Examining the Data of Covid-19 Virus with Factor Analysis by Country

Abstract

Factor analysis is a multivariate statistical method that aims to obtain a small number of new variables by combining many interrelated variables. This study aims to determine which countries are associated with each other in terms of COVID-19 disease and which country is under which factor with a data set consisting of daily case numbers. Covid-19 data covering 16 countries for 201 days obtained from Github was used as the material in the study. Factor analysis was performed by applying Principal components and Varimax rotation. With the KMO test, it was determined that the data set was suitable for factor analysis and with the principal component approach, sixteen original variables were reduced to four factors with a loss of approximately 24%. After the Varimax rotation process, it was determined which variable (country) under which factor. According to the results, the USA (35393.14) is the first and China (47.64) is the last (sixteenth) in terms of the number of daily cases. As a result, it can be said that countries with similar characteristics in terms of the number of daily cases are under the same factor and therefore, the factors affecting the virus or the methods of combating the virus in these countries are close to each other. In general, countries in the fourth factor are more successful in combating the virus, and other countries can achieve more successful results by using similar methods.