Please use this identifier to cite or link to this item:
https://elib.bsu.by/handle/123456789/291842| Title: | Multi-country analysis of the COVID-19 pandemic typology using machine learning algorithms |
| Authors: | Malugin, V. I. Kornievich, A. K. |
| Keywords: | ЭБ БГУ::ЕСТЕСТВЕННЫЕ И ТОЧНЫЕ НАУКИ::Математика ЭБ БГУ::ЕСТЕСТВЕННЫЕ И ТОЧНЫЕ НАУКИ::Кибернетика |
| Issue Date: | 2022 |
| Publisher: | Minsk : BSU |
| Citation: | Computer Data Analysis and Modeling: Stochastics and Data Science : Proc. of the XIII Intern. Conf., Minsk, Sept. 6–10, 2022 / Belarusian State University ; eds.: Yu. Kharin [et al.]. – Minsk : BSU, 2022. – Pp. 116-121. |
| Abstract: | The paper presents the results of a multi-country analysis of the intensity of the COVID-19 pandemic in 30 countries of the European region based on publicly available and regularly updated panel data. In the generated space of classification features countries are divided into three classes, which differ in the intensity of the epidemic process. Based on the obtained country ratings, an integral statistical indicator of the COVID-19 pandemic is constructed. The relationship of country ratings with their economic indicators are investigated |
| URI: | https://elib.bsu.by/handle/123456789/291842 |
| ISBN: | 978-985-881-420-5 |
| Licence: | info:eu-repo/semantics/restrictedAccess |
| Appears in Collections: | 2022. Computer Data Analysis and Modeling: Stochastics and Data Science |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 116-121.pdf | 1,22 MB | Adobe PDF | View/Open |
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