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

TL;DR

This paper introduces an autonomous system leveraging LLM-guided tree search to generate, evaluate, and optimize infectious disease forecasting models, outperforming traditional human-curated methods in real-time scenarios.

Contribution

The novel framework autonomously translates epidemiological theory into accurate forecasting models, reducing manual effort and enabling scalable, rapid deployment for multiple pathogens.

Findings

Ensembles from machine-generated models matched or outperformed CDC human-curated models.

Successfully navigated data-scarce scenarios for RSV.

Automated system maintained structural fidelity to scientific theories.

Abstract

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 US respiratory season, the system autonomously discovered methodologically diverse models for influenza, COVID-19, and respiratory syncytial virus (RSV). Aggregating these machine-generated models yielded an ensemble that consistently matched or outperformed the gold-standard, human-curated Centers for Disease Control and Prevention (CDC) hub ensembles out-of-sample. The system successfully navigated data-scarce "cold start" scenarios for RSV. Moreover, controlled retrospective ablations revealed that optimizing log-scale distance metrics prevents reward hacking, while an automated judge-in-the-loop ensures structural fidelity to complex scientific theories. By autonomously translating epidemiological theory into accurate, transparent code, this framework overcomes the modeling labor bottleneck, enabling rapid deployment of expert-level disease forecasting at unprecedented scales.