Assessment of the dissimilarities of totally 186 countries and regions according to COVID-19 indicators at the end of March 2020

  • Handan Ankarali Department of Biostatistics and Medical Informatics, Faculty of Medicine, Istanbul Medeniyet University, Turkey
  • Unal Uslu Department of Histology and Embryology, Faculty of Medicine, Istanbul Medeniyet University, Turkey
  • Seyit Ankarali Department of Physiology, Faculty of Medicine, Istanbul Medeniyet University, Turkey
  • Sengul Cangur Department of Biostatistics and Medical Informatics, Faculty of Medicine, Duzce University, Turkey
Keywords: COVID-19, total number of cases, total number of deaths, outbreak, clustering


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.
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.
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.
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

Original Research

Journal Identifiers

eISSN: 1995-7262
print ISSN: 1995-7262