Ankaralı, HandanUslu, ÜnalAnkaralı, SeyitCangür, Şengül2023-07-262023-07-2620221995-72621995-7270https://doi.org/10.4314/mmj.v34i2.2https://hdl.handle.net/20.500.12684/12771Background This study is aimed at evaluating the relationship between the number of days elapsed since a country's first case(s) of coronavirus disease 2019 (COVID-19), the total number of tests conducted, and outbreak indicators such as the total numbers of cases, deaths, and patients who recovered. The study compares COVID-19 indicators among countries and clusters them according to similarities in the indicators. Methods Descriptive statistics of the indicators were computed and the results were presented in figures and tables. A fuzzy c-means clustering algorithm was used to cluster/group the countries according to the similarities in the total numbers of patients who recovered, deaths, and active cases. Results The highest numbers of COVID-19 cases were found in Gibraltar, Spain, Switzerland, Liechtenstein and Italy were also of that order with about 1500 cases per million population. Spain and Italy had the highest total number of deaths, which were about 140 and 165 per million population, respectively. In Japan, where exposure to the causative virus was longer than in most other countries, the total number of deaths per million population was less than 0.5. According to cluster analysis, the total numbers of deaths, patients who recovered, and active cases were higher in Western countries, especially in central and southern European countries, which had the highest numbers when compared with other countries. Conclusion There may be various reasons for the differences between the clusters obtained by fuzzy c-means clustering. These include quarantine measures, climatic conditions, economic levels, health policies, and the duration of the fight against the outbreak.en10.4314/mmj.v34i2.2info:eu-repo/semantics/openAccessCovid-19; Total Number Of Cases; Total Number Of Deaths; Outbreak; ClusteringFuzzyArticle3427386359918172-s2.0-85135251222WOS:000863008400002Q3Q4