AI 中文总结
该研究指出Aygün等人的ERA系统预测性能优势源于回顾性预测中的信息泄漏，警示AI辅助预测建模需关注数据修订带来的泄漏问题。
AI 中文摘要
Aygün等人（2026年，网址：https://this URL）声称其AI驱动的经验研究辅助（ERA）系统生成的COVID-19住院预测，在2024/25赛季的表现大幅优于当前最先进的CDC集成模型。我们证明，观测到的性能提升归因于回顾性预测设置中的信息泄漏，该问题源于未考虑数据修订。由于类似机制在许多其他预测领域也存在，我们的警示不仅适用于流行病预测，还与整个新兴的AI辅助预测建模领域相关。
英文摘要
Aygün et al (2026, https://doi.org/10.1038/s41586-026-10658-6) claim that their AI-driven Empirical Research Assistance (ERA) system produces COVID-19 hospitalisation forecasts which outperform the state-of-the-art CDC ensemble by a considerable margin for the 2024/25 season. We demonstrate that the observed performance gain is attributable to information leakage in the retrospective forecasting setup, which resulted because data revisions were not taken into account. As similar mechanisms are at play in many other forecasting fields, our cautionary tale applies not just to epidemic forecasting, but is relevant to the entire emerging field of AI-assisted predictive modelling.
Comments4 pages, 1 figure. This is a re-analysis of a paper by Aygün et al (2026, https://doi.org/10.1038/s41586-026-10658-6)
