Russian Journal of Earth Sciences Russian Journal of Earth Sciences Russian Journal of Earth Sciences 1681-1208 57849 10.2205/2023ES000839 ОРИГИНАЛЬНЫЕ СТАТЬИ ORIGINAL ARTICLES ОРИГИНАЛЬНЫЕ СТАТЬИ COVID-19 Pandemic Course 2020–2022: Description by Methods of Mathematical Statistics and Discrete Mathematical Analysis COVID-19 Pandemic Course 2020–2022: Description by Methods of Mathematical Statistics and Discrete Mathematical Analysis https://orcid.org/0000-0002-4874-7475 Гвишиани Алексей Джерменович Gvishiani Alexei D. rjes@wdcb.ru

доктор физико-математических наук;

doctor of physical and mathematical sciences;

 https://orcid.org/0000-0002-5037-4778 Одинцова Анастасия Александровна Odintsova Anastasia Aleksandrovna Ровенская Елена Александровна Rovenskaya Elena Aleksandrovna Boyarshinov Grigory Boyarshinov Grigory g.boyarshinov@gcras.ru Белов Иван Олегович Belov Ivan Olegovich https://orcid.org/0000-0001-6930-3331 Добровольский Михаил Николаевич Dobrovolsky Michael Nikolaevich m.dobrovolsky@gcras.ru Геофизический центр Российской Академии Наук Москва Россия Geophysical Center of the Russian Academy of Sciences Moscow Russian Federation Институт физики Земли им. О.Ю. Шмидта Российской академии наук Москва Россия Schmidt Institute of the Physics of the Earth Russian Academy of Sciencies Moscow Russian Federation Геофизический центр Российской Академии Наук RU Geophysical Center of the Russian Academy of Sciences RU Международный институт прикладного системного анализа (МИПСА) Австрия International Institute for Applied Systems Analysis (IIASA) Austria ГЦ РАН Москва Россия GC RAS Moscow Russian Federation 29 06 2023 29 06 2023 23 2 1 20 22 03 2023 29 05 2023 https://rjes.ru/en/nauka/article/57849/view

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.

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.

 COVID-19 DMA statistics data analysis This work was supported by the Russian Foundation for Basic Research (project No. 20-57-82003 “System Analysis of patterns of COVID-19 spread in Europe and Russia”).

Adjei, S., K. Hong, N.-A. M. Molinari, L. Bull-Otterson, U. A. Ajani, A. V. Gundlapalli, A. M. Harris, J. Hsu, S. S. Kadri, J. Starnes, K. Yeoman, and T. K. Boehmer (2022), Mortality Risk Among Patients Hospitalized Primarily for COVID-19 During the Omicron and Delta Variant Pandemic Periods - United States, April 2020 - June 2022, MMWR. Morbidity and Mortality Weekly Report, 71(37), 1182-1189, doi:10.15585/mmwr.mm7137a4. Adjei, S., K. Hong, N.-A. M. Molinari, L. Bull-Otterson, U. A. Ajani, A. V. Gundlapalli, A. M. Harris, J. Hsu, S. S. Kadri, J. Starnes, K. Yeoman, and T. K. Boehmer (2022), Mortality Risk Among Patients Hospitalized Primarily for COVID-19 During the Omicron and Delta Variant Pandemic Periods - United States, April 2020 - June 2022, MMWR. Morbidity and Mortality Weekly Report, 71(37), 1182-1189, doi:10.15585/mmwr.mm7137a4. Agayan, S. M., Sh. R. Bogoutdinov, and R. I. Krasnoperov (2018), Short introduction into DMA, Russian Journal of Earth Sciences, 18(2), ES2001, doi:10.2205/2018es000618. Agayan, S. M., Sh. R. Bogoutdinov, and R. I. Krasnoperov (2018), Short introduction into DMA, Russian Journal of Earth Sciences, 18(2), ES2001, doi:10.2205/2018es000618. Agayan, S. M., Sh. R. Bogoutdinov, D. A. Kamaev, and M. N. Dobrovolsky (2019a), Stochastic trends based on fuzzy mathematics, Chebyshevskii Sbornik, 20(3), 92-106, doi:10.22405/2226-8383-2019-20-3-92-106, (in Russian). Agayan, S. M., Sh. R. Bogoutdinov, D. A. Kamaev, and M. N. Dobrovolsky (2019a), Stochastic trends based on fuzzy mathematics, Chebyshevskii Sbornik, 20(3), 92-106, doi:10.22405/2226-8383-2019-20-3-92-106, (in Russian). Agayan, S. M., A. A. Soloviev, Sh. R. Bogoutdinov, and Yu. I. Nikolova (2019b), Regression derivatives and their application in the study of geomagnetic jerks, Geomagnetism and Aeronomy, 59(3), 359-367, doi:10.1134/s0016793219030022. Agayan, S. M., A. A. Soloviev, Sh. R. Bogoutdinov, and Yu. I. Nikolova (2019b), Regression derivatives and their application in the study of geomagnetic jerks, Geomagnetism and Aeronomy, 59(3), 359-367, doi:10.1134/s0016793219030022. Agayan, S. M., Sh. R. Bogoutdinov, M. N. Dobrovolsky, O. V. Ivanchenko, and D. A. Kamaev (2021), Regression differentiation and regression integration of finite series, Chebyshevskii Sbornik, 22(2), 27-47, doi:10.22405/2226-8383-2021-22-2-27-47, (in Russian). Agayan, S. M., Sh. R. Bogoutdinov, M. N. Dobrovolsky, O. V. Ivanchenko, and D. A. Kamaev (2021), Regression differentiation and regression integration of finite series, Chebyshevskii Sbornik, 22(2), 27-47, doi:10.22405/2226-8383-2021-22-2-27-47, (in Russian). Aurambout, J.-P., C. Pettit, and H. Lewis (2008), Virtual Globes: the Next GIS?, in Lecture Notes in Geoinformation and Cartography, pp. 509-532, Springer Berlin Heidelberg, doi:10.1007/978-3-540-69168-6_25. Aurambout, J.-P., C. Pettit, and H. Lewis (2008), Virtual Globes: the Next GIS?, in Lecture Notes in Geoinformation and Cartography, pp. 509-532, Springer Berlin Heidelberg, doi:10.1007/978-3-540-69168-6_25. Dzeboev, B. A., A. A. Odintsova, A. I. Rybkina, and B. V. Dzeranov (2022), Assessment of the influence of astronomical cyclicity on sedimentation processes in the Eastern Paratethys based on paleomagnetic measurements using Discrete Mathematical Analysis, Applied Sciences, 12(2), 580, doi:10.3390/app12020580. Dzeboev, B. A., A. A. Odintsova, A. I. Rybkina, and B. V. Dzeranov (2022), Assessment of the influence of astronomical cyclicity on sedimentation processes in the Eastern Paratethys based on paleomagnetic measurements using Discrete Mathematical Analysis, Applied Sciences, 12(2), 580, doi:10.3390/app12020580. GIBDD (2023), Information about the indicators of the state of road safety, http://stat.gibdd.ru/. GIBDD (2023), Information about the indicators of the state of road safety, http://stat.gibdd.ru/. GitHub (2023), CSSEGISandData/COVID-19: Novel Coronavirus (COVID-19) Cases, provided by JHU CSSE, http s://github.com/CSSEGISandData/COVID-19. GitHub (2023), CSSEGISandData/COVID-19: Novel Coronavirus (COVID-19) Cases, provided by JHU CSSE, http s://github.com/CSSEGISandData/COVID-19. Gvishiani, A. D. (2019), Interdisciplinary Arctic Studies and System Analysis, in V International Arctic Forum "Arctic: Territory of Dialogue" on 9-10 April, St. Petersburg, Russia, (in Russian). Gvishiani, A. D. (2019), Interdisciplinary Arctic Studies and System Analysis, in V International Arctic Forum "Arctic: Territory of Dialogue" on 9-10 April, St. Petersburg, Russia, (in Russian). Gvishiani, A. D., S. M. Agayan, Sh. R. Bogoutdinov, A. V. Ledenev, Z. Zlotniki, and Z. Bonnin (2003), Mathematical Methods of Geoinformatics. II. Fuzzy-Logic Algorithms in the Problems of Abnormality Separation in Time Series, Cybernetics and Systems Analysis, 39(4), 555-563, doi:10.1023/b:casa.0000003505.56410.4f. Gvishiani, A. D., S. M. Agayan, Sh. R. Bogoutdinov, A. V. Ledenev, Z. Zlotniki, and Z. Bonnin (2003), Mathematical Methods of Geoinformatics. II. Fuzzy-Logic Algorithms in the Problems of Abnormality Separation in Time Series, Cybernetics and Systems Analysis, 39(4), 555-563, doi:10.1023/b:casa.0000003505.56410.4f. Gvishiani, A. D., S. M. Agayan, and Sh. R. Bogoutdinov (2008), Fuzzy recognition of anomalies in time series, Doklady Earth Sciences, 421(1), 838-842, doi:10.1134/s1028334x08050292. Gvishiani, A. D., S. M. Agayan, and Sh. R. Bogoutdinov (2008), Fuzzy recognition of anomalies in time series, Doklady Earth Sciences, 421(1), 838-842, doi:10.1134/s1028334x08050292. Huang, C., Y. Wang, X. Li, et al. (2020), Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China, The Lancet, 395(10223), 497-506, doi:10.1016/s0140-6736(20)30183-5. Huang, C., Y. Wang, X. Li, et al. (2020), Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China, The Lancet, 395(10223), 497-506, doi:10.1016/s0140-6736(20)30183-5. Kolmogorov, A. N., and S. V. Fomin (2004), Elements of the Theory of Functions and Functional Analysis, 7th ed., Fizmatlit, Moscow, (in Russian). Kolmogorov, A. N., and S. V. Fomin (2004), Elements of the Theory of Functions and Functional Analysis, 7th ed., Fizmatlit, Moscow, (in Russian). Odintsova, A., A. Rybkina, J. Nikolova, and A. Korolkova (2020), GIS Project ROSA: FAIR Principles in the Petroleum Industry, Data Science Journal, 19(1), 13, doi:10.5334/dsj-2020-013. Odintsova, A., A. Rybkina, J. Nikolova, and A. Korolkova (2020), GIS Project ROSA: FAIR Principles in the Petroleum Industry, Data Science Journal, 19(1), 13, doi:10.5334/dsj-2020-013. ORBUS Web (2019), http://data.sph.gcras.ru/OrbusWeb.html?/COVID-19. ORBUS Web (2019), http://data.sph.gcras.ru/OrbusWeb.html?/COVID-19. Rospotrebnadzor (2023), Answers to frequently asked questions about coronavirus infection COVID-19, http://86.rospotrebnadzor.ru/territorialnye-otdely/ug/otvety-na-chasto-zadavaemye-voprosy-po-koronavirusnoj-infekcii-covid-19. Rospotrebnadzor (2023), Answers to frequently asked questions about coronavirus infection COVID-19, http://86.rospotrebnadzor.ru/territorialnye-otdely/ug/otvety-na-chasto-zadavaemye-voprosy-po-koronavirusnoj-infekcii-covid-19. Rossiyskaya Gazeta (2022), Preliminary results of the All-Russian Population Census, https://rg.ru/2022/05/30/predvaritelnye-itogi-vserossijskoj-perepisi-naseleniia.html. Rossiyskaya Gazeta (2022), Preliminary results of the All-Russian Population Census, https://rg.ru/2022/05/30/predvaritelnye-itogi-vserossijskoj-perepisi-naseleniia.html. Saxena, R., M. Jadeja, and V. Bhateja (2022), Exploring Susceptible-Infectious-Recovered (SIR) Model for COVID-19 Investigation, 53 pp., Springer Nature Singapore, doi:10.1007/978-981-19-4175-7. Saxena, R., M. Jadeja, and V. Bhateja (2022), Exploring Susceptible-Infectious-Recovered (SIR) Model for COVID-19 Investigation, 53 pp., Springer Nature Singapore, doi:10.1007/978-981-19-4175-7. Sklyarov, B. A. (2022), Research data and conclusions pertaining to the omicron strain of coronavirus (COVID-19), International Journal of Humanities and Natural Sciences, pp. 102-105, doi:10.24412/2500-1000-2022-5-1-102-105, (in Russian). Sklyarov, B. A. (2022), Research data and conclusions pertaining to the omicron strain of coronavirus (COVID-19), International Journal of Humanities and Natural Sciences, pp. 102-105, doi:10.24412/2500-1000-2022-5-1-102-105, (in Russian). Soloviev, A. A., A. I. Gorshkov, and A. A. Soloviev (2016), Application of the data on the lithospheric magnetic anomalies in the problem of recognizing the earthquake prone areas, Izvestiya, Physics of the Solid Earth, 52(6), 803-809, doi:10.1134/s1069351316050141. Soloviev, A. A., A. I. Gorshkov, and A. A. Soloviev (2016), Application of the data on the lithospheric magnetic anomalies in the problem of recognizing the earthquake prone areas, Izvestiya, Physics of the Solid Earth, 52(6), 803-809, doi:10.1134/s1069351316050141. World Health Organization (2019), Public Health and Social Measures, https://www.who.int/emergencies/diseases/novel-coronavirus-2019/phsm. World Health Organization (2019), Public Health and Social Measures, https://www.who.int/emergencies/diseases/novel-coronavirus-2019/phsm. Yandex DataLens (2023), Coronavirus. Dashboard and data. Yandex DataLens (2023), Coronavirus. Dashboard and data.