A MATHEMATICAL MODEL FOR PROGNOSIS OF THE COVID-19 INCIDENCE IN UKRAINE USING GOOGLE TRENDS RESOURCES IN REAL-TIME AND FOR THE FUTURE PERIOD

Abstract

Digital epidemiology resources are actively used for the timely response of the health care system to the emergence and spread of diseases. Analytical methods applicable to time series of data are used for detailed analysis of seasonal fluctuations of infectious diseases. Together with the Google Trends (GT) tool, such methods allow modeling the dynamics of diseases in real-time and for future periods. Given that the COVID-19 pandemic is still at an early stage of development, new methods of epidemiological surveillance of the disease will be able to ensure a timely response of the health care system to it. The aim of this research is to study the use of GT resources to build a mathematical model for the prognosis of the COVID-19 incidence in Ukraine in real time and for future periods. Materials and methods. In the course of the study, we used the GT tool to search Google queries “ковід, ковид, COVID-19” (KKC). Data on morbidity in Ukraine were obtained using the web resource: https://index.minfin.com.ua/ua/reference/coronavirus/ukraine/. Excel, Eviews, and StatPlus software packages were used to analyze time series, construct periodograms, correlograms, and mathematical models. The mathematical model of morbidity dynamics was built based on statistical exponential smoothing. Results. As Cyrillic equivalents of the term COVID-19, Ukrainians use the queries “кові(и)д”. Correlograms of KKC requests and actual incidence show seasonal fluctuations of the same frequency, and singular spectral analysis revealed statistically significant peaks. Based on statistical exponential smoothing, a prognostic model for the incidence of COVID-19 for 2022-2024 was built, which is reliable according to the criteria of accuracy and the results of the Dickey-Fuller test. Conclusions. The GT tool is a reliable source of data for studying the dynamics of the spread of COVID-19. Together with the use of additive time series models, it allows for a real-time reliable prognosis of the development of the disease. The presented approach to modeling the dynamics of the spread of COVID-19 can be used to track outbreaks of the disease and respond promptly to them both on a national and local scale.