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Introduction. Understanding the epidemic curve and spatiotemporal dynamics of SARS-CoV-2 virus is of fundamental importance for the work of the health system during epidemic and pandemic periods. Firstly, the data obtained allow us to assess the epidemiological characteristics of the virus. Secondly, it becomes possible to develop and coordinate measures to counter the spread of COVID-19, to allocate resources reasonably.   The work objective is to create and initialize a mathematical model of the epidemic process, which makes it possible to explain the observed dynamics, to predict its development and to assess the reliability of such forecasts.   Materials and Methods. Scientific research was based on the statistical data analysis. A hierarchy of mathematical models describing the dynamics of the spread of a new coronavirus infection (COVID-19) and the mortality of COVID-positive patients from 12.02.2020 to 22.09.2021 has been constructed. The incidence submodel reflects regular (aperiodic and periodic), as well as random components. To study and predict the processes, the classical technique of time series research, correlation and Fourier analysis were used. This approach allowed using the method of moments to identify the statistical properties of the scientific research object, and then visualize the stages and algorithm of work.   Results. An optimistic, pessimistic and intermediate scenario of infection spread has been mathematically investigated. Their strengths and weaknesses are noted. Numerical characteristics of the trend model and the model of fluctuations in the incidence of COVID-19 are systematized in the form of tables. Based on these data, a conclusion is formulated about the optimality of the pessimistic model: after the highest possible indicators, the infection curve reaches a plateau, and the virus remains in the population. It has been established that the spread of a new coronavirus infection has a pronounced seasonal character with a period of 1/3 of the year. Mathematical analysis and modeling of the mortality dynamics of COVID-positive patients revealed weekly fluctuations in the level of deaths. At the same time, it turned out that the maximum risk corresponds to the 15th and 22nd day of infection. According to the hypothesis proposed by the authors, this virus will be characteristic of the human population. The mortality rate is expected to be 1.75 %. The calculations have shown that the influence of random components of morbidity and mortality will correspond to seasonal fluctuations.   Discussion and Conclusion. The probable frequency of the epidemic has been established — three times a year. The potential mortality rate is determined as constant. It is caused by epidemiological and organizational reasons, i. e. the work of medical institutions and authorities. Taking into account the features of the new coronavirus strain (omicron), it is possible to predict the further dynamics of the pandemic and make recommendations regarding its prevention. The authors believe that vaccination should be carried out three times a year. Optimal periods of vaccination campaigns:05. 02–15. 02, 17. 05–28. 05, 24. 09–5. 10.

In the paper an appreciable hierarchy of mathematical models is proposed. It describes the actual dynamics of COVID-19 thickness during 02/12/2020 – 09/22/2021. The incidence sub-model reflects reliably regular (aperiodic and periodic), as well as random components. It is established that the dynamics of the epidemy is essentially seasonal thrice a year. Model elaborated enables to clarify and explain weak weekly fluctuations in the death rate dynamics. It turned out that the maximum risk of death is at 15 and 22 days of disease duration. It means that this virus will presumably be a "satellite" of the human population with corresponded mortality at 1.75%. Calculations performed enable to estimate the level of stochastics in disease and death dynamics. It is near to the amplitude of periodic variation. Computer experiments with developed model predict the global dynamics of the incidence of COVID-19. New epidemic data can show the prospect of improving our model to regard the competitiveness between new sporadically emerging virus strains.