2025/06/19 by Gibson, Graham C., Fox, Spencer J., Javan, Emily +4 · 1 citation
Decision Sciences · Mathematics · Medicine · #Applications (stat.AP) #COVID-19 epidemiological studies #Data-Driven Disease Surveillance #FOS: Computer and information sciences #Forecasting Techniques and Applications
paper · pdf · doi:10.48550/arxiv.2506.16410
openalex publication_date 2025/06/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Accurate forecasts of disease 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 epidemiological assumptions. However, these methods often underperform precisely when reliable predictions are most urgently needed -- during periods of rapid epidemic escalation. Here, we introduce epimodulation, a hybrid approach that integrates fundamental epidemiological principles into existing predictive models to enhance forecasting accuracy, especially around epidemic peaks. When applied to simple empirical forecasting methods (ARIMA, Holt--Winters, and spline models), epimodulation improved overall prediction accuracy by an average of 9.1% (range: 8.2--12.5%) for COVID-19 hospital admissions and by 19.5% (range: 17.6--23.2%) for influenza hospital admissions; accuracy during epidemic peaks improved even further, by an average of 20.7% and 25.4%, respectively. Epimodulation also substantially enhanced the performance of complex forecasting methods, including the COVID-19 Forecast Hub ensemble model, demonstrating its broad utility in improving forecast reliability at critical moments in disease outbreaks.