Methods of intellectual data analysis in COVID-19 research

Authors

  • Oleg V. Senko Central Research Institute of Epidemiology, Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing, 3a Novogireevskaya Street, Moscow 111123, Russia; Federal Research Center «Computer Science and Control», Russian Academy of Sciences, 44 Vavilova Street, 2 building, Moscow 119333, Russia
  • Anna V. Kuznetsova Central Research Institute of Epidemiology, Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing; Institute of Biochemical Physics named after N. M. Emanuel, Russian Academy of Sciences, 4 Kosygina Street, Moscow 119334, Russia
  • Evgeny M. Voronin Central Research Institute of Epidemiology, Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing, 3a Novogireevskaya Street, Moscow 111123, Russia
  • Olga A. Kravtsova Central Research Institute of Epidemiology, Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing, 3a Novogireevskaya Street, Moscow 111123, Russia; Lomonosov Moscow State University, 1 Leninskie Gory, Moscow 119991, Russia https://orcid.org/0000-0002-7757-5334
  • Ludmila R. Borisova Financial University under the Government of the Russian Federation, 49/2 Leningradskii Avenue, Moscow 125167, Russia https://orcid.org/0000-0002-5757-0341
  • Igor L. Kirilyuk Institute of Economics, Russian Academy of Sciences, 32 Nakhimovskii Avenue, Moscow 117218, Russia https://orcid.org/0000-0002-8935-9241
  • Vasiliy G. Akimkin Central Research Institute of Epidemiology, Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing, 3a Novogireevskaya Street, Moscow 111123, Russia

Keywords:

cluster analysis, machine learning methods, statistics, epidemiological process, COVID-19
Supporting Agencies
FSBI Сentral Research Institute of Epidemiology (CRIE) of Federal Service for the Oversight of Consumer Protection and Welfare (Rospotrebnadzor), 3a, Novogireevskaya str., Moscow, Russia; A.A. Dorodnitsyn FITs of Informatics and Management RAS, Moscow, Vavilova, 42, Russia the NM. Emanuel IBCP of RAS, Moscow, 4, st. Kosygin, Russia; Lomonosov Moscow State University, Moscow, Russia; Financial University under the Government of the Russian Federation, Moscow, 49 Leningradsky ave., Moscow, 125993, Russia. Institute of Economics, Russian Academy of Sciences (IE RAS), 32 Nakhimovskiy prospekt, Moscow.

Abstract

The paper presents an original method for solving the problem of finding a connection between the course of the epidemic and socio-economic, demographic and climatic factors. The method was applied to solve this problem for 110 countries of the world using a set of corresponding curves of the COVID-19 growth rate for the period from January 2020 to August 2021. Hierarchical agglomerative clustering was applied. Four large clusters with uniform curves were identified – 11, 39, 17 and 13 countries, respectively. Another 30 countries were not included in any cluster. Using machine learning methods, we identified the differences in socio-economic, demographic and geographical and climatic indicators in the selected clusters of countries of the world. The most important indicators by which the clusters differ from each other are amplitude of temperatures throughout the year, high-tech exports, Gini coefficient, size of the urban population and the general population, index of net barter terms of trade, population growth, average January temperature, territory (land area), number of deaths due to natural disasters, birth rate, coastline length, oil reserves, population in urban agglomerations with a population of more than 1 million etc. This approach (the use of clustering in combination with classification by methods of logical-statistical analysis) has not been used by anyone before. The found patterns will make it possible to more accurately predict the epidemiological process in countries belonging to different clusters. Supplementing this approach with autoregressive models will automate the forecast and improve its accuracy.

Author Biographies

  • Oleg V. Senko, Central Research Institute of Epidemiology, Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing, 3a Novogireevskaya Street, Moscow 111123, Russia; Federal Research Center «Computer Science and Control», Russian Academy of Sciences, 44 Vavilova Street, 2 building, Moscow 119333, Russia

    doctor of science (physics and mathematics), full professor; senior researcher at the scientific group of mathematical methods and epidemiological forecasting, Central Research Institute of Epidemiology, Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing, and leading researcher at the Dorodnicyn Computing Centre, Russian Academy of Sciences, Federal Research Center «Computer Science and Control», Russian Academy of Sciences

  • Anna V. Kuznetsova, Central Research Institute of Epidemiology, Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing; Institute of Biochemical Physics named after N. M. Emanuel, Russian Academy of Sciences, 4 Kosygina Street, Moscow 119334, Russia

    PhD (biology); senior researcher at the scientific group of mathematical methods and epidemiological forecasting, Central Research Institute of Epidemiology, Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing, and senior researcher at the laboratory of mathematical biophysics, Institute of Biochemical Physics named after N. M. Emanuel, Russian Academy of Sciences

  • Evgeny M. Voronin, Central Research Institute of Epidemiology, Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing, 3a Novogireevskaya Street, Moscow 111123, Russia

    PhD (medicine); head of the scientific group of mathematical methods and epidemiological forecasting

  • Olga A. Kravtsova, Central Research Institute of Epidemiology, Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing, 3a Novogireevskaya Street, Moscow 111123, Russia; Lomonosov Moscow State University, 1 Leninskie Gory, Moscow 119991, Russia

    methodological expert at the scientific group of mathematical methods and epidemiological forecasting, Central Research Institute of Epidemiology, Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing, and postgraduate student at the department of mathematical methods of forecasting, faculty of computational mathematics and cybernetics, Lomonosov Moscow State University

  • Ludmila R. Borisova, Financial University under the Government of the Russian Federation, 49/2 Leningradskii Avenue, Moscow 125167, Russia

    PhD (physics and mathematics); associate professor at the Department of Mathematics

  • Igor L. Kirilyuk, Institute of Economics, Russian Academy of Sciences, 32 Nakhimovskii Avenue, Moscow 117218, Russia

    researcher at the subcenter «Institutional and evolutionary economics», Center for Institutional and Evolutionary Economics and Applied Problems of Reproduction

  • Vasiliy G. Akimkin, Central Research Institute of Epidemiology, Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing, 3a Novogireevskaya Street, Moscow 111123, Russia

    academician of the Russian Academy of Sciences, doctor of science (medicine), full professor; director

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Published

2022-04-06

Issue

No. 1 (2022): Journal of the Belarusian State University. Mathematics and Informatics

Section

Theoretical Foundations of Computer Science

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How to Cite

[1]
Senko, O.V. et al. 2022. Methods of intellectual data analysis in COVID-19 research. Journal of the Belarusian State University. Mathematics and Informatics. 1 (Apr. 2022), 83–96. DOI:https://doi.org/10.33581/2520-6508-2022-1-83-96.