IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i2p195-d720646.html
   My bibliography  Save this article

COVID-19 Spread Forecasting, Mathematical Methods vs. Machine Learning, Moscow Case

Author

Listed:
  • Matvey Pavlyutin

    (Department of Data Analysis and Machine Learning, Financial University under the Government of the Russian Federation, 109456 Moscow, Russia)

  • Marina Samoyavcheva

    (Department of Data Analysis and Machine Learning, Financial University under the Government of the Russian Federation, 109456 Moscow, Russia)

  • Rasul Kochkarov

    (Department of Data Analysis and Machine Learning, Financial University under the Government of the Russian Federation, 109456 Moscow, Russia)

  • Ekaterina Pleshakova

    (Department of Data Analysis and Machine Learning, Financial University under the Government of the Russian Federation, 109456 Moscow, Russia)

  • Sergey Korchagin

    (Department of Data Analysis and Machine Learning, Financial University under the Government of the Russian Federation, 109456 Moscow, Russia)

  • Timur Gataullin

    (Department of Mathematical Methods in Economics and Management, State University of Management, 109542 Moscow, Russia)

  • Petr Nikitin

    (Department of Data Analysis and Machine Learning, Financial University under the Government of the Russian Federation, 109456 Moscow, Russia)

  • Mohiniso Hidirova

    (Research Institute for Development of Digital Technologies and Artificial Intelligence, Tashkent 100094, Uzbekistan)

Abstract

To predict the spread of the new coronavirus infection COVID-19, the critical values of spread indicators have been determined for deciding on the introduction of restrictive measures using the city of Moscow as an example. A model was developed using classical methods of mathematical modeling based on exponential regression, the accuracy of the forecast was estimated, and the shortcomings of mathematical methods for predicting the spread of infection for more than two weeks. As a solution to the problem of the accuracy of long-term forecasts for more than two weeks, two models based on machine learning methods are proposed: a recurrent neural network with two layers of long short-term memory (LSTM) blocks and a 1-D convolutional neural network with a description of the choice of an optimization algorithm. The forecast accuracy of ML models was evaluated in comparison with the exponential regression model and one another using the example of data on the number of COVID-19 cases in the city of Moscow.

Suggested Citation

  • Matvey Pavlyutin & Marina Samoyavcheva & Rasul Kochkarov & Ekaterina Pleshakova & Sergey Korchagin & Timur Gataullin & Petr Nikitin & Mohiniso Hidirova, 2022. "COVID-19 Spread Forecasting, Mathematical Methods vs. Machine Learning, Moscow Case," Mathematics, MDPI, vol. 10(2), pages 1-19, January.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:2:p:195-:d:720646
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/2/195/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/2/195/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Contreras, Sebastián & Villavicencio, H. Andrés & Medina-Ortiz, David & Biron-Lattes, Juan Pablo & Olivera-Nappa, Álvaro, 2020. "A multi-group SEIRA model for the spread of COVID-19 among heterogeneous populations," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    2. Martelloni, Gabriele & Martelloni, Gianluca, 2020. "Analysis of the evolution of the Sars-Cov-2 in Italy, the role of the asymptomatics and the success of Logistic model," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    3. Wang, Peipei & Zheng, Xinqi & Li, Jiayang & Zhu, Bangren, 2020. "Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    4. Atanasov, Atanas & Marinova, Rumyana, 2020. "Интегрираното Отчитане Като Инструмент За Комуникиране На Корпоративна Информация В Условията На Covid-19 [Integrated reporting as a tool for communication of corporate information in the COVID-19 ," MPRA Paper 105256, University Library of Munich, Germany.
    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. Askar Akaev & Alexander I. Zvyagintsev & Askar Sarygulov & Tessaleno Devezas & Andrea Tick & Yuri Ichkitidze, 2022. "Growth Recovery and COVID-19 Pandemic Model: Comparative Analysis for Selected Emerging Economies," Mathematics, MDPI, vol. 10(19), pages 1-18, October.
    2. Wang, Peipei & Zheng, Xinqi & Ai, Gang & Liu, Dongya & Zhu, Bangren, 2020. "Time series prediction for the epidemic trends of COVID-19 using the improved LSTM deep learning method: Case studies in Russia, Peru and Iran," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    3. Fadaki, Masih & Asadikia, Atie, 2024. "Augmenting Monte Carlo Tree Search for managing service level agreements," International Journal of Production Economics, Elsevier, vol. 271(C).
    4. Munir Ahmad & Nadeem Akhtar & Gul Jabeen & Muhammad Irfan & Muhammad Khalid Anser & Haitao Wu & Cem Işık, 2021. "Intention-Based Critical Factors Affecting Willingness to Adopt Novel Coronavirus Prevention in Pakistan: Implications for Future Pandemics," IJERPH, MDPI, vol. 18(11), pages 1-28, June.
    5. 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.
    6. Wang, Renfei & Li, Yilin & Wu, Dayu & Zou, Yong & Tang, Ming & Guan, Shuguang & Liu, Ying & Jin, Zhen & Pelinovsky, Efim & Kirillin, Mikhail & Macau, Elbert, 2024. "Impact of agent-based intervention strategies on the COVID-19 pandemic in large-scale dynamic contact networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 646(C).
    7. Pawan Kumar Singh & Anushka Chouhan & Rajiv Kumar Bhatt & Ravi Kiran & Ansari Saleh Ahmar, 2022. "Implementation of the SutteARIMA method to predict short-term cases of stock market and COVID-19 pandemic in USA," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(4), pages 2023-2033, August.
    8. Md Rashidul Hasan & Muntasir A Kabir & Rezoan A Shuvro & Pankaz Das, 2022. "A Comparative Study on Forecasting of Retail Sales," Papers 2203.06848, arXiv.org.
    9. Omer Bagais & Khaled Aljaaidi & Abdulaziz Alothman, 2021. "An Empirical Investigation of the Associations of Short and Long Debt Policies with Economic Values of Energy Sector," International Journal of Energy Economics and Policy, Econjournals, vol. 11(1), pages 249-254.
    10. Maria Palazzo & Iza Gigauri & Mirela Clementina Panait & Simona Andreea Apostu & Alfonso Siano, 2022. "Sustainable Tourism Issues in European Countries during the Global Pandemic Crisis," Sustainability, MDPI, vol. 14(7), pages 1-21, March.
    11. Kim-Hung Pho & Michael McAleer, 2021. "Specification and Estimation of a Logistic Function, with Applications in the Sciences and Social Sciences," Advances in Decision Sciences, Asia University, Taiwan, vol. 25(2), pages 74-104, June.
    12. Freire-Flores, Danton & Llanovarced-Kawles, Nyna & Sanchez-Daza, Anamaria & Olivera-Nappa, Álvaro, 2021. "On the heterogeneous spread of COVID-19 in Chile," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
    13. Matouk, A.E., 2020. "Complex dynamics in susceptible-infected models for COVID-19 with multi-drug resistance," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    14. Otunuga, Olusegun Michael, 2021. "Time-dependent probability distribution for number of infection in a stochastic SIS model: case study COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 147(C).
    15. Paul, Ayan & Reja, Selim & Kundu, Sayani & Bhattacharya, Sabyasachi, 2021. "COVID-19 pandemic models revisited with a new proposal: Plenty of epidemiological models outcast the simple population dynamics solution," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    16. Ben R. Craig & Tom Phelan & Jan-Peter Siedlarek & Jared Steinberg, 2021. "Two Approaches to Predicting the Path of the COVID-19 Pandemic: Is One Better?," Economic Commentary, Federal Reserve Bank of Cleveland, vol. 2021(10), pages 1-8, April.
    17. Contreras, Sebastian & Oróstica, Karen Y. & Daza-Sanchez, Anamaria & Wagner, Joel & Dönges, Philipp & Medina-Ortiz, David & Jara, Matias & Verdugo, Ricardo & Conca, Carlos & Priesemann, Viola & Oliver, 2023. "Model-based assessment of sampling protocols for infectious disease genomic surveillance," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    18. Lyudmil Naydenov & Dimitar Tsenov, 2021. "Covid-19 Effects On Tax Gaps," Economic Archive, D. A. Tsenov Academy of Economics, Svishtov, Bulgaria, issue 1 Year 20, pages 15-27.
    19. Md Akhtarul Islam & Sutapa Dey Barna & Hasin Raihan & Md Nafiul Alam Khan & Md Tanvir Hossain, 2020. "Depression and anxiety among university students during the COVID-19 pandemic in Bangladesh: A web-based cross-sectional survey," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-12, August.
    20. Malki, Zohair & Atlam, El-Sayed & Hassanien, Aboul Ella & Dagnew, Guesh & Elhosseini, Mostafa A. & Gad, Ibrahim, 2020. "Association between weather data and COVID-19 pandemic predicting mortality rate: Machine learning approaches," Chaos, Solitons & Fractals, Elsevier, vol. 138(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:gam:jmathe:v:10:y:2022:i:2:p:195-:d:720646. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.