Журнал «Информационные технологии и вычислительные системы» - A.Y. Popkov, Y.A. Dubnov, Y.S. Popkov Forecasting of COVID-19 Dynamics in EU Using Randomized Machine Learning Applied to Dynamic Models

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A.Y. Popkov, Y.A. Dubnov, Y.S. Popkov Forecasting of COVID-19 Dynamics in EU Using Randomized Machine Learning Applied to Dynamic Models

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	A.Y. Popkov, Y.A. Dubnov, Y.S. Popkov Forecasting of COVID-19 Dynamics in EU Using Randomized Machine Learning Applied to Dynamic Models
Abstract. The work is devoted to application of the theory of Randomized Machine Learning to forecasting of the COVID-19 pandemic based on SIR epidemiological model. We propose two modelling variants, the first is based on estimation of SIR model using real case data, the second is based on the idea of modelling transmission coefficient and its prediction. Comparative study of proposed approach is based on a comparison with the standard least squares approach and is carried out on a dataset of several countries of the European Union. It is shown the performance of the proposed approach and its effectiveness and adequacy under conditions of small amount of data with a high level of uncertainty. Keywords: epidemic modelling; SARS-CoV-2; COVID-19; SIR; randomized machine learning; entropy; entropy estimation; forecasting; randomized forecasting. PP. 67-78. DOI 10.14357/20718632220307 References 1. van den Driessche P. Mathematical Epidemiology / ed. by Brauer Fred, van den Driessche Pauline, and Wu Jianhong. — Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. — Vol. 1945 of Lecture Notes in Mathematics. — P. 147–157. — Access mode: https://doi.org/10.1007/978-3-540-78911-6. 2. Khan Irtesam Mahmud, Haque Ubydul, Kaisar Samiha, Rahman Mohammad Sohel. A Computational Modeling Study of COVID-19 in Bangladesh // The American Journal of Tropical Medicine and Hygiene. — 2021. — jan. — Vol. 104, no. 1. — P. 66–74. 3. Lavielle Marc, Faron Matthieu, Lefevre J´er´emie H., and Zeitoun Jean-David. Predicting the propagation of COVID-19 at an international scale: extension of an SIR model // BMJ Open. — 2021. — may. —Vol. 11, no. 5. — P. e041472. 4. Lawson Andrew B., Kim Joanne. Space-time covid-19 Bayesian SIR modeling in South Carolina // PLOS ONE. — 2021. — mar. — Vol. 16, no. 3. — P. e0242777. 5. Purkayastha Soumik, Bhattacharyya Rupam, Bhaduri Ritwik, Kundu Ritoban, Gu Xuelin, Salvatore Maxwell, Ray Debashree, Mishra Swapnil, Mukherjee Bhramar. A comparison of five epidemiological models for transmission of SARS-CoV-2 in India // BMC Infectious Diseases. — 2021. — jun. — Vol. 21, no. 1. 6. de Andres P. L., de Andres-Bragado L., and Hoessly L. Monitoring and Forecasting COVID-19: Heuristic Regression, Susceptible-Infected-Removed Model and, Spatial Stochastic // Frontiers in Applied Mathematics and Statistics. — 2021. — may. — Vol. 7. 7. Deo Vishal and Grover Vishal. A new extension of statespace SIR model to account for Underreporting – An application to the COVID-19 transmission in California and Florida // Results in Physics. — 2021. —may. — Vol. 24. — P. 104182. 8. Popkov Y.S., Popkov A.Y., Dubnov Y.A. Randomizirovannoe mashinnoe obuchenie: ot empiricheskoi veroyatnosti k entropiinoy randomizacii. — Moscow: LENAND, 2019. — ISBN: 978-5-9710-5908-0. 9. Boltzmann L. On connection between the second law of mechanical theory of heat and probability theory in heat equilibrium theorems / In: Boltzmann L.E. Selected proceedings, Moscow: Nauka, 1984. 10. Jaynes Edwin T. Information theory and statistical mechanics // Physical review. — 1957. — Vol. 106, no. 4. — P. 620–630. 11. Jaynes Edwin T. Probability theory: the logic of science. — Cambridge university press, 2003. 12. Shannon Claude E. Communication theory of secrecy systems // Bell Labs Technical Journal. — 1949. — Vol. 28, no. 4. — P. 656–715. 13. Guidotti Emanuele and Ardia David. COVID-19 Data Hub // Journal of Open Source Software. — 2020. — Vol. 5, no. 51. — P. 2376. — Access mode: https://doi.org/10.21105/joss.02376. 14. COVID-19 Data Hub. — https://www.covid19datahub.io. — 2021. — Accessed: 2021-12-20.

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