AI Summary & Key Takeaways
AI InsightWhile developed countries are expected to be more effective in combating the epidemic, it was observed that they showed similarities with underdeveloped countries.
📌 3 Key Takeaways
Özet
In this study it is aimed to analyze the similarities of 50 countries where coronavirus pandemic, which has been profoundly affecting the whole world socially, psychologically and economically, was mostly seen. The similarities of the countries were investigated with Hierarchical Cluster Analysis and Multi-dimensional Scaling Analysis, which are among multivariate statistical analysis techniques in terms of coronavirus pandemic indicators. The variables used in the analysis are death rate, recovery rate, active rate, serious case rate, case rate per 1 million, death rate per 1 million, and test rate per 1 million. As a result of Hierarchical Cluster Analysis, the countries were divided into seven clusters. In the two-dimensional projections of Multidimensional Scaling, Kruskal stress statistics was found as 0,00001. According to this, a complete compatibility was found between data distances and configuration distances. Also, the fact that R2 is 1,00000 shows that the model is quite powerful. As a result of the study, the results of both methods were found to be very close to each other. In the same subgroup, Turkey; Peru, Poland, Panama, Romania, Netherlands and Kazakhstan take place. In the study; both developed and underdeveloped countries were found to be in the same cluster. This is a surprising situation. While developed countries are expected to be more effective in combating the epidemic, it was observed that they showed similarities with underdeveloped countries.