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Forecasting of COVID-19 Epidemic Process in Ukraine and Neighboring Countries by Gradient Boosting Method

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Information Technology for Education, Science, and Technics (ITEST 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 178))

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Abstract

The new coronavirus has changed the life of the planet and continues to spread around the world. Mathematical modeling allows the development of effective scientifically substantiated preventive and anti-epidemic measures. Machine learning methods have the highest accuracy when constructing the predicted incidence of infectious diseases. In this work, a model of gradient boosting is built to calculate the predicted incidence of COVID-19. To investigate epidemic process in Ukraine, we have built simulation model for Ukraine and its neighbors: Belarus, Hungary, Moldova, Poland, Romania, Russia, Slovakia. To verify the model, real data on the incidence of coronavirus were used. These countries were chosen because they have different dynamics of the epidemic process, different control measures and influenced the dynamics of COVID-19 epidemic process in Ukraine.

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Acknowledgement

The study was funded by the Ministry of Education and Science of Ukraine in the framework of the research project 0121U109814 on the topic “Sociological and mathematical modeling of the effectiveness of managing social and epidemic processes to ensure the national security of Ukraine” [21].

Author information

Authors and Affiliations

  1. National Aerospace University “Kharkiv Aviation Institute”, Kharkiv, Ukraine

    Dmytro Chumachenko & Ievgen Meniailov

  2. Kharkiv National Medical University, Kharkiv, Ukraine

    Tetyana Chumachenko

  3. V.N. Karazin Kharkiv National University, Kharkiv, Ukraine

    Olena Muradyan & Grigoriy Zholtkevych

Authors
  1. Dmytro Chumachenko
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  2. Tetyana Chumachenko
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  3. Ievgen Meniailov
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  4. Olena Muradyan
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  5. Grigoriy Zholtkevych
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Corresponding author

Correspondence to Dmytro Chumachenko .

Editor information

Editors and Affiliations

  1. Department of Information Security and Computer Engineering, Cherkasy State Technological University, Cherkasy, Ukraine

    Emil Faure

  2. Department of Computer science and Systems Analysis, Cherkasy State Technological University, Cherkasy, Ukraine

    Olena Danchenko

  3. Department of Instrumentation, Mechatronics and Computerized Technologies, Cherkasy State Technological University, Cherkasy, Ukraine

    Maksym Bondarenko

  4. Department of Computer science and Systems Analysis, Cherkasy State Technological University, Cherkasy, Ukraine

    Yurii Tryus

  5. Department of Instrumentation, Mechatronics and Computerized Technologies, Cherkasy State Technological University, Cherkasy, Ukraine

    Constantine Bazilo

  6. Department of Software of Automated Systems, Cherkasy State Technological University, Cherkasy, Ukraine

    Grygoriy Zaspa

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Chumachenko, D., Chumachenko, T., Meniailov, I., Muradyan, O., Zholtkevych, G. (2023). Forecasting of COVID-19 Epidemic Process in Ukraine and Neighboring Countries by Gradient Boosting Method. In: Faure, E., Danchenko, O., Bondarenko, M., Tryus, Y., Bazilo, C., Zaspa, G. (eds) Information Technology for Education, Science, and Technics. ITEST 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 178. Springer, Cham. https://doi.org/10.1007/978-3-031-35467-0_30

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  • DOI: https://doi.org/10.1007/978-3-031-35467-0_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-35466-3

  • Online ISBN: 978-3-031-35467-0

  • eBook Packages: EngineeringEngineering (R0)

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