The aim: Is to build a forecast of the COVID-19 disease course, considering the vaccination of the population from particular countries. Materials and methods: Based on the analysis of statistical data, the article deals with the topical issue of the impact made by vaccination on the prevention of the COVID-19 pandemic. The time series, showing the dynamics of changes in the number of infected in Chile, Latvia, Japan, Israel, Australia, Finland, India, United States of America, New Zealand, Czech Republic, Venezuela, Poland, Ukraine, Brazil, Georgia for the period 07.08. 2020–09.09.2021, are analyzed. Trend-cyclic models of time series are obtained using fast Fourier transform. The predicted values of the COVID-19 incidence rate for different countries in the period from September 10, 2021 to February 2, 2022 were calculated using the constructed models. Results and conclusions: The results of the study show that vaccination of the population is one of the most effective methods to prevent the COVID-19 pandemic. The proposed method of modeling the dynamics of the incidence rate based on statistical data can be used to build further predictions of the incidence rate dynamics. The study of behavioral aspects of trust in vaccination is proposed to be conducted within the theory regarding the self-organization of complex systems.

The article summarizes the arguments and counterarguments in the scholarly discussion about the problem of choosing a model of healthcare organization. The study’s primary goal was to identify the economic efficiency of the public health system and resistance to COVID-19. The relevance of addressing this research issue is that the epidemiological challenges posed by the pandemic worldwide have manifested themselves differently in various countries. Therefore, it is advisable to consider the effectiveness of public healthcare models and how they have worked out in the fight against COVID-19. Research in the work was carried out in the following logical sequence: conducting scientometric analysis of research, creation of a statistical research base for 22 countries of the world; construction of integral indices of the economic efficiency of the health care system; calculation of public health system resilience to the COVID-19 pandemic; application of frontier DEA analysis to determine system efficiency; comparison and analysis of the results of research on the economic efficiency of public health systems obtained by different methods. The article presents the results of a comparison of the economic efficiency of the public health system, which showed that the system built according to the Beveridge principle is the most resistant to the pandemic and, at the same time, has the highest indices of economic efficiency.

According to the COVID-19 pandemic, the Ukrainian regions significantly differ in the population's vulnerability to this infection. Specific patterns (combinations) of factors identify the reasons for regional differentiation of morbidity and mortality from COVID-19. They were accumulated over a long period and formed the so-called «retrospective portraits of the region's vulnerability to COVID-19» for each region. The main purpose of the study is to define such combinations of financial, economic, environmental and social factors causing many deaths and morbidity from COVID-19 among the population of different Ukrainian regions. The study is based on a constructed spatial nonlinear model. According to the step-by-step algorithm, individual factor variables are gradually added / removed from the model specifications by the Aitken method depending on their correlation with morbidity and mortality from COVID-19 in the region until the model's specification with the highest adequacy by p-value and t-statistics is formed. The nonlinear multifactorial regression equations regarding the dependence of the resulting indicator (the level of morbidity and mortality of the region from COVID-19) on variables-23 indicators of social, economic, environmental and financial development of each Ukrainian region and Kyiv are built for the creation of the «retrospective portraits of the region's vulnerability to COVID-19». Besides, the correlation matrices and correlation pleiades are formed. Based on a correlation matrix, the multicollinearity test is performed using the Farrar-Glauber algorithm. The Durbin-Watson method checks residuals for autocorrelation. The heteroskedasticity test is performed using the Spearman rank correlation test. The empirical analysis results show that migration, population size, the environmental situation in the region, a significant index of medical institutions readiness for qualitative patient care during the pandemic and citizens' income dynamics mostly affect the incidence of COVID-19 and the number of deaths. The retrospective research results can help create road maps of individual regions to overcome the future epidemiological influence effects.

Abstract. This paper summarizes the arguments and counter-arguments in the scholarly debates on transformations in healthcare budgeting that should consider the differentiated regional vulnerability in responding to the pandemic. The primary purpose of the study is to identify priorities for local health development programs. The urgency of solving this problem is that the pandemic has revealed the unprecedented unpreparedness of the health care system to respond effectively to challenges; also, hidden problems accumulated during the last decades, which increase the emerging risks. The study is carried out in the following logical sequence: 1) collection, processing, and analysis of statistical data; 2) conducting a cluster analysis for group regions by vulnerability to different classes of diseases; 3) conducting correlation and regression analysis to compare the effects of the COVID-19 pandemic (cases and deaths) and the state of the region; 4) selection of the most significant features of the vulnerability of the region; 5) designing the matrix of the choice of priorities for financing targeted programs in the field of health care. Methodological tools of the study were methods of correlation and regression analysis, cluster analysis, testing for autocorrelation by Darbin — Watson method, sigma limited parameterization to identify the most significant coefficients. The method is tested for 25 regions of Ukraine (including Kyiv), as they can serve as pilots for other regions with similar demographic and economic characteristics. The article presents the results of an empirical analysis of the readiness of regions for critical conditions, such as COVID-19. Identifying such readiness and appropriate distribution of regions by disease classes allows to make decisions in financing and budgeting and improve the quality of health care. Keywords: COVID-19, regional vulnerability to COVID-19, step-by-step nonlinear regression, morbidity, mortality, regional profile, pandemic, multicollinearity, targeting budgeting. JEL Classification C21, C51, C31, C12, I15, I18, R58, R11 Formulas: 9; fig.: 5; tabl.: 7; bibl.: 36.

This article summarizes the arguments and counterarguments in the framework of the scientific discussion on the problem of identifying, using the Granger test, the components of macroeconomic stability of Ukraine most sensitive to the destructive impact of Covid-19. The study’s primary goal is to select from among many macroeconomic indicators precisely those that cause epidemiological surges in morbidity and mortality of the population using the example of the Covid-19 pandemic. The systematization of literary sources and approaches to solving the problem of finding determinants that affect the course of the pandemic shows many views among the scientists of the world. Still, they do not establish a single opinion. The study of the topic of identifying the influence of indicators of macroeconomic stability on the destructive impact of the pandemic in work is carried out in the following logical sequence: 1) systematization of literary sources according to the topic of the study; 2) creation of a statistical database that meets the requirements of the chosen methods; 3) bringing the obtained time series to a comparative form and achieving their stationarity; 4) conducting a two-sided test to identify causality. The methodological tools of the research methods were the Dickey-Fuller test for detecting a unit root and stationarity of a series, ways to achieve stationarity of a series using different methods, and a two-sided Granger test for detecting the causality of indicators. The object of the study is Ukraine; the term of the study is the beginning of the pandemic from February 2020 to December 2021. The article presents the results of an empirical analysis of the identification of the components of macroeconomic stability of Ukraine most sensitive to the destructive impact of Covid-19, which showed that such indicators exist and the causal relationship exists in both directions. The study empirically confirms and theoretically proves that the most influential factors are the consumer price index and inflation, which cause the variability of the number of infected and deaths in Ukraine. The study results can help create regional and national patterns of resistance to the destructive impact of the pandemic on macroeconomic stability.