In modern world, the digitalisation of financial relations, the development of innovative technologies, and the emergence and use of cryptocurrencies for payments lead to an increase in the number of cyber frauds in the financial sector and their intellectualisation, increasing the illegal outflow of funds abroad. Ineffective decisions and inaction in counteracting these threats lead to large-scale negative consequences of both financial and social nature. The purpose of this study is to implement economic and mathematical modelling of the effectiveness of the national system for combatting cyber fraud and legalisation of criminal proceeds, which is based on the use of survival analysis methods. The study provides a bibliometric analysis of publications on the effectiveness of cyber fraud and combatting the legalisation of illegal funds, by building a bibliometric map of keywords, using VOSviewer software. This allowed identifying 7 clusters of basic categories of cyber fraud analysis, and changes in the vectors of research scientists showed a visual map of the contextual-temporal measurement of research into the effectiveness of cyber fraud in the publications of the Scopus database. The paper examines the effectiveness of the national system for combatting cyber fraud and money laundering based on survival tables. As a result of the study, the effectiveness of the national system for combatting cyber fraud and money laundering was analysed based on the Kaplan-Meier method. The study identified the dependences of the effectiveness of the national system for combatting cyber fraud and legalisation of criminal proceeds on the time interval after the discovery of violations. The practical value of applying the developed model is to form an analytical basis for further management decisions by the National Bank of Ukraine, the State Financial Monitoring Service, and the Security Service of Ukraine in terms of the effectiveness of the national system to combat cyber fraud and legalisation of criminal proceeds and the need to adjust it

The article deals with the impact of digitalization and COVID-19 on the choice of AML scenarios for reforming the system of tactical and strategic monitoring of transactions carried out by economic entities based on providing good governance. The study period is 2011-2020; the objects of the study are 140 countries. Calculations are performed using Data-Mining methods, such as AML scenarios based on the classification tree method (one-dimensional CART branching method) and clustering of countries according to relevant AML scenarios based on agglomerative methods. There are three stages of research. The first builds a comprehensive system of indicators which involves financial inclusion indicators of the population, the ranking of countries on the Basel AML Index, and effectiveness of the AML policy implementation at the country level. The second stage considers countries' clustering according to the AML scenarios and formalizes the portraits of countries' clusters. The third stage examines the impact of digitalization and COVID-19 on the choice of AML scenarios. According to the empirical results, rapid, moderately rapid, slow and neutral adaptability to external factors are formalized in the possible scenarios as a result of such effects. Moreover, the countries' clustering proves that the money laundering risks relevant to the country lower and the implementation of the AML measures by the state grows more effective with higher financial inclusion for the population in the country. The study results can be helpful for authorized bodies in providing good governance while conducting financial monitoring and analysis of information on transactions carried out by economic entities.

This paper summarizes the arguments and counterarguments within the scientific discussion on the issue of the need for an innovative policy in the area of health protection at the link with the transformation of the social and economic development of the country through the pandemic COVID-19. The main goal of this study is to predict two scenarios for the development of the main indicators of the country's socio-economic development: considering the pandemic COVID-19 and the possible course of events without the influence of epidemiological threats. The systematization of literary sources and approaches to innovation and the determination of the volume of negative consequences for the national economy, due to the introduction of quarantine restrictions, has shown that this issue is quite relevant around the world. The study of the transformation of the trajectory of economic development of Ukraine in the article was carried out in the following logical sequence: 1) collection of statistical information, including 118 indicators of social development, the state of capital investment and business expectations of Ukrainian enterprises and screening of multicollinear indicators among them; 2) performing a time series decomposition separately for the interval 5 years before quarantine and taking into account the impact of the pandemic; 3) forecasting the consequences of the pandemic according to the investigated indicators of economic development in 2020-2022 by turning the time series into the Fourier series. The methodological tools of the study were methods of checking for multicollinearity by Pearson coefficients, decomposition of additive models into a trend and cyclic components, selection of cyclic oscillations by fast Fourier transform, extrapolation of constructed models for subsequent years, and quality control of constructed models by F-test quarterly data for 2015-2020 are selected. The study empirically confirms and theoretically proves that among the socio-economic development factors studied, most experienced significant transformations due to the introduction of quarantine restrictions by the government. This leads to the need for innovation policy in the health sector in order to minimize such consequences in the future. Keywords: Fourier series, forecasting, COVID-19, innovation, time series decomposition, health care.

The division of the regions of Ukraine into “red”, “orange”, “yellow” and “green” zones are the consequences of the differentiated regional impact of the pandemic caused by the COVID-19 virus, but the reasons for such different vulnerabilities have not been clarified yet. The purpose of the study is to construct a system of regression equations containing implicit variables that are common characteristics of industries and help to analyse relationships in a complex system. The methodological tools of the study were: review of current scientific trends using VOSViewer 1.6.10, the main component method, which allows selecting the most significant factors and model with structural equations that reflect the relationship between the three areas of activity. 25 oblasts of Ukraine were selected as the object of the study, since they have different levels of vulnerability to the pandemic and can become a model for studying the regional differentiation of any country. The study presents the results of an empirical analysis of the structure of three areas of activity of the country. Modelling of structural equations to establish the relationship between the factors of vulnerability of the regions of Ukraine from the COVID-19 pandemic, the environmental state and the state of readiness of the medical system is carried out. It is theoretically substantiated that there is a direct connection between the studied areas: environmental, medical and epidemiological, and that deterioration in one industry leads to deterioration in another. The results obtained prove that it is possible to influence the differentiated course of the pandemic, but not after the event. A consistent increase in funding from the state budget for healthcare would have a greater effect, with sufficient financial support for environmental protection. The choice of state strategies must be approached comprehensively, because a narrow reform of the system, such as medical, will not give the maximum effect, without an innovative policy in the field of ecology

Background. Vaccination is the most effective part of primary prevention. Serological monitoring of infectious diseases covered by national immunization programs is very important as it provides up-to-date information on the burden of the infection and the immunological status of the population. The study was aimed to present an analysis of epidemiological monitoring of the protection against diphtheria of the population, to show the generalized epidemiological situation regarding diphtheria, and to determine the risk of diphtheria among the population of Dnipropetrovsk region. Materials and methods. Epidemiological analysis of diphtheria immunity (2017–2019) was performed based on the results of enzyme-linked immunosorbent assay of IgG antibodies levels against diphtheria toxin in 271 residents of Dnipropetrovsk region. Results. Analysis of the results revealed that only 30.6 % (n = 83) of the population have levels of antitoxic antibodies of 1.0 IU/ml or more, which provides sufficient protection against diphtheria in the next 5–7 years of life. At that time, the majority of the population (69.4 %) needs immediate one-time booster vaccination (n = 134; 49.5 %) or immediate basic vaccination (n = 54; 19.9 %) due to low levels of antitoxic diphtheria antibodies. In the age group 8–15 years, 65.9 % (n = 62) of patients require immediate basic or booster vaccination, which indicates that children of this age do not have basic immunological protection due to violations of the vaccination schedule or its absence. In the group aged 27 years and older, 79.1 % (n = 72) of the subjects do not have protective levels of anti-diphtheria antibodies, which indicates a lack of actual protection against diphtheria. Conclusions. The results indicate insufficient protection of the population against diphtheria. In this regard, the development of strategic measures for mass immunoprophylaxis of diphtheria both in children and adults is relevant. The country should conduct regular epidemiological monitoring, which would study the population’s immunity against diphtheria and other controlled infections, and draw up a long-term strategic and tactical plan to address shortcomings in the work of mass immunoprophylaxis of the population.

The article summarizes the arguments in the scientific debate on public trust in vaccination against COVID-19. The main purpose of the research is to model the trend of changes in the trust in vaccination against COVID-19, analyzing time series by exponential smoothing. The object of the study is the public trust in vaccination against COVID-19. The research aims to model the changing trend of the trust in the vaccination against COVID-19 using time series analysis. Systematization of literature sources and approaches to solving the vaccination problem during the pandemic showed that various psychological, social, economic factors, including trust in official information, affect the level of trust in vaccination. Declining confidence in the authorities, medical institutions, social institutions, and the media significantly impacts the effectiveness of pandemic containment measures. The trust of the population in state institutions in scientifically based information on vaccination provides the necessary level of vaccination during the COVID-19 pandemic. The study regarding the changing trend in the level of trust in vaccination against COVID-19 analyzing time series by exponential smoothing in the article is carried out in the following logical sequence: 1) Internet users on the issues of “Trust in vaccination against COVID-19”, “Vaccination against COVID-19”, “Trust in vaccination” was carried out using Google Trends; 3) Statistica software package was used to implement exponential smoothing models. Predictive models of exponential smoothing based on the following indicators: “Trust in vaccination against COVID-19”, “Trust in vaccination”, “Vaccination against COVID-19” are constructed. Time series models related to public confidence in COVID-19 vaccination demonstrate the presence of a seasonal component every two weeks. The results of the study can be useful for the development of scientifically sound recommendations to control the vaccination process during a pandemic, to build predictions of the level of trust in vaccination against COVID-19.

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