Short-term forecasting of the main indicators of the COVID-19 epidemic in Ukraine based on the seasonal cycle model
DOI:
https://doi.org/10.20535/SRIT.2308-8893.2024.4.02Keywords:
COVID-19 epidemic, time series, short-term forecasting, seasonal cycle, indicatorsAbstract
The authors of this study propose a method of short-term forecasting of time series of the main indicators of the COVID-19 epidemic, which has a pronounced seasonality. This method, which has no direct analogies, provides the decomposition of a general forecasting task into several simpler tasks, such as the tasks of building a model of the seasonal cycle of a time series, aggregating the original time series, taking into account the duration of the seasonal cycle, forecasting an aggregated time series, developing an aggregated forecast into a forecast in the original time scale, using the seasonal cycle model. The solution for each task allows the usage of relatively simple methods of mathematical statistics. The article provides a formally rigorous description of all procedures of the method and illustrations of their numerical implementation on the example of a real forecasting task. The use of this method for short-term forecasting of the COVID-19 epidemic development in Ukraine has systematically demonstrated its effectiveness.
References
W.O. Kermack, A.G. McKendrick, “A Contribution to the Mathematical Theory of Epidemics,” Proc. Roy. Soc. Lond. A., no. 115, pp. 700–721, 1972.
S. Borenstein, C.K. Johnson, “Modeling coronavirus: ‘Uncertainty is the only certainty,’” AP News, April 8, 2020. Accessed on: February 10, 2022. [Online]. Available: https://apnews.com/article/public-health-health-us-news-ap-top-news-virus-outbreak-88866498ff5c908e5f28f7b5b5e5b695
V. Romanenko, Y. Milyavsky, “Combined control of multirate impulse processes in a cognitive map of COVID-19 morbidity,” System Research and Information Technologies, no. 3, pp. 46–56, 2022. doi: https://doi.org/10.20535/SRIT.2308-8893.2022.3.03
L Guryanova, R. Yatsenko, N. Dubrovina, V. Babenko, and V. Gvozditskyi, “Machine learning methods and models, predictive analytics and applications: development trends in the post-crisis syndrome caused by COVID-19,” CEUR Workshop Proceedings, 2927, pp. 1–7, 2021.
G.E.P. Box, G.M. Jenkins, G.C. Reinsel, and G.M. Ljung, Time Series Analysis: Forecasting and Control. New Jersey: John Wiley & Sons, Inc., 2015, 712 p.
P.R. Winters, “Forecasting sales by exponentially weighted moving averages” Management Science, no. 6, pp. 324–342, 1960.
“Koronavirusna infektsiia COVID-19 [Corona virus infection COVID-19],” Tsentr hromadskoho zdorovia MOZ Ukrainy. Available: https://phc.org.ua/kontrol-zakhvoryuvan/inshi-infekciyni-zakhvoryuvannya/koronavirusna-infekciya-covid-19
“Prohnoz rozvytku epidemii COVID-19 v Ukraini na period 7–13 hrudnia 2020 roku vid uchenykh Instytutu problem rynku ta ekonomiko-ekolohichnykh doslidzhen NAN Ukrainy [Forecast of the development of the COVID-19 epidemic in Ukraine for the period of December 7–13, 2020 by scientists of the Institute of Market Problems and Economic and Environmental Research of the National Academy of Sciences of Ukraine],” Natsionalna akademiia nauk Ukrainy, December 7, 2020. Available: https://www.nas.gov.ua/UA/Messages/Pages/View.aspx?MessageID=7237
