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Impact of COVID-19 on G20 countries: analysis of economic recession using data mining approaches
The G20 countries are the locomotives of economic growth, representing 64% of the global population and including 4.7 billion inhabitants. As a monetary and market value index, real gross domestic product (GDP) is affected by several factors and reflects the economic development of countries. This s...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Springer Berlin Heidelberg
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9441845/ https://www.ncbi.nlm.nih.gov/pubmed/36091580 http://dx.doi.org/10.1186/s40854-022-00385-y |
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author | Taylan, Osman Alkabaa, Abdulaziz S. Yılmaz, Mustafa Tahsin |
author_facet | Taylan, Osman Alkabaa, Abdulaziz S. Yılmaz, Mustafa Tahsin |
author_sort | Taylan, Osman |
collection | PubMed |
description | The G20 countries are the locomotives of economic growth, representing 64% of the global population and including 4.7 billion inhabitants. As a monetary and market value index, real gross domestic product (GDP) is affected by several factors and reflects the economic development of countries. This study aimed to reveal the hidden economic patterns of G20 countries, study the complexity of related economic factors, and analyze the economic reactions taken by policymakers during the coronavirus disease of 2019 (COVID-19) pandemic recession (2019–2020). In this respect, this study employed data-mining techniques of nonparametric classification tree and hierarchical clustering approaches to consider factors such as GDP/capita, industrial production, government spending, COVID-19 cases/population, patient recovery, COVID-19 death cases, number of hospital beds/1000 people, and percentage of the vaccinated population to identify clusters for G20 countries. The clustering approach can help policymakers measure economic indices in terms of the factors considered to identify the specific focus of influences on economic development. The results exhibited significant findings for the economic effects of the COVID-19 pandemic on G20 countries, splitting them into three clusters by sharing different measurements and patterns (harmonies and variances across G20 countries). A comprehensive statistical analysis was performed to analyze endogenous and exogenous factors. Similarly, the classification and regression tree method was applied to predict the associations between the response and independent factors to split the G-20 countries into different groups and analyze the economic recession. Variables such as GDP per capita and patient recovery of COVID-19 cases with values of $12,012 and 82.8%, respectively, were the most significant factors for clustering the G20 countries, with a correlation coefficient (R2) of 91.8%. The results and findings offer some crucial recommendations to handle pandemics in terms of the suggested economic systems by identifying the challenges that the G20 countries have experienced. |
format | Online Article Text |
id | pubmed-9441845 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-94418452022-09-06 Impact of COVID-19 on G20 countries: analysis of economic recession using data mining approaches Taylan, Osman Alkabaa, Abdulaziz S. Yılmaz, Mustafa Tahsin Financ Innov Research The G20 countries are the locomotives of economic growth, representing 64% of the global population and including 4.7 billion inhabitants. As a monetary and market value index, real gross domestic product (GDP) is affected by several factors and reflects the economic development of countries. This study aimed to reveal the hidden economic patterns of G20 countries, study the complexity of related economic factors, and analyze the economic reactions taken by policymakers during the coronavirus disease of 2019 (COVID-19) pandemic recession (2019–2020). In this respect, this study employed data-mining techniques of nonparametric classification tree and hierarchical clustering approaches to consider factors such as GDP/capita, industrial production, government spending, COVID-19 cases/population, patient recovery, COVID-19 death cases, number of hospital beds/1000 people, and percentage of the vaccinated population to identify clusters for G20 countries. The clustering approach can help policymakers measure economic indices in terms of the factors considered to identify the specific focus of influences on economic development. The results exhibited significant findings for the economic effects of the COVID-19 pandemic on G20 countries, splitting them into three clusters by sharing different measurements and patterns (harmonies and variances across G20 countries). A comprehensive statistical analysis was performed to analyze endogenous and exogenous factors. Similarly, the classification and regression tree method was applied to predict the associations between the response and independent factors to split the G-20 countries into different groups and analyze the economic recession. Variables such as GDP per capita and patient recovery of COVID-19 cases with values of $12,012 and 82.8%, respectively, were the most significant factors for clustering the G20 countries, with a correlation coefficient (R2) of 91.8%. The results and findings offer some crucial recommendations to handle pandemics in terms of the suggested economic systems by identifying the challenges that the G20 countries have experienced. Springer Berlin Heidelberg 2022-09-05 2022 /pmc/articles/PMC9441845/ /pubmed/36091580 http://dx.doi.org/10.1186/s40854-022-00385-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Taylan, Osman Alkabaa, Abdulaziz S. Yılmaz, Mustafa Tahsin Impact of COVID-19 on G20 countries: analysis of economic recession using data mining approaches |
title | Impact of COVID-19 on G20 countries: analysis of economic recession using data mining approaches |
title_full | Impact of COVID-19 on G20 countries: analysis of economic recession using data mining approaches |
title_fullStr | Impact of COVID-19 on G20 countries: analysis of economic recession using data mining approaches |
title_full_unstemmed | Impact of COVID-19 on G20 countries: analysis of economic recession using data mining approaches |
title_short | Impact of COVID-19 on G20 countries: analysis of economic recession using data mining approaches |
title_sort | impact of covid-19 on g20 countries: analysis of economic recession using data mining approaches |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9441845/ https://www.ncbi.nlm.nih.gov/pubmed/36091580 http://dx.doi.org/10.1186/s40854-022-00385-y |
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