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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
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    References listed on IDEAS

    as
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    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.
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