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Neural network powered COVID-19 spread forecasting model

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  • Wieczorek, Michał
  • Siłka, Jakub
  • Woźniak, Marcin

Abstract

Virus spread prediction is very important to actively plan actions. Viruses are unfortunately not easy to control, since speed and reach of spread depends on many factors from environmental to social ones. In this article we present research results on developing Neural Network model for COVID-19 spread prediction. Our predictor is based on classic approach with deep architecture which learns by using NAdam training model. For the training we have used official data from governmental and open repositories. Results of prediction are done for countries but also regions to provide possibly wide spectrum of values about predicted COVID-19 spread. Results of the proposed model show high accuracy, which in some cases reaches above 99%.

Suggested Citation

  • Wieczorek, Michał & Siłka, Jakub & Woźniak, Marcin, 2020. "Neural network powered COVID-19 spread forecasting model," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    • Handle: RePEc:eee:chsofr:v:140:y:2020:i:c:s0960077920305993
    DOI: 10.1016/j.chaos.2020.110203
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    References listed on IDEAS

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    1. Fanelli, Duccio & Piazza, Francesco, 2020. "Analysis and forecast of COVID-19 spreading in China, Italy and France," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).
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    3. Perone, G., 2020. "Comparison of ARIMA, ETS, NNAR and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy," Health, Econometrics and Data Group (HEDG) Working Papers 20/18, HEDG, c/o Department of Economics, University of York.
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    7. Olga Krivorotko & Nikolay Zyatkov, 2024. "The Forecasting of the Spread of Infectious Diseases Based on Conditional Generative Adversarial Networks," Mathematics, MDPI, vol. 12(19), pages 1-22, September.
    8. Yas Al-Hadeethi & Intesar F. El Ramley & Hiba Mohammed & Nada M. Bedaiwi & Abeer Z. Barasheed, 2024. "A Novel Computational Instrument Based on a Universal Mixture Density Network with a Gaussian Mixture Model as a Backbone for Predicting COVID-19 Variants’ Distributions," Mathematics, MDPI, vol. 12(8), pages 1-24, April.
    9. Çaparoğlu, Ömer Faruk & Ok, Yeşim & Tutam, Mahmut, 2021. "To restrict or not to restrict? Use of artificial neural network to evaluate the effectiveness of mitigation policies: A case study of Turkey," Chaos, Solitons & Fractals, Elsevier, vol. 151(C).
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    11. Jayles, Bertrand & Cheong, Siew Ann & Herrmann, Hans J., 2022. "Modeling the resilience of social networks to lockdowns regarding the dynamics of meetings," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 602(C).

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