Objective: It was aimed to be obtained descriptive values with respect to the outbreak time course, demographic structure, and symptom distribution by the help of case-based data, and to be compared countries by being grouped according to their similarities of outbreak indicators.
Methods: The data were obtained from open-access database. Univariate tests and cluster analysis were used to analyze the data.
Results: After the symptoms onset, the prolonged admission to the hospital significantly increases the risk of death. The average age and percentage of the male gender of the deceased cases were found to be significantly higher. In addition, the symptoms including fever, throat complaints, and dyspnea were determined in 70%. Countries were divided into four clusters according to their similarities in terms of three outbreak indicators. The differences among the clusters with regard to mean age, urban rate, and average of the outbreak indicators were found significant.
Conclusion: Delaying treatment from the moment the symptoms appear will increase the risk of death and the average time to recovery or death was 2.5 weeks. It can be stated that the most important measure is to focus on methods that can detect the cases before symptoms. The indicators that have a very important role in defining the pandemic are also related to each other. Therefore, multivariate methods, which take these relationships into account, are able to produce more accurate information in determining the similarities of countries.
Keywords: Coronavirus; coronavirus disease 2019; expectation maximization clustering; outbreak indicators; pandemic.
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