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Transfer Function Model for COVID-19 Deaths in USA Using Case Counts as Input Series

This paper presents a transfer function time series forecast model for COVID-19 deaths using reported COVID-19 case positivity counts as the input series. We have used deaths and case counts data reported by the Center for Disease Control for the USA from July 24 to December 31, 2021. To demonstrate...

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Detalles Bibliográficos
Autores principales: Shahela, Fahmida Akter, Uddin, Nizam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Nature Singapore 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9199477/
https://www.ncbi.nlm.nih.gov/pubmed/35729955
http://dx.doi.org/10.1007/s40840-022-01332-x
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author Shahela, Fahmida Akter
Uddin, Nizam
author_facet Shahela, Fahmida Akter
Uddin, Nizam
author_sort Shahela, Fahmida Akter
collection PubMed
description This paper presents a transfer function time series forecast model for COVID-19 deaths using reported COVID-19 case positivity counts as the input series. We have used deaths and case counts data reported by the Center for Disease Control for the USA from July 24 to December 31, 2021. To demonstrate the effectiveness of the proposed transfer function methodology, we have compared some summary results of forecast errors of the fitted transfer function model to those of an adequate autoregressive integrated moving average model and observed that the transfer function model achieved better forecast results than the autoregressive integrated moving average model. Additionally, separate autoregressive integrated moving average models for COVID-19 cases and deaths are also reported.
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spelling pubmed-91994772022-06-17 Transfer Function Model for COVID-19 Deaths in USA Using Case Counts as Input Series Shahela, Fahmida Akter Uddin, Nizam Bull Malays Math Sci Soc Article This paper presents a transfer function time series forecast model for COVID-19 deaths using reported COVID-19 case positivity counts as the input series. We have used deaths and case counts data reported by the Center for Disease Control for the USA from July 24 to December 31, 2021. To demonstrate the effectiveness of the proposed transfer function methodology, we have compared some summary results of forecast errors of the fitted transfer function model to those of an adequate autoregressive integrated moving average model and observed that the transfer function model achieved better forecast results than the autoregressive integrated moving average model. Additionally, separate autoregressive integrated moving average models for COVID-19 cases and deaths are also reported. Springer Nature Singapore 2022-06-15 2022 /pmc/articles/PMC9199477/ /pubmed/35729955 http://dx.doi.org/10.1007/s40840-022-01332-x Text en © The Author(s), under exclusive licence to Malaysian Mathematical Sciences Society and Penerbit Universiti Sains Malaysia 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Shahela, Fahmida Akter
Uddin, Nizam
Transfer Function Model for COVID-19 Deaths in USA Using Case Counts as Input Series
title Transfer Function Model for COVID-19 Deaths in USA Using Case Counts as Input Series
title_full Transfer Function Model for COVID-19 Deaths in USA Using Case Counts as Input Series
title_fullStr Transfer Function Model for COVID-19 Deaths in USA Using Case Counts as Input Series
title_full_unstemmed Transfer Function Model for COVID-19 Deaths in USA Using Case Counts as Input Series
title_short Transfer Function Model for COVID-19 Deaths in USA Using Case Counts as Input Series
title_sort transfer function model for covid-19 deaths in usa using case counts as input series
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9199477/
https://www.ncbi.nlm.nih.gov/pubmed/35729955
http://dx.doi.org/10.1007/s40840-022-01332-x
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