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

M. S. Panwar | Zendy; Chandra Prakash Yadav | Zendy; Harendra Pal Singh | Zendy; Taghreed M. Jawa | Zendy; Neveen Sayed-Ahmed | Zendy

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

Author(s) -

M. S. Panwar,

Chandra Prakash Yadav,

Harendra Pal Singh,

Taghreed M. Jawa,

Neveen Sayed-Ahmed

Publication year - 2022

Publication title -

computational intelligence and neuroscience

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.605

H-Index - 52

eISSN - 1687-5273

pISSN - 1687-5265

DOI - 10.1155/2022/3538866

Subject(s) - covariate , covid-19 , growth curve (statistics) , pandemic , econometrics , latent growth modeling , growth model , statistics , structural equation modeling , environmental science , mathematics , biology , medicine , virology , disease , mathematical economics , outbreak , infectious disease (medical specialty)

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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