IDEAS home Printed from https://ideas.repec.org/a/aiy/journl/v6y2020i3p129-145.html
   My bibliography  Save this article

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

Author

Listed:
  • Sinitsyn, E. V.
  • Tolmachev, A. V.
  • Ovchinnikov, A. S.

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.

Suggested Citation

  • Sinitsyn, E. V. & Tolmachev, A. V. & Ovchinnikov, A. S., 2020. "Socio-economic factors in the spread of SARS-COV-2 across Russian regions," R-Economy, Ural Federal University, Graduate School of Economics and Management, vol. 6(3), pages 129-145.
    • Handle: RePEc:aiy:journl:v:6:y:2020:i:3:p:129-145
    DOI: 10.15826/recon.2020.6.3.011
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10995/92816
    Download Restriction: no
    File URL: https://libkey.io/10.15826/recon.2020.6.3.011?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Chakraborty, Tanujit & Ghosh, Indrajit, 2020. "Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    2. Ndaïrou, Faïçal & Area, Iván & Nieto, Juan J. & Torres, Delfim F.M., 2020. "Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    3. Zhang, Xiaolei & Ma, Renjun & Wang, Lin, 2020. "Predicting turning point, duration and attack rate of COVID-19 outbreaks in major Western countries," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    4. Tolmachev, A. V. & Sinitsyn, E. V. & Brusyanin, D. A., 2019. "Transport System Modelling Based on Analogies Between Road Networks and Electrical Circuits," R-Economy, Ural Federal University, Graduate School of Economics and Management, vol. 5(2), pages 92-98.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. da Silva, Ramon Gomes & Ribeiro, Matheus Henrique Dal Molin & Mariani, Viviana Cocco & Coelho, Leandro dos Santos, 2020. "Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variables," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    2. Koutsellis, Themistoklis & Nikas, Alexandros, 2020. "A predictive model and country risk assessment for COVID-19: An application of the Limited Failure Population concept," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    3. Dalton Garcia Borges de Souza & Erivelton Antonio dos Santos & Francisco Tarcísio Alves Júnior & Mariá Cristina Vasconcelos Nascimento, 2021. "On Comparing Cross-Validated Forecasting Models with a Novel Fuzzy-TOPSIS Metric: A COVID-19 Case Study," Sustainability, MDPI, vol. 13(24), pages 1-25, December.
    4. Amaral, Marco A. & Oliveira, Marcelo M. de & Javarone, Marco A., 2021. "An epidemiological model with voluntary quarantine strategies governed by evolutionary game dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    5. Fredj, Haifa Ben & Chérif, Farouk, 2020. "Novel Corona virus disease infection in Tunisia: Mathematical model and the impact of the quarantine strategy," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    6. Pelinovsky, Efim & Kurkin, Andrey & Kurkina, Oxana & Kokoulina, Maria & Epifanova, Anastasia, 2020. "Logistic equation and COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    7. Swapnarekha, H. & Behera, Himansu Sekhar & Nayak, Janmenjoy & Naik, Bighnaraj, 2020. "Role of intelligent computing in COVID-19 prognosis: A state-of-the-art review," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    8. Rafael Pérez Abreu C. & Samantha Estrada & Héctor de-la-Torre-Gutiérrez, 2021. "A Two-Step Polynomial and Nonlinear Growth Approach for Modeling COVID-19 Cases in Mexico," Mathematics, MDPI, vol. 9(18), pages 1-18, September.
    9. Milad Haghani & Michiel C. J. Bliemer, 2020. "Covid-19 pandemic and the unprecedented mobilisation of scholarly efforts prompted by a health crisis: Scientometric comparisons across SARS, MERS and 2019-nCoV literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2695-2726, December.
    10. Gregory L Watson & Di Xiong & Lu Zhang & Joseph A Zoller & John Shamshoian & Phillip Sundin & Teresa Bufford & Anne W Rimoin & Marc A Suchard & Christina M Ramirez, 2021. "Pandemic velocity: Forecasting COVID-19 in the US with a machine learning & Bayesian time series compartmental model," PLOS Computational Biology, Public Library of Science, vol. 17(3), pages 1-20, March.
    11. Aldila, Dipo & Khoshnaw, Sarbaz H.A. & Safitri, Egi & Anwar, Yusril Rais & Bakry, Aanisah R.Q. & Samiadji, Brenda M. & Anugerah, Demas A. & GH, M. Farhan Alfarizi & Ayulani, Indri D. & Salim, Sheryl N, 2020. "A mathematical study on the spread of COVID-19 considering social distancing and rapid assessment: The case of Jakarta, Indonesia," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    12. Mandal, Manotosh & Jana, Soovoojeet & Nandi, Swapan Kumar & Khatua, Anupam & Adak, Sayani & Kar, T.K., 2020. "A model based study on the dynamics of COVID-19: Prediction and control," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    13. Ahumada, M. & Ledesma-Araujo, A. & Gordillo, L. & Marín, J.F., 2023. "Mutation and SARS-CoV-2 strain competition under vaccination in a modified SIR model," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    14. Vahideh Vakil & Wade Trappe, 2022. "Projecting the Pandemic Trajectory through Modeling the Transmission Dynamics of COVID-19," IJERPH, MDPI, vol. 19(8), pages 1-28, April.
    15. Maher Abida & Emna Mnif, 2023. "Investor Attention in Cryptocurrency Markets: Examining the Effects of Vaccination and COVID-19 Spread through a Wavelet Approach," International Journal of Economics and Financial Issues, Econjournals, vol. 13(5), pages 43-51, September.
    16. Aman Khakharia & Vruddhi Shah & Sankalp Jain & Jash Shah & Amanshu Tiwari & Prathamesh Daphal & Mahesh Warang & Ninad Mehendale, 2021. "Outbreak Prediction of COVID-19 for Dense and Populated Countries Using Machine Learning," Annals of Data Science, Springer, vol. 8(1), pages 1-19, March.
    17. Usama H. Issa & Ashraf Balabel & Mohammed Abdelhakeem & Medhat M. A. Osman, 2021. "Developing a Risk Model for Assessment and Control of the Spread of COVID-19," Risks, MDPI, vol. 9(2), pages 1-15, February.
    18. Nikola Anđelić & Sandi Baressi Šegota & Ivan Lorencin & Zdravko Jurilj & Tijana Šušteršič & Anđela Blagojević & Alen Protić & Tomislav Ćabov & Nenad Filipović & Zlatan Car, 2021. "Estimation of COVID-19 Epidemiology Curve of the United States Using Genetic Programming Algorithm," IJERPH, MDPI, vol. 18(3), pages 1-26, January.
    19. Gaetano Perone, 2022. "Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(6), pages 917-940, August.
    20. Matouk, A.E., 2020. "Complex dynamics in susceptible-infected models for COVID-19 with multi-drug resistance," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aiy:journl:v:6:y:2020:i:3:p:129-145. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Irina Turgel (email available below). General contact details of provider: https://edirc.repec.org/data/seurfru.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.