Abstract

This study aims to analyze the course of the COVID-19 disease and forecast its progress. Systematization of scientific background concerning the issues under investigation indicated the snowballing growth of scientific publications devoted to COVID-19. The urgency of the research rests on the negative influence of the SARS-CoV-2 virus on all spheres that deepen the global economic crisis. The forecast of the COVID-19 progress in Ukraine was carried out in the following logical sequence: 1) collection and analysis of statistical data; 2) testing stationarity and periodicity of the time series, using software Statistica (portable); 3) constructing the trend component 4) detecting the seasonal component by the fast Fourier transformation under excluded trend; 5) building the general model, checking its quality and adequacy; 6) forecast and elaboration on the recommendations. The methodological tools of the study were the regression and correlation analysis, the Dickie-Fuller test, decomposition of the additive model into cyclic and trend components, and the fast Fourier transformation, using software MathCAD(portable). The empirical research was conducted based on panel data from 22 January 2020 to 2 May 2021. The object of the study was selected the population of Ukraine who were infected with coronavirus during the study period. The study empirically confirmed and theoretically proved that if the pandemic tendency persists and the absence of carefully planned COVID-19 preventive work in Ukraine, a new wave of the disease may occur from mid-August to early September. Besides, the growth of the load on the medical sector and the increase in COVID-19 deaths among the population may lead to the introduction of new restrictive quarantine measures. In turn, it would result in an economic shutdown and the decline of small and medium businesses. Therefore, it is necessary to direct the Government’s policy to eliminate the myths about vaccination, to increase confidence in vaccination among the population, which will avoid the next wave of diseases.

Keywords: COVID-19, fast Fourier transformation, stationarity, Dickie-Fuller test, morbidity, harmonic analysis, trend-seasonal model, forecast.

JEL Classification: M12, M19, M21.

Cite as: Kascha, M., Palienko, M., & Marchenko, R. (2021). Forecast of COVID-19 progress considering the seasonal fluctuations. Health Economics and Management Review, 2(2), 71-82. https://doi.org/10.21272/hem.2021.2-07

This work is licensed under a Creative Commons Attribution 4.0 International License

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