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A predictive internet-based model for COVID-19 hospitalization census
The COVID-19 pandemic has strained hospital resources and necessitated the need for predictive models to forecast patient care demands in order to allow for adequate staffing and resource allocation. Recently, other studies have looked at associations between Google Trends data and the number of COV...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7930254/ https://www.ncbi.nlm.nih.gov/pubmed/33658529 http://dx.doi.org/10.1038/s41598-021-84091-2 |
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author | Turk, Philip J. Tran, Thao P. Rose, Geoffrey A. McWilliams, Andrew |
author_facet | Turk, Philip J. Tran, Thao P. Rose, Geoffrey A. McWilliams, Andrew |
author_sort | Turk, Philip J. |
collection | PubMed |
description | The COVID-19 pandemic has strained hospital resources and necessitated the need for predictive models to forecast patient care demands in order to allow for adequate staffing and resource allocation. Recently, other studies have looked at associations between Google Trends data and the number of COVID-19 cases. Expanding on this approach, we propose a vector error correction model (VECM) for the number of COVID-19 patients in a healthcare system (Census) that incorporates Google search term activity and healthcare chatbot scores. The VECM provided a good fit to Census and very good forecasting performance as assessed by hypothesis tests and mean absolute percentage prediction error. Although our study and model have limitations, we have conducted a broad and insightful search for candidate Internet variables and employed rigorous statistical methods. We have demonstrated the VECM can potentially be a valuable component to a COVID-19 surveillance program in a healthcare system. |
format | Online Article Text |
id | pubmed-7930254 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-79302542021-03-05 A predictive internet-based model for COVID-19 hospitalization census Turk, Philip J. Tran, Thao P. Rose, Geoffrey A. McWilliams, Andrew Sci Rep Article The COVID-19 pandemic has strained hospital resources and necessitated the need for predictive models to forecast patient care demands in order to allow for adequate staffing and resource allocation. Recently, other studies have looked at associations between Google Trends data and the number of COVID-19 cases. Expanding on this approach, we propose a vector error correction model (VECM) for the number of COVID-19 patients in a healthcare system (Census) that incorporates Google search term activity and healthcare chatbot scores. The VECM provided a good fit to Census and very good forecasting performance as assessed by hypothesis tests and mean absolute percentage prediction error. Although our study and model have limitations, we have conducted a broad and insightful search for candidate Internet variables and employed rigorous statistical methods. We have demonstrated the VECM can potentially be a valuable component to a COVID-19 surveillance program in a healthcare system. Nature Publishing Group UK 2021-03-03 /pmc/articles/PMC7930254/ /pubmed/33658529 http://dx.doi.org/10.1038/s41598-021-84091-2 Text en © The Author(s) 2021, corrected publication 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Turk, Philip J. Tran, Thao P. Rose, Geoffrey A. McWilliams, Andrew A predictive internet-based model for COVID-19 hospitalization census |
title | A predictive internet-based model for COVID-19 hospitalization census |
title_full | A predictive internet-based model for COVID-19 hospitalization census |
title_fullStr | A predictive internet-based model for COVID-19 hospitalization census |
title_full_unstemmed | A predictive internet-based model for COVID-19 hospitalization census |
title_short | A predictive internet-based model for COVID-19 hospitalization census |
title_sort | predictive internet-based model for covid-19 hospitalization census |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7930254/ https://www.ncbi.nlm.nih.gov/pubmed/33658529 http://dx.doi.org/10.1038/s41598-021-84091-2 |
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