Latent Growth Curve Modeling for COVID-19 Cases in Presence of Time-Variant Covariate

For the past two years, the entire world has been fighting against the COVID-19 pandemic. The rapid increase in COVID-19 cases can be attributed to several factors. Recent studies have revealed that changes in environmental temperature are associated with the growth of cases. In this study, we model...

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Autores principales: Panwar, M. S., Yadav, C. P., Singh, Harendra, Jawa, Taghreed M., Sayed-Ahmed, Neveen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8881161/
https://www.ncbi.nlm.nih.gov/pubmed/35222625
http://dx.doi.org/10.1155/2022/3538866
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author Panwar, M. S.
Yadav, C. P.
Singh, Harendra
Jawa, Taghreed M.
Sayed-Ahmed, Neveen
author_facet Panwar, M. S.
Yadav, C. P.
Singh, Harendra
Jawa, Taghreed M.
Sayed-Ahmed, Neveen
author_sort Panwar, M. S.
collection PubMed
description For the past two years, the entire world has been fighting against the COVID-19 pandemic. The rapid increase in COVID-19 cases can be attributed to several factors. Recent studies have revealed that changes in environmental temperature are associated with the growth of cases. In this study, we modeled the monthly growth rate of COVID-19 cases per million infected in 126 countries using various growth curves under structural equation modeling. Moreover, the environmental temperature has been introduced as a time-varying covariate to enhance the performance of the models. The parameters of growth curve models have been estimated, and accordingly, the results are discussed for the affected countries from August 2020 to July 2021.
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spelling pubmed-88811612022-02-26 Latent Growth Curve Modeling for COVID-19 Cases in Presence of Time-Variant Covariate Panwar, M. S. Yadav, C. P. Singh, Harendra Jawa, Taghreed M. Sayed-Ahmed, Neveen Comput Intell Neurosci Research Article For the past two years, the entire world has been fighting against the COVID-19 pandemic. The rapid increase in COVID-19 cases can be attributed to several factors. Recent studies have revealed that changes in environmental temperature are associated with the growth of cases. In this study, we modeled the monthly growth rate of COVID-19 cases per million infected in 126 countries using various growth curves under structural equation modeling. Moreover, the environmental temperature has been introduced as a time-varying covariate to enhance the performance of the models. The parameters of growth curve models have been estimated, and accordingly, the results are discussed for the affected countries from August 2020 to July 2021. Hindawi 2022-02-18 /pmc/articles/PMC8881161/ /pubmed/35222625 http://dx.doi.org/10.1155/2022/3538866 Text en Copyright © 2022 M. S. Panwar et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Panwar, M. S.
Yadav, C. P.
Singh, Harendra
Jawa, Taghreed M.
Sayed-Ahmed, Neveen
Latent Growth Curve Modeling for COVID-19 Cases in Presence of Time-Variant Covariate
title Latent Growth Curve Modeling for COVID-19 Cases in Presence of Time-Variant Covariate
title_full Latent Growth Curve Modeling for COVID-19 Cases in Presence of Time-Variant Covariate
title_fullStr Latent Growth Curve Modeling for COVID-19 Cases in Presence of Time-Variant Covariate
title_full_unstemmed Latent Growth Curve Modeling for COVID-19 Cases in Presence of Time-Variant Covariate
title_short Latent Growth Curve Modeling for COVID-19 Cases in Presence of Time-Variant Covariate
title_sort latent growth curve modeling for covid-19 cases in presence of time-variant covariate
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8881161/
https://www.ncbi.nlm.nih.gov/pubmed/35222625
http://dx.doi.org/10.1155/2022/3538866
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