EpiEvolve agent improves pandemic forecasting with adaptive memory

Researchers have developed EpiEvolve, a novel agent designed to improve pandemic forecasting by adapting to changing disease dynamics. Unlike static models, EpiEvolve uses a fixed LLM backbone but dynamically updates its forecasting strategy through a hierarchical episodic memory. This memory stores past outcomes and distills recurring errors into strategic rules, allowing the model to learn from delayed labels and adapt to new disease regimes more quickly. In tests with COVID-19 hospitalization data, EpiEvolve significantly outperformed static models and external ensembles, reducing the lag time after regime shifts. AI

IMPACT Introduces a novel adaptive agent architecture for time-series forecasting, potentially applicable to other domains with shifting data regimes.

RANK_REASON This is a research paper detailing a new model/agent for a specific task. [lever_c_demoted from research: ic=1 ai=1.0]

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• COVID-19
• EpiEvolve
• LLM

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