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COVID-19 Pandemic Prediction for Hungary; A Hybrid Machine Learning Approach

Author

Listed:
  • Gergo Pinter

    (John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary)

  • Imre Felde

    (John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary)

  • Amir Mosavi

    (Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany
    Kalman Kando Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary
    Thuringian Institute of Sustainability and Climate Protection, 07743 Jena, Germany
    Department of Mathematics, J. Selye University, 94501 Komarno, Slovakia)

  • Pedram Ghamisi

    (Machine Learning Group, Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Chemnitzer Straße 40, 09599 Freiberg, Germany)

  • Richard Gloaguen

    (Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Chemnitzer Straße 40, 09599 Freiberg, Germany)

Abstract

Several epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to the lack of essential data and uncertainty, the epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19, and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are proposed to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for 9 days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.

Suggested Citation

  • Gergo Pinter & Imre Felde & Amir Mosavi & Pedram Ghamisi & Richard Gloaguen, 2020. "COVID-19 Pandemic Prediction for Hungary; A Hybrid Machine Learning Approach," Mathematics, MDPI, vol. 8(6), pages 1-20, June.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:6:p:890-:d:366145
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    References listed on IDEAS

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    Cited by:

    1. Csaba G. TÓTH, 2022. "Narrowing the gap in regional and age-specific excess mortality during the COVID-19 in Hungary," Eastern Journal of European Studies, Centre for European Studies, Alexandru Ioan Cuza University, vol. 13, pages 185-207, June.
    2. Haghighat, Fatemeh, 2021. "Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    3. Abdelrahman E. E. Eltoukhy & Ibrahim Abdelfadeel Shaban & Felix T. S. Chan & Mohammad A. M. Abdel-Aal, 2020. "Data Analytics for Predicting COVID-19 Cases in Top Affected Countries: Observations and Recommendations," IJERPH, MDPI, vol. 17(19), pages 1-25, September.
    4. Gabriel Sepulveda & Abraham J. Arenas & Gilberto González-Parra, 2023. "Mathematical Modeling of COVID-19 Dynamics under Two Vaccination Doses and Delay Effects," Mathematics, MDPI, vol. 11(2), pages 1-30, January.
    5. Yulan Li & Kun Ma, 2022. "A Hybrid Model Based on Improved Transformer and Graph Convolutional Network for COVID-19 Forecasting," IJERPH, MDPI, vol. 19(19), pages 1-17, September.
    6. Jelena Musulin & Sandi Baressi Šegota & Daniel Štifanić & Ivan Lorencin & Nikola Anđelić & Tijana Šušteršič & Anđela Blagojević & Nenad Filipović & Tomislav Ćabov & Elitza Markova-Car, 2021. "Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review," IJERPH, MDPI, vol. 18(8), pages 1-39, April.
    7. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," FrenXiv e75gc_v1, Center for Open Science.
    8. Mustapha Kamal Benramdane & Elena Kornyshova & Samia Bouzefrane & Hubert Maupas, 2024. "Supervised Machine Learning for Matchmaking in Digital Business Ecosystems and Platforms," Information Systems Frontiers, Springer, vol. 26(4), pages 1331-1343, August.
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    10. Amir Masoud Rahmani & Efat Yousefpoor & Mohammad Sadegh Yousefpoor & Zahid Mehmood & Amir Haider & Mehdi Hosseinzadeh & Rizwan Ali Naqvi, 2021. "Machine Learning (ML) in Medicine: Review, Applications, and Challenges," Mathematics, MDPI, vol. 9(22), pages 1-52, November.
    11. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," LawArchive kczj5_v1, Center for Open Science.
    12. repec:osf:edarxi:5dwrt_v1 is not listed on IDEAS
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    15. Abraham J. Arenas & Gilberto González-Parra & Miguel Saenz Saenz, 2024. "Qualitative Analysis of a COVID-19 Mathematical Model with a Discrete Time Delay," Mathematics, MDPI, vol. 13(1), pages 1-23, December.
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    17. repec:osf:metaar:haf2v_v1 is not listed on IDEAS

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