Taylan Osman, Alkabaa Abdulaziz S, Yılmaz Mustafa Tahsin
Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, P.O. Box 80204, Jeddah, 21589 Saudi Arabia.
Financ Innov. 2022;8(1):81. doi: 10.1186/s40854-022-00385-y. Epub 2022 Sep 5.
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 (2) 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.
二十国集团（G20）国家是经济增长的火车头，占全球人口的64%，涵盖47亿居民。实际国内生产总值（GDP）作为一种货币和市场价值指标，受到多种因素影响，反映了各国的经济发展情况。本研究旨在揭示G20国家隐藏的经济模式，研究相关经济因素的复杂性，并分析政策制定者在2019年冠状病毒病（COVID-19）大流行衰退（2019 - 2020年）期间采取的经济应对措施。在这方面，本研究采用非参数分类树和层次聚类方法的数据挖掘技术，考虑人均国内生产总值、工业生产、政府支出、COVID-19病例/人口、患者康复情况、COVID-19死亡病例、每千人医院床位数以及接种疫苗人口百分比等因素，为G20国家识别聚类。聚类方法可帮助政策制定者根据所考虑的因素衡量经济指标，以确定对经济发展影响的具体重点。结果显示了COVID-19大流行对G20国家经济影响的显著发现，通过共享不同的度量和模式（G20国家之间的协调性和差异）将它们分为三个聚类。进行了全面的统计分析以分析内生和外生因素。同样，应用分类和回归树方法预测响应因素与独立因素之间的关联，将G20国家分为不同组并分析经济衰退情况。人均国内生产总值和COVID-19病例患者康复率分别为12,012美元和82.8%等变量，是对G20国家进行聚类的最显著因素，相关系数（2）为91.8%。研究结果针对G20国家所经历的挑战，就建议的经济体系在应对大流行方面提供了一些关键建议。
