With the wide spread of Coronavirus, most people who infected with the COVID‐19, will recover without requiring special treatment. Whereas, elders and those with underlying medical problems are more likely to have serious illnesses, even be threatened with death. Many more disciplines try to find solutions and drive master plan to this global trouble. Consequently, by taking one particular population, Hungary, this study aims to explore a pattern of COVID‐19 victims, who suffered from some underlying conditions. Age, gender, and underlying medical problems form the structure of the clustering. K‐Means and two step clustering methods were applied for age‐based and age‐independent analysis. Grouping of the deaths in the form of two different scenarios may highlight some concepts of this deadly disease for public health professionals. Our result for clustering can forecast similar cases which are assigned to any cluster that it will be a serious cautious for the population.

Sovereign debt ratings provided by rating agencies measure the solvency of a country, as gauged by a lender or an investor. It is an indication of the risk involved in investment and should be determined correctly and in a well-timed manner. The current system is lacking transparency of rating criteria and mechanism. The present study reconstructs sovereign debt ratings through logical analysis of data (LAD), which is based on the theory of Boolean functions. It organizes groups of countries according to 20 World Bank-defined variables for the period 2012–2015. The Fitch Rating Agency, one of the three big global rating agencies, is used as a case study. An approximate algorithm was crucial in exploring the rating method, in correcting the agency’s errors, and in determining the estimated rating of otherwise unrated countries. The outcome was a decision tree for each year. Each country was assigned a rating. On average, the algorithm reached almost 98% matched ratings in the training set and was verified by 84% in the test set.

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Credit ratings, represent the creditworthiness of countries and financial organizations that nowadays due to the corona virus crisis hit the world is being threatened to be downgraded. This study uses Logical Analysis of Data to analyze the Fitch rating agency response to Covid-19. Three varied parts of variables, composed of the significant economic and social factors, pandemic -related variables and pre-credit rating (2019) are under survey. The time interval of the study is 2019-2020. The output of the research in the form of the decision trees shows the selected patterns of each newly published Fitch rating in July of 2020. The consequences of the research in training and test sets by 100% and 80% matched cases, respectively shed light on the robust results of explored patterns. Surveying on Fitch`s response in this span showed that pandemic-related variables mostly have an impact on “B” classes and they were not significant in “investment grades” (AAA-BBBP), whereas, 2019`s credit rating may be a strong factor to forecast next ratings just in normal state of affairs, nevertheless, selected well-built economic and social factors described the hidden structure of Fitch Agency in the optimum way during pandemic also.