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Numerical Investigations through ANNs for Solving COVID-19 Model

Author

Listed:
  • Muhammad Umar

    (Department of Mathematics and Statistics, Hazara University, Mansehra 21300, Pakistan)

  • Zulqurnain Sabir

    (Department of Mathematics and Statistics, Hazara University, Mansehra 21300, Pakistan)

  • Muhammad Asif Zahoor Raja

    (Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou 64002, Taiwan)

  • Shumaila Javeed

    (Department of Mathematics, Islamabad Campus, COMSATS University Islamabad, Park Road, Islamabad 45550, Pakistan)

  • Hijaz Ahmad

    (Department of Computer Engineering, Biruni University, Istanbul 34025, Turkey
    Section of Mathematics, International Telematic University Uninettuno, Corso Vittorio Emanuele II, 39, 00186 Roma, Italy)

  • Sayed K. Elagen

    (Department of Mathematics and Statistics, College of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

  • Ahmed Khames

    (Department of Pharmaceutics and Industrial Pharmacy, College of Pharmacy, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

Abstract

The current investigations of the COVID-19 spreading model are presented through the artificial neuron networks (ANNs) with training of the Levenberg-Marquardt backpropagation (LMB), i.e., ANNs-LMB. The ANNs-LMB scheme is used in different variations of the sample data for training, validation, and testing with 80%, 10%, and 10%, respectively. The approximate numerical solutions of the COVID-19 spreading model have been calculated using the ANNs-LMB and compared viably using the reference dataset based on the Runge-Kutta scheme. The obtained performance of the solution dynamics of the COVID-19 spreading model are presented based on the ANNs-LMB to minimize the values of fitness on mean square error (M.S.E), along with error histograms, regression, and correlation analysis.

Suggested Citation

  • Muhammad Umar & Zulqurnain Sabir & Muhammad Asif Zahoor Raja & Shumaila Javeed & Hijaz Ahmad & Sayed K. Elagen & Ahmed Khames, 2021. "Numerical Investigations through ANNs for Solving COVID-19 Model," IJERPH, MDPI, vol. 18(22), pages 1-15, November.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:22:p:12192-:d:683850
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    References listed on IDEAS

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    1. Marcus de Barros Braga & Rafael da Silva Fernandes & Gilberto Nerino de Souza Jr & Jonas Elias Castro da Rocha & Cícero Jorge Fonseca Dolácio & Ivaldo da Silva Tavares Jr & Raphael Rodrigues Pinheiro , 2021. "Artificial neural networks for short-term forecasting of cases, deaths, and hospital beds occupancy in the COVID-19 pandemic at the Brazilian Amazon," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-27, March.
    2. Amr Elsonbaty & Zulqurnain Sabir & Rajagopalan Ramaswamy & Waleed Adel, 2021. "Dynamical Analysis Of A Novel Discrete Fractional Sitrs Model For Covid-19," FRACTALS (fractals), World Scientific Publishing Co. Pte. Ltd., vol. 29(08), pages 1-15, December.
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    Cited by:

    1. Anwar, Nabeela & Ahmad, Iftikhar & Kiani, Adiqa Kausar & Shoaib, Muhammad & Raja, Muhammad Asif Zahoor, 2024. "Novel intelligent predictive networks for analysis of chaos in stochastic differential SIS epidemic model with vaccination impact," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 219(C), pages 251-283.

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