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

1
Department of Data Analysis and Machine Learning, Financial University under the Government of the Russian Federation, 109456 Moscow, Russia
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Department of Mathematical Methods in Economics and Management, State University of Management, 109542 Moscow, Russia
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Research Institute for Development of Digital Technologies and Artificial Intelligence, Tashkent 100094, Uzbekistan
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Author to whom correspondence should be addressed.
Academic Editor: Georgios Tsekouras
Mathematics 2022, 10(2), 195; https://doi.org/10.3390/math10020195
Received: 25 November 2021 / Revised: 2 January 2022 / Accepted: 6 January 2022 / Published: 9 January 2022
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. View Full-Text
Keywords: COVID-19; epidemic spreading; forecasting; mathematical methods; machine learning COVID-19; epidemic spreading; forecasting; mathematical methods; machine learning
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MDPI and ACS Style

Pavlyutin, M.; Samoyavcheva, M.; Kochkarov, R.; Pleshakova, E.; Korchagin, S.; Gataullin, T.; Nikitin, P.; Hidirova, M. COVID-19 Spread Forecasting, Mathematical Methods vs. Machine Learning, Moscow Case. Mathematics 2022, 10, 195. https://doi.org/10.3390/math10020195

AMA Style

Pavlyutin M, Samoyavcheva M, Kochkarov R, Pleshakova E, Korchagin S, Gataullin T, Nikitin P, Hidirova M. COVID-19 Spread Forecasting, Mathematical Methods vs. Machine Learning, Moscow Case. Mathematics. 2022; 10(2):195. https://doi.org/10.3390/math10020195

Chicago/Turabian Style

Pavlyutin, Matvey, Marina Samoyavcheva, Rasul Kochkarov, Ekaterina Pleshakova, Sergey Korchagin, Timur Gataullin, Petr Nikitin, and Mohiniso Hidirova. 2022. "COVID-19 Spread Forecasting, Mathematical Methods vs. Machine Learning, Moscow Case" Mathematics 10, no. 2: 195. https://doi.org/10.3390/math10020195

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