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

Matvey Pavlyutin, Marina Samoyavcheva, Rasul Kochkarov, Ekaterina Pleshakova, Sergey Korchagin, Timur Gataullin, Petr Nikitin and Mohiniso Hidirova
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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

Mathematics, 2022, vol. 10, issue 2, 1-19

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.

Keywords: COVID-19; epidemic spreading; forecasting; mathematical methods; machine learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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