CREATIVE ACCOUNTING PRACTICIES IN SLOVAKIA AFTER PASSING FINANCIAL CRISIS

COVID-19 remains a global health challenge, with severe cases often leading to complications and fatalities. The objective of this study was to assess supervised machine learning algorithms for predicting severe COVID-19 based on demographic, clinical, biochemical, and genetic variables, with the aim of identifying the most informative prognostic markers. For Machine Learning (ML) analysis, we utilized a dataset comprising 226 observations with 68 clinical, biochemical, and genetic features collected from 226 patients with confirmed COVID-19 (54—moderate, 142—severe and 30 with mild disease). The target variable was disease severity (mild, moderate, severe). The feature set included demographic variables (age, sex), genetic markers (single-nucleotide polymorphisms (SNPs) in FGB (rs1800790), NOS3 (rs2070744), and TMPRSS2 (rs12329760)), biochemical indicators (IL-6, endothelin-1, D-dimer, fibrinogen, among others), and clinical parameters (blood pressure, body mass index, comorbidities). The target variable was disease severity. To identify the most effective predictive models for COVID-19 severity, we systematically evaluated multiple supervised learning algorithms, including logistic regression, k-nearest neighbors, decision trees, random forest, gradient boosting, bagging, naïve Bayes, and support vector machines. Model performance was assessed using accuracy and the area under the receiver operating characteristic curve (AUC-ROC). Among the predictors, IL-6, presence of depression/pneumonia, LDL cholesterol, AST, platelet count, lymphocyte count, and ALT showed the strongest correlations with severity. The highest predictive accuracy, with negligible error rates, was achieved by ensemble-based models such as ExtraTreesClassifier, HistGradientBoostingClassifier, BaggingClassifier, and GradientBoostingClassifier. Notably, decision tree models demonstrated high classification precision at terminal nodes, many of which yielded a 100% probability for a specific severity class.

The purpose of this study is to investigate the potential for restructuring industrial areas toward tourism development within local communities, with a special emphasis on the socio-cultural determinants of residents, as well as their attitudes regarding the sustainable development of tourism. The research is also oriented toward the interests of local communities with respect to entrepreneurial activities in the field of tourism within regions relying on traditional industries, in this case, one of the largest open-pit mining surfaces in Europe (near the Serbian town of Lazarevac). The survey was conducted on a sample of 273 respondents. The research results point to the residents’ attitudes regarding the acceptability of tourism development options, as well as their attitudes toward tourism development, with the aim of providing the conditions for a successful transition from a typical heavy industrial setting toward sustainable tourism development.

The paper proposed a modification of the GeoSER(D) model previously developed by us by detailing the age structure of the population, personal schedule on weekdays and working days, and individual health characteristics of the agents, this made it possible to build a more realistic model of the functioning of the city and its residents. The developed model made it possible to simulate the spread of 3 types of the strain of the COVID-19 virus, and to analyze the adequacy of this model in the case of unhindered spread of the virus among city residents. The paper showed that SARS COV 2 spreads mainly from contacts in workplaces and transport, and schoolchildren and preschool children are the consequence, not the initiator of the epidemic. Fluctuations in the dynamics of various indicators of the spread of SARS COV 2 associated with the difference in the daily schedule on weekdays and weekends. It has been shown that people's daily schedules strongly influence the spread of SARS COV 2. For the more contagious "rapid" strains of SARS COV 2 (omicron), immunocompetent people become a significant source of infection. For the less contagious "slow strains" (alpha) of SARS COV 2, the most active source of infection is immunocompromised individuals (pregnant women). The more contagious – "fast" strain of the SARS COV 2 virus (omicron) spreads faster in public transport. For less contagious – "slow" strains of the virus (alpha), the greatest infection occurs due to work and educational contacts.

The work is devoted to studying SARS-CoV-2-associated pneumonia and the investigating of the main indicators that lead to the patients’ mortality. Using the good-known parameters that are routinely embraced in clinical practice, we obtained new functional dependencies based on an accessible and understandable decision tree and ML ensemble of classifiers models that would allow the physician to determine the prognosis in a few minutes and, accordingly, to understand the need for treatment adjustment, transfer of the patient to the emergency department. The accuracy of the resulting ensemble of models fitted on actual hospital patient data was in the range of 0.88–0.91 for different metrics. Creating a data collection system with further training of classifiers will dynamically increase the forecast’s accuracy and automate the doctor’s decision-making process.

This study proposes a modification of the GeoCity model previously developed by the authors, detailing the age structure of the population, personal schedule on weekdays and working days, and individual health characteristics of the agents. This made it possible to build a more realistic model of the functioning of the city and its residents. The developed model made it possible to simulate the spread of three types of strain of the COVID-19 virus, and to analyze the adequacy of this model in the case of unhindered spread of the virus among city residents. Calculations based on the proposed model show that SARS-CoV 2 spreads mainly from contacts in workplaces and transport, and schoolchildren and preschool children are the recipients, not the initiators of the epidemic. The simulations showed that fluctuations in the dynamics of various indicators of the spread of SARS-CoV 2 were associated with the difference in the daily schedule on weekdays and weekends. The results of the calculations showed that the daily schedules of people strongly influence the spread of SARS-CoV 2. Under assumptions of the model, the results show that for the more contagious “rapid” strains of SARS-CoV 2 (omicron), immunocompetent people become a significant source of infection. For the less contagious “slow strains” (alpha) of SARS-CoV 2, the most active source of infection is immunocompromised individuals (pregnant women). The more contagious, or “fast” strain of the SARS-CoV 2 virus (omicron), spreads faster in public transport. For less contagious, or “slow” strains of the virus (alpha), the greatest infection occurs due to work and educational contacts.