RT Journal Article SR Electronic T1 A stacked ensemble method for forecasting influenza-like illness visit volumes at emergency departments JF bioRxiv FD Cold Spring Harbor Laboratory SP 2020.10.21.348417 DO 10.1101/2020.10.21.348417 A1 Arthur Novaes de Amorim A1 Rob Deardon A1 Vineet Saini YR 2020 UL http://biorxiv.org/content/early/2020/10/21/2020.10.21.348417.abstract AB Accurate and reliable short-term forecasts of influenza-like illness (ILI) visit volumes at the emergency departments can improve staffing and resource allocation decisions in each hospital. 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 reliability and accuracy of our model’s 1 to 4 weeks ahead forecasts 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. Over this time period, our model’s prediction deviated from the realized ILI visit volume by an average of 12% for 1 week ahead forecasts, with a 90% prediction interval having coverage rates ranging from 90.7 to 97.7%.