### Socio-economic factors in the spread of SARS-COV-2 across Russian regions

#### Abstract

**Relevance.** The worldwide spread of a new infection SARS-CoV-2 makes relevant the analysis of the socio-economic factors that make modern civilization vulnerable to previously unknown diseases. In this regard, the development of mathematical models describing the spread of pandemics like COVID-19 and the identification of socio-economic factors affecting the epidemiological situation in regions is an important research task.

**Research objective.** This study seeks to develop a mathematical model describing the spread of COVID-19, thus enabling the analysis of the main characteristics of the spread of the disease and assessment of the impact of various socio-economic factors.

**Data and methods.** The study relies on the official statistical data on the pandemic presented on coronavirus sites in Russia and other countries, Yandex DataLens dataset service, as well as data from the Federal State Statistics Service. The data were analyzed by using a correlation analysis of COVID-19 incidence parameters and socio-economic characteristics of regions; multivariate regression – to determine the parameters of the probabilistic mathematical model of the spread of the pandemic proposed by the authors; clustering – to group the regions with similar incidence characteristics and exclude the regions with abnormal parameters from the analysis.

**Results.** A mathematical model of the spread of the COVID-19 pandemic is proposed. The parameters of this model are determined on the basis of official statistics on morbidity, in particular the frequency (probability) of infections, the reliability of the disease detection, the probability density of the disease duration, and its average value. Based on the specificity of COVID-19, Russia regions are clustered according to disease-related characteristics. For clusters that include regions with typical disease-related characteristics, a correlation analysis of the relationship between the number of cases and the rate of infection ( with the socio-economic characteristics of the region is carried out. The most significant factors associated with the parameters of the pandemic are identified.

**Conclusions.** The proposed mathematical model of the pandemic and the established correlations between the parameters of the epidemiological situation and the socio-economic characteristics of the regions can be used to make informed decisions regarding the key risk factors and their impact on the course of the pandemic.

#### Keywords

#### Full Text:

PDF#### References

Abdo, M.S., Shah, K., Wahash, H.A., Panchal, S.K. (2020). On a comprehensive model of the novel coronavirus (COVID-19) under Mittag-Leffler derivative. Chaos, Solitons & Fractals, Vol.135:109867, doi: 10.1016/j.chaos.2020.109867

Amosova N.N., Kuklin B.A., Makarova S.B., Maksimov Yu.D. (2001). Probabilistic sections of mathematics. SPb .: Ivan Fedorov, 592.

Bailey, N.T.J. (1975). The Mathematical Theory of Infectious Diseases and Its Application. Griffin, London.

Barlow, N.S., Weinstein, S.J. (2020). Accurate closed-form solution of the SIR epidemic model. Physica D: Nonlinear Phenomena, Vol.408: 132540, doi: 10.1016/j.physd.2020.132540

Barsegyan, A.A., Kupriyanov, M.S., Stepanenko, V.V., Kholod, I.I. (2007). Data analysis technologies. Data mining, Visual mining, Text mining, OLAP. 2nd edition. SPb: BHV-Petersburg, 384.

Chakraborty,T., Ghosh, I. (2020). Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis. Chaos, Solitons & Fractals, Vol.135: 109850, doi:10.1016/j.chaos.2020.109850

Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C. and Wirth, R. (2000). CRISP-DM 1.0 Step-by-step data mining guide: ftp://ftp.software.ibm.com/software/analytics/spss/support/Modeler/Documentation/14/UserManual/CRISP-DM.pdf

Debock, G., Kohonen, T. (2001). Analyze financial data using self-organizing maps. Moscow: Alpina, 317.

Driessche, P., Watmough, J. (2020). Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission.nMathematical Biosciences,Vol.180, 29-48, doi: 10.1016/S0025-5564(02)00108-6

Feller, W. (1964). An introduction to probability theory and its applications. Volume 1.Moscow: Mir, 498.

Fikhtengolts G.M. (1970). Differential and integral calculus course. Volume II. Moscow: FML, 800.

Ivchenko G.I., Medvedev Yu.I. Mathematical Statistics (2014). Moscow: LIBROKOM, 352.

Leskovec, J., Rajaraman,A., Ullman, J.D. (2020). Mining of Massive Datasets. Cambridge University Press, 3 edition, 565.

Ndairou, F., Area, I., Nieto, J.J., Torres D.F.M. (2020). Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan. Chaos, Solitons & Fractals, Vol.135: 109846, doi: 10.1016/j.chaos.2020.109846

Rvachev, L.A. (1971). Experiment on machine forecasting of influenza epidemic. Reports of the USSR Academy of Sciences, volume 198, number 1, 68-70.

Sinitsyn, E.V., Astratova, G.V., Frishberg, L.A. (2011). Using information models to assess intellectual property. Practical Marketing, No. 5 (171), 28-35.

Svirezhev, Yu.M. (1987). Nonlinear waves, dissipative structures and catastrophes in ecology. Moscow: Nauka, 368.

Tolmachev, A. V., Sinitsyn, E. V., & Brusyanin, D. A. (2019). Transport system modelling based on analogies between road networks and electrical circuits. R-economy, 5(2), 92–98.

Whittle, P. (1955). The outcome of a stochastic epidemic – a note on Bailey’s paper. Biometrika, Vol.42, Issue 1-2, 116–122, doi: 10.1093/biomet/42.1-2.116

Xiaolei Zhang, Renjun Ma and Lin Wang (2020). Predicting turning point, duration and attack rate of COVID-19 outbreaks in major Western countries. Chaos, Solitons & Fractals, Vol.135:109829, doi: 10.1016/j.chaos.2020.109829

Zhambyu, M. (1988). Hierarchical Cluster Analysis and Compliance. Moscow: Finance and Statistics, 345.

DOI: https://doi.org/10.15826/recon.2020.6.3.011

Copyright (c) 2020 Evgeny V. Sinitsyn, Alexander V. Tolmachev, Alexander S. Ovchinnikov