
- ▪The machine-generated ensemble consistently matched or outperformed CDC hub ensembles.
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Computer Science > Artificial Intelligence arXiv:2605.16238 (cs) COVID-19 e-print Important: e-prints posted on arXiv are not peer-reviewed by arXiv; they should not be relied upon without context to guide clinical practice or health-related behavior and should not be reported in news media as established information without consulting multiple experts in the field. [Submitted on 15 May 2026] Title:Prospective multi-pathogen disease forecasting using autonomous LLM-guided tree search Authors:Sarah Martinson, Michael P. Brenner, Martyna Plomecka, Brian P. Williams, Nicholas G.
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