A predictive internet-based model for COVID-19 hospitalization census

Abstract 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 numb...

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Autores principales:	Philip J. Turk, Thao P. Tran, Geoffrey A. Rose, Andrew McWilliams
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0b9a79667419436aa889c369618e0cf4
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id	oai:doaj.org-article:0b9a79667419436aa889c369618e0cf4
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spelling	oai:doaj.org-article:0b9a79667419436aa889c369618e0cf42021-12-02T15:54:01ZA predictive internet-based model for COVID-19 hospitalization census10.1038/s41598-021-84091-22045-2322https://doaj.org/article/0b9a79667419436aa889c369618e0cf42021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84091-2https://doaj.org/toc/2045-2322Abstract 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.Philip J. TurkThao P. TranGeoffrey A. RoseAndrew McWilliamsNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Philip J. Turk Thao P. Tran Geoffrey A. Rose Andrew McWilliams A predictive internet-based model for COVID-19 hospitalization census
description	Abstract 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	article
author	Philip J. Turk Thao P. Tran Geoffrey A. Rose Andrew McWilliams
author_facet	Philip J. Turk Thao P. Tran Geoffrey A. Rose Andrew McWilliams
author_sort	Philip J. Turk
title	A predictive internet-based model for COVID-19 hospitalization census
title_short	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_sort	predictive internet-based model for covid-19 hospitalization census
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/0b9a79667419436aa889c369618e0cf4
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A predictive internet-based model for COVID-19 hospitalization census

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