Using Intuitionistic Fuzzy C-Means Clustering Algorithms to Model Covid-19 Cases for Countries in the Worldwide

Abstract

Every day, the number of newly confirmed cases of coronavirus (COVID-19) rises in many countries. It is critical to adjust policies and plans in order to investigate the relationships between the distributions of the spread of this virus in other countries. During this study, the intuitionistic fuzzy c-means (IFCM) clustering method is used to compare and cluster the distributions of COVID-19 spread in 62 countries. Using the IFCM clustering algorithm, the study aims to cluster the countries that use environmental, economic, social, health, and related measurements that affect disease spread to implement policies that regulate disease spread. As a result, countries that have similar factors can take proactive measures to address the pandemic. The data are obtained for 62 countries, and six different feature variables (factors associated with the spread of COVID-19) are determined. The data are obtained for 62 countries, and six variables with different characteristics (linked to the spread of COVID-19) are identified. In this study, the IFCM clustering algorithm is used to determine the dynamic behavior of COVID-19 based on real-world data for multiple countries and Turkey around the world. Data analysis is performed through MATLAB 2018a and R programs. The clustering results revealed that the distribution of dissemination in Brazil, India, and the United States was nearly identical and distinct from that of the 59 other countries.