arXiv:2606.05513v1 Announce Type: cross Abstract: Epidemic LLM forecasters are usually trained and evaluated as static supervised models, whereas operational pandemic forecasting is a streaming process in which labels arrive after predictions and disease regimes shift over time. We study this mismatch in weekly COVID-19 hospitalization trend forecasting across five variant regimes. We introduce EpiEvolve, a self-evolving agent that wraps an LLM forecaster trained on the warm-start period and keeps its weights fixed during streaming. EpiEvolve adapts by storing forecast outcomes in a hierarchic
The proliferation of Large Language Models (LLMs) and the need for more adaptive forecasting in dynamic environments drive the development of self-evolving agents like EpiEvolve.
This development moves AI forecasting beyond static models, offering a more robust and responsive approach to real-time events like pandemics, with implications for various streaming data applications.
AI forecasting for streaming data can now dynamically learn and adapt to regime shifts without retraining, significantly improving accuracy and operational relevance in highly mutable contexts.
- · Public health organizations
- · AI agents developers
- · Epidemiologists
- · Real-time analytics platforms
- · Static forecasting model providers
- · Traditional statistical modeling approaches
More accurate and timely public health responses become possible due to improved pandemic forecasting.
The agentic framework could extend to other domains requiring adaptive forecasting, such as financial markets or climate modeling.
Reduced societal and economic disruption from future pandemics or other dynamic crises, fostering greater resilience.
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