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Using Artificial Neural Network with Prey Predator Algorithm for Prediction of the COVID-19: The Case of Brazil and Mexico

Author

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  • Nawaf N. Hamadneh

    (Department of Basic Sciences, College of Science and Theoretical Studies, Saudi Electronic University, Riyadh 11673, Saudi Arabia)

  • Muhammad Tahir

    (Department of Computer Science, College of Computing and Informatics, Saudi Electronic University, Riyadh 11673, Saudi Arabia)

  • Waqar A. Khan

    (Department of Mechanical Engineering, College of Engineering, Prince Mohammad Bin Fahd University, Al Khobar 31952, Saudi Arabia)

Abstract

The spread of the COVID-19 epidemic worldwide has led to investigations in various aspects, including the estimation of expected cases. As it helps in identifying the need to deal with cases caused by the pandemic. In this study, we have used artificial neural networks (ANNs) to predict the number of cases of COVID-19 in Brazil and Mexico in the upcoming days. Prey predator algorithm (PPA), as a type of metaheuristic algorithm, is used to train the models. The proposed ANN models’ performance has been analyzed by the root mean squared error (RMSE) function and correlation coefficient (R). It is demonstrated that the ANN models have the highest performance in predicting the number of infections (active cases), recoveries, and deaths in Brazil and Mexico. The simulation results of the ANN models show very well predicted values. Percentages of the ANN’s prediction errors with metaheuristic algorithms are significantly lower than traditional monolithic neural networks. The study shows the expected numbers of infections, recoveries, and deaths that Brazil and Mexico will reach daily at the beginning of 2021.

Suggested Citation

  • Nawaf N. Hamadneh & Muhammad Tahir & Waqar A. Khan, 2021. "Using Artificial Neural Network with Prey Predator Algorithm for Prediction of the COVID-19: The Case of Brazil and Mexico," Mathematics, MDPI, vol. 9(2), pages 1-14, January.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:2:p:180-:d:482120
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    References listed on IDEAS

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    Cited by:

    1. Meng Zhu & Jing Li & Xinze Lian, 2022. "Pattern Dynamics of Cross Diffusion Predator–Prey System with Strong Allee Effect and Hunting Cooperation," Mathematics, MDPI, vol. 10(17), pages 1-20, September.
    2. Huiping Li & Yunxuan Li, 2023. "A Novel Explanatory Tabular Neural Network to Predicting Traffic Incident Duration Using Traffic Safety Big Data," Mathematics, MDPI, vol. 11(13), pages 1-24, June.
    3. Yoshihiko Kadoya & Somtip Watanapongvanich & Pattaphol Yuktadatta & Pongpat Putthinun & Stella T. Lartey & Mostafa Saidur Rahim Khan, 2021. "Willing or Hesitant? A Socioeconomic Study on the Potential Acceptance of COVID-19 Vaccine in Japan," IJERPH, MDPI, vol. 18(9), pages 1-18, May.

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