ArticlePloS one2021
A stacked ensemble method for forecasting influenza-like illness visit volumes at emergency departments.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Expanding optimization ensemble model methods for forecasting seasonal influenza in the U.S.Infectious Disease Modelling · 2026Article
- An ensemble n-sub-epidemic modeling framework for short-term forecasting epidemic trajectories: Application to the COVID-19 pandemic in the USA.PLoS computational biology · 2022Article
- An ensemblemedRxiv : the preprint server for health sciences · 2022Article
- Ensemble bootstrap methodology for forecasting dynamic growth processes using differential equations: application to epidemic outbreaks.BMC medical research methodology · 2021Article
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Authors and funding
3 authors.
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Abstract
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.
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