A stacked ensemble method for forecasting influenza-like illness visit volumes at emergency departments

Accurate and reliable short-term forecasts of influenza-like illness (ILI) visit volumes at emergency departments can improve staffing and resource allocation decisions within hospitals. In this paper, we developed a stacked ensemble model that averages the predictions from various competing methodologies in the current frontier for ILI-related forecasts. We also constructed a back-of-the-envelope prediction interval for the stacked ensemble, which provides a conservative characterization of the uncertainty in the stacked ensemble predictions. We assessed the accuracy and reliability of our model with 1 to 4 weeks ahead forecast targets using real-time hospital-level data on weekly ILI visit volumes during the 2012-2018 flu seasons in the Alberta Children's Hospital, located in Calgary, Alberta, Canada. Our results suggest the forecasting performance of the stacked ensemble meets or exceeds the performance of the individual models over all forecast targets.

Automated summary

Accurate and reliable short-term forecasts of influenza-like illness (ILI) visit volumes at emergency departments are crucial for improving staffing and resource allocation decisions within hospitals. The stacked ensemble model developed in this study, which averages predictions from competing methodologies, outperforms individual models in forecasting ILI visits at Alberta Children's Hospital in Canada.