COVID-19 Risk Factors, Economic Factors, and Epidemiological Factors nexus on Economic Impact: Machine Learning and Structural Equation Modelling Approaches

Since the declaration of COVID-19 as a global pandemic, it has been transmitted to more than 200 nations of the world. The harmful impact of the pandemic on the economy of nations is far greater than anything suffered in almost a century. The main objective of this paper is to apply Structural Equa...

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Autores principales:	David Opeoluwa Oyewola, Emmanuel Gbenga Dada, Juliana Ngozi Ndunagu, Terrang Abubakar Umar, Akinwunmi S.A
Formato:	article
Lenguaje:	EN
Publicado:	Nigerian Society of Physical Sciences 2021
Materias:	COVID-19 Structural Equation Modelling, Latent variables, Random forest, Boosting. Physics QC1-999
Acceso en línea:	https://doaj.org/article/badb5590780846a8bb68ad3f16f0d2ff
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spelling	oai:doaj.org-article:badb5590780846a8bb68ad3f16f0d2ff2021-11-30T12:19:17ZCOVID-19 Risk Factors, Economic Factors, and Epidemiological Factors nexus on Economic Impact: Machine Learning and Structural Equation Modelling Approaches10.46481/jnsps.2021.1732714-28172714-4704https://doaj.org/article/badb5590780846a8bb68ad3f16f0d2ff2021-11-01T00:00:00Zhttps://journal.nsps.org.ng/index.php/jnsps/article/view/173https://doaj.org/toc/2714-2817https://doaj.org/toc/2714-4704 Since the declaration of COVID-19 as a global pandemic, it has been transmitted to more than 200 nations of the world. The harmful impact of the pandemic on the economy of nations is far greater than anything suffered in almost a century. The main objective of this paper is to apply Structural Equation Modeling (SEM) and Machine Learning (ML) to determine the relationships among COVID-19 risk factors, epidemiology factors and economic factors. Structural equation modeling is a statistical technique for calculating and evaluating the relationships of manifest and latent variables. It explores the causal relationship between variables and at the same time taking measurement error into account. Bagging (BAG), Boosting (BST), Support Vector Machine (SVM), Decision Tree (DT) and Random Forest (RF) Machine Learning techniques was applied to predict the impact of COVID-19 risk factors. Data from patients who came into contact with coronavirus disease were collected from Kaggle database between 23 January 2020 and 24 June 2020. Results indicate that COVID-19 risk factors have negative effects on epidemiology factors. It also has negative effects on economic factors. David Opeoluwa OyewolaEmmanuel Gbenga DadaJuliana Ngozi Ndunagu Terrang Abubakar UmarAkinwunmi S.ANigerian Society of Physical SciencesarticleCOVID-19 Structural Equation Modelling, Latent variables, Random forest, Boosting.PhysicsQC1-999ENJournal of Nigerian Society of Physical Sciences, Vol 3, Iss 4 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	COVID-19 Structural Equation Modelling, Latent variables, Random forest, Boosting. Physics QC1-999
spellingShingle	COVID-19 Structural Equation Modelling, Latent variables, Random forest, Boosting. Physics QC1-999 David Opeoluwa Oyewola Emmanuel Gbenga Dada Juliana Ngozi Ndunagu Terrang Abubakar Umar Akinwunmi S.A COVID-19 Risk Factors, Economic Factors, and Epidemiological Factors nexus on Economic Impact: Machine Learning and Structural Equation Modelling Approaches
description	Since the declaration of COVID-19 as a global pandemic, it has been transmitted to more than 200 nations of the world. The harmful impact of the pandemic on the economy of nations is far greater than anything suffered in almost a century. The main objective of this paper is to apply Structural Equation Modeling (SEM) and Machine Learning (ML) to determine the relationships among COVID-19 risk factors, epidemiology factors and economic factors. Structural equation modeling is a statistical technique for calculating and evaluating the relationships of manifest and latent variables. It explores the causal relationship between variables and at the same time taking measurement error into account. Bagging (BAG), Boosting (BST), Support Vector Machine (SVM), Decision Tree (DT) and Random Forest (RF) Machine Learning techniques was applied to predict the impact of COVID-19 risk factors. Data from patients who came into contact with coronavirus disease were collected from Kaggle database between 23 January 2020 and 24 June 2020. Results indicate that COVID-19 risk factors have negative effects on epidemiology factors. It also has negative effects on economic factors.
format	article
author	David Opeoluwa Oyewola Emmanuel Gbenga Dada Juliana Ngozi Ndunagu Terrang Abubakar Umar Akinwunmi S.A
author_facet	David Opeoluwa Oyewola Emmanuel Gbenga Dada Juliana Ngozi Ndunagu Terrang Abubakar Umar Akinwunmi S.A
author_sort	David Opeoluwa Oyewola
title	COVID-19 Risk Factors, Economic Factors, and Epidemiological Factors nexus on Economic Impact: Machine Learning and Structural Equation Modelling Approaches
title_short	COVID-19 Risk Factors, Economic Factors, and Epidemiological Factors nexus on Economic Impact: Machine Learning and Structural Equation Modelling Approaches
title_full	COVID-19 Risk Factors, Economic Factors, and Epidemiological Factors nexus on Economic Impact: Machine Learning and Structural Equation Modelling Approaches
title_fullStr	COVID-19 Risk Factors, Economic Factors, and Epidemiological Factors nexus on Economic Impact: Machine Learning and Structural Equation Modelling Approaches
title_full_unstemmed	COVID-19 Risk Factors, Economic Factors, and Epidemiological Factors nexus on Economic Impact: Machine Learning and Structural Equation Modelling Approaches
title_sort	covid-19 risk factors, economic factors, and epidemiological factors nexus on economic impact: machine learning and structural equation modelling approaches
publisher	Nigerian Society of Physical Sciences
publishDate	2021
url	https://doaj.org/article/badb5590780846a8bb68ad3f16f0d2ff
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_version_	1718406647330635776

COVID-19 Risk Factors, Economic Factors, and Epidemiological Factors nexus on Economic Impact: Machine Learning and Structural Equation Modelling Approaches

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