Prospective multi-pathogen disease forecasting using autonomous LLM-guided tree search

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arXiv: Prospective multi-pathogen disease forecasting using autonomous LLM-guided tree search Probabilistic forecasting of infectious diseases is crucial for public health but relies on labor-intensive manual model curation by expert modeling teams. This bespoke development bottlenecks scalability to granular geographic resolutions or emerging pathogens. Here, we present an autonomous system using Large Language Model (LLM)-guided tree search to iteratively generate, evaluate, and optimize executable forecasting software. In a fully prospective, real-time evaluation during the 2025-2026

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