COVID-19 Pandemic Course 2020–2022: Description by Methods of Mathematical Statistics and Discrete Mathematical Analysis
The paper describes the course of the COVID-19 pandemic using a combination of mathematical statistics and discrete mathematical analysis (DMA) methods. The method of regression derivatives and FCARS algorithm as components of DMA will be for the first time tested outside of geophysics problems. The...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Russian Academy of Sciences, The Geophysical Center
2023-06-01
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| Series: | Russian Journal of Earth Sciences |
| Subjects: | |
| Online Access: | http://doi.org/10.2205/2023ES000839 |
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| _version_ | 1839608634869284864 |
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| author | Gvishiani Alexei Odintsova Anastasiya Rovenskaya Elena Boyarshinov Grigory Belov Ivan Dobrovolsky Michael |
| author_facet | Gvishiani Alexei Odintsova Anastasiya Rovenskaya Elena Boyarshinov Grigory Belov Ivan Dobrovolsky Michael |
| author_sort | Gvishiani Alexei |
| collection | DOAJ |
| description | The paper describes the course of the COVID-19 pandemic using a combination of mathematical statistics and discrete mathematical analysis (DMA) methods. The method of regression derivatives and FCARS algorithm as components of DMA will be for the first time tested outside of geophysics problems. The algorithm is applied to time series of the number of new cases of COVID-19 infections per day for some regions of Russia and the Republic of Austria. This allowed to assess the nature and anomalies of pandemic spread as well as restrictive measures and decisions taken in terms of the administration of countries and territories. It was shown that these methods can be used to identify time intervals of change in the nature of the incidence rate and areas with the most severe course of the epidemic. This made it possible to identify the most significant restrictive measures that allowed to reduce the growth of the disease. |
| format | Article |
| id | doaj-art-e63b413a5f954c668fa24e3cc62e9d9b |
| institution | Matheson Library |
| issn | 1681-1208 |
| language | English |
| publishDate | 2023-06-01 |
| publisher | Russian Academy of Sciences, The Geophysical Center |
| record_format | Article |
| series | Russian Journal of Earth Sciences |
| spelling | doaj-art-e63b413a5f954c668fa24e3cc62e9d9b2025-07-31T08:24:16ZengRussian Academy of Sciences, The Geophysical CenterRussian Journal of Earth Sciences1681-12082023-06-0123212010.2205/2023ES000839COVID-19 Pandemic Course 2020–2022: Description by Methods of Mathematical Statistics and Discrete Mathematical AnalysisGvishiani Alexei0https://orcid.org/0000-0002-4874-7475Odintsova Anastasiya1Rovenskaya Elena2Boyarshinov Grigory3Belov Ivan4Dobrovolsky Michael5https://orcid.org/0000-0001-6930-3331Geophysical Center of the Russian Academy of SciencesGeophysical Center of the Russian Academy of Sciences, Moscow, RussiaInternational Institute for Applied Systems Analysis (IIASA)GC RASGeophysical Center of the Russian Academy of SciencesGeophysical Center of the Russian Academy of SciencesThe paper describes the course of the COVID-19 pandemic using a combination of mathematical statistics and discrete mathematical analysis (DMA) methods. The method of regression derivatives and FCARS algorithm as components of DMA will be for the first time tested outside of geophysics problems. The algorithm is applied to time series of the number of new cases of COVID-19 infections per day for some regions of Russia and the Republic of Austria. This allowed to assess the nature and anomalies of pandemic spread as well as restrictive measures and decisions taken in terms of the administration of countries and territories. It was shown that these methods can be used to identify time intervals of change in the nature of the incidence rate and areas with the most severe course of the epidemic. This made it possible to identify the most significant restrictive measures that allowed to reduce the growth of the disease.http://doi.org/10.2205/2023ES000839COVID-19 DMA statistics data analysis |
| spellingShingle | Gvishiani Alexei Odintsova Anastasiya Rovenskaya Elena Boyarshinov Grigory Belov Ivan Dobrovolsky Michael COVID-19 Pandemic Course 2020–2022: Description by Methods of Mathematical Statistics and Discrete Mathematical Analysis Russian Journal of Earth Sciences COVID-19 DMA statistics data analysis |
| title | COVID-19 Pandemic Course 2020–2022: Description by Methods of Mathematical Statistics and Discrete Mathematical Analysis |
| title_full | COVID-19 Pandemic Course 2020–2022: Description by Methods of Mathematical Statistics and Discrete Mathematical Analysis |
| title_fullStr | COVID-19 Pandemic Course 2020–2022: Description by Methods of Mathematical Statistics and Discrete Mathematical Analysis |
| title_full_unstemmed | COVID-19 Pandemic Course 2020–2022: Description by Methods of Mathematical Statistics and Discrete Mathematical Analysis |
| title_short | COVID-19 Pandemic Course 2020–2022: Description by Methods of Mathematical Statistics and Discrete Mathematical Analysis |
| title_sort | covid 19 pandemic course 2020 2022 description by methods of mathematical statistics and discrete mathematical analysis |
| topic | COVID-19 DMA statistics data analysis |
| url | http://doi.org/10.2205/2023ES000839 |
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