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 methodo...

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Main Authors: Arthur Novaes de Amorim, Rob Deardon, Vineet Saini
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2021-01-01
Series:PLoS ONE
Online Access:https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0241725&type=printable
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author Arthur Novaes de Amorim
Rob Deardon
Vineet Saini
author_facet Arthur Novaes de Amorim
Rob Deardon
Vineet Saini
author_sort Arthur Novaes de Amorim
collection DOAJ
description 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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spelling doaj-art-eb7d4b121ed443669b56d27f00403c162025-08-20T02:54:30ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01163e024172510.1371/journal.pone.0241725A stacked ensemble method for forecasting influenza-like illness visit volumes at emergency departments.Arthur Novaes de AmorimRob DeardonVineet SainiAccurate 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.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0241725&type=printable
spellingShingle Arthur Novaes de Amorim
Rob Deardon
Vineet Saini
A stacked ensemble method for forecasting influenza-like illness visit volumes at emergency departments.
PLoS ONE
title A stacked ensemble method for forecasting influenza-like illness visit volumes at emergency departments.
title_full A stacked ensemble method for forecasting influenza-like illness visit volumes at emergency departments.
title_fullStr A stacked ensemble method for forecasting influenza-like illness visit volumes at emergency departments.
title_full_unstemmed A stacked ensemble method for forecasting influenza-like illness visit volumes at emergency departments.
title_short A stacked ensemble method for forecasting influenza-like illness visit volumes at emergency departments.
title_sort stacked ensemble method for forecasting influenza like illness visit volumes at emergency departments
url https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0241725&type=printable
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