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On Predictive Modeling Using a New Flexible Weibull Distribution and Machine Learning Approach: Analyzing the COVID-19 Data

Author

Listed:
  • Zubair Ahmad

    (Department of Statistics, Yazd University, Yazd P.O. Box 89175-741, Iran)

  • Zahra Almaspoor

    (Department of Statistics, Yazd University, Yazd P.O. Box 89175-741, Iran)

  • Faridoon Khan

    (PIDE School of Economics, Islamabad 44000, Pakistan)

  • Mahmoud El-Morshedy

    (Department of Mathematics, College of Science and Humanities in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
    Department of Mathematics, Faculty of Science, Mansoura University, Mansoura 35516, Egypt)

Abstract

Predicting and modeling time-to-events data is a crucial and interesting research area. For modeling and predicting such types of data, numerous statistical models have been suggested and implemented. This study introduces a new statistical model, namely, a new modified flexible Weibull extension (NMFWE) distribution for modeling the mortality rate of COVID-19 patients. The introduced model is obtained by modifying the flexible Weibull extension model. The maximum likelihood estimators of the NMFWE model are obtained. The evaluation of the estimators of the NMFWE model is assessed in a simulation study. The flexibility and applicability of the NMFWE model are established by taking two datasets representing the mortality rates of COVID-19-infected persons in Mexico and Canada. For predictive modeling, we consider two pure statistical models and two machine learning (ML) algorithms. The pure statistical models include the autoregressive moving average (ARMA) and non-parametric autoregressive moving average (NP-ARMA), and the ML algorithms include neural network autoregression (NNAR) and support vector regression (SVR). To evaluate their forecasting performance, three standard measures of accuracy, namely, root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are calculated. The findings demonstrate that ML algorithms are very effective at predicting the mortality rate data.

Suggested Citation

  • Zubair Ahmad & Zahra Almaspoor & Faridoon Khan & Mahmoud El-Morshedy, 2022. "On Predictive Modeling Using a New Flexible Weibull Distribution and Machine Learning Approach: Analyzing the COVID-19 Data," Mathematics, MDPI, vol. 10(11), pages 1-26, May.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:11:p:1792-:d:822693
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    References listed on IDEAS

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    Cited by:

    1. 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.

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