Improving Outbreak Forecasts Through Model Augmentation

TL;DR

This paper presents epimodulation, a hybrid method that enhances disease outbreak forecasts by integrating epidemiological principles into existing models, significantly improving accuracy during epidemic peaks.

Contribution

Introduction of epimodulation, a novel hybrid approach that improves the accuracy of outbreak forecasts, especially during rapid epidemic escalation periods.

Findings

Epimodulation improved COVID-19 hospital admission forecasts by 9.1%.

It increased influenza forecast accuracy by 19.5%.

Performance gains were even greater during epidemic peaks.

Abstract

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.