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原文連結
論文資訊
- 類型：已發表論文
- 日期：2025-10-24
摘要
Accurate forecasts of 疾病 outbreaks are critical for effective public health responses, management of healthcare surge capacity, and communication of public risk. There are a growing number of powerful forecasting methods that fall into two broad categories-empirical models that extrapolate from historical data, and mechanistic models based on fixed 流行病學ological assumptions. However, these methods often underperform precisely when reliable predictions are most urgently needed-during periods of rapid 流行病學c escalation. Here, we introduce epimodulation, a hybrid approach that integrates fundamental 流行病學ological principles into existing predictive models to enhance forecasting accuracy, especially around 流行病學c peaks. When applied to empirical and 機器學習 forecasting methods (Autoregressive Integra
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