On the implementation of a new version of the Weibull distribution and machine learning approach to model the COVID-19 data

Yinghui Zhou; Zubair Ahmad; Zahra Almaspoor; Faridoon Khan; Elsayed tag-Eldin; Zahoor Iqbal; Mahmoud El-Morshedy; Yinghui Zhou; Zubair Ahmad; Zahra Almaspoor; Faridoon Khan; Elsayed tag-Eldin; Zahoor Iqbal; Mahmoud El-Morshedy

doi:10.3934/mbe.2023016

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 1: 337-364. doi: 10.3934/mbe.2023016

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On the implementation of a new version of the Weibull distribution and machine learning approach to model the COVID-19 data

1.
School of Information and Communication Engineering, Communication University of China, Beijing, China
2.
Department of Statistics, Yazd University, P. O. Box 89175-741, Yazd, Iran
3.
PIDE School of Economics, PIDE Islamabad 44000, Pakistan
4.
Faculty of Engineering and Technology, Future University in Egypt New Cairo 11835, Egypt
5.
Department of Mathematics, Quaid-i-Azam University, Islamabad 44000, Pakistan
6.
Department of Mathematics, College of Science and Humanities in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
7.
Department of Mathematics, Faculty of Science, Mansoura University, Mansoura 35516, Egypt

Academic Editor: Victor Leiva

Received: 01 July 2022 Revised: 20 September 2022 Accepted: 21 September 2022 Published: 08 October 2022

Statistical methodologies have broader applications in almost every sector of life including education, hydrology, reliability, management, and healthcare sciences. Among these sectors, statistical modeling and predicting data in the healthcare sector is very crucial. In this paper, we introduce a new method, namely, a new extended exponential family to update the distributional flexibility of the existing models. Based on this approach, a new version of the Weibull model, namely, a new extended exponential Weibull model is introduced. The applicability of the new extended exponential Weibull model is shown by considering two data sets taken from the health sciences. The first data set represents the mortality rate of the patients infected by the coronavirus disease 2019 (COVID-19) in Mexico. Whereas, the second set represents the mortality rate of COVID-19 patients in Holland. Utilizing the same data sets, we carry out forecasting using three machine learning (ML) methods including support vector regression (SVR), random forest (RF), and neural network autoregression (NNAR). To assess their forecasting performances, two statistical accuracy measures, namely, root mean square error (RMSE) and mean absolute error (MAE) are considered. Based on our findings, it is observed that the RF algorithm is very effective in predicting the death rate of the COVID-19 data in Mexico. Whereas, for the second data, the SVR performs better as compared to the other methods.
- family of distributions,
- healthcare sector,
- machine learning algorithms,
- mathematical properties,
- simulation,
- statistical modeling
Citation: Yinghui Zhou, Zubair Ahmad, Zahra Almaspoor, Faridoon Khan, Elsayed tag-Eldin, Zahoor Iqbal, Mahmoud El-Morshedy. On the implementation of a new version of the Weibull distribution and machine learning approach to model the COVID-19 data[J]. Mathematical Biosciences and Engineering, 2023, 20(1): 337-364. doi: 10.3934/mbe.2023016

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Abstract

Statistical methodologies have broader applications in almost every sector of life including education, hydrology, reliability, management, and healthcare sciences. Among these sectors, statistical modeling and predicting data in the healthcare sector is very crucial. In this paper, we introduce a new method, namely, a new extended exponential family to update the distributional flexibility of the existing models. Based on this approach, a new version of the Weibull model, namely, a new extended exponential Weibull model is introduced. The applicability of the new extended exponential Weibull model is shown by considering two data sets taken from the health sciences. The first data set represents the mortality rate of the patients infected by the coronavirus disease 2019 (COVID-19) in Mexico. Whereas, the second set represents the mortality rate of COVID-19 patients in Holland. Utilizing the same data sets, we carry out forecasting using three machine learning (ML) methods including support vector regression (SVR), random forest (RF), and neural network autoregression (NNAR). To assess their forecasting performances, two statistical accuracy measures, namely, root mean square error (RMSE) and mean absolute error (MAE) are considered. Based on our findings, it is observed that the RF algorithm is very effective in predicting the death rate of the COVID-19 data in Mexico. Whereas, for the second data, the SVR performs better as compared to the other methods.

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