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

arXiv:2605.16238v2 Announce Type: replace Abstract: Probabilistic forecasting of infectious diseases is crucial for public health monitoring and preparedness but has historically relied on labor-intensive manual model curation by expert modeling teams. Bespoke development is an obstacle to scaling models to granular geographic resolutions or emerging pathogens. Here, we prospectively evaluate Empirical Research Assistance (ERA), an autonomous system that uses Large Language Model-guided tree search to iteratively generate, evaluate, and optimize executable forecasting software. ERA was primari

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