Predictive modeling of the COVID-19 data using a new version of the flexible Weibull model and machine learning techniques

Rashad A. R. Bantan; Zubair Ahmad; Faridoon Khan; Mohammed Elgarhy; Zahra Almaspoor; G. G. Hamedani; Mahmoud El-Morshedy; Ahmed M. Gemeay; Rashad A. R. Bantan; Zubair Ahmad; Faridoon Khan; Mohammed Elgarhy; Zahra Almaspoor; G. G. Hamedani; Mahmoud El-Morshedy; Ahmed M. Gemeay

doi:10.3934/mbe.2023134

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 2: 2847-2873. doi: 10.3934/mbe.2023134

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Predictive modeling of the COVID-19 data using a new version of the flexible Weibull model and machine learning techniques

1.
Department of Marine Geology, Faculty of Marine Science, King Abdulaziz University, Jeddah 21551, Saudi Arabia
2.
Department of Statistics, Yazd University, P.O. Box 89175-741, Yazd, Iran
3.
PIDE School of Economics, Islamabad 44000, Pakistan
4.
The Higher Institute of Commercial Sciences, Al mahalla Al kubra, Algarbia 31951, Egypt
5.
Department of Mathematical and Statistical Sciences, Marquette University, Milwaukee, WI, USA
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
8.
Department of Mathematics, Faculty of Science, Tanta University, Tanta 31527, Egypt

Academic Editor: Víctor Leiva

Received: 26 July 2022 Revised: 10 November 2022 Accepted: 20 November 2022 Published: 01 December 2022

Statistical modeling and forecasting of time-to-events data are crucial in every applied sector. For the modeling and forecasting of such data sets, several statistical methods have been introduced and implemented. This paper has two aims, i.e., (i) statistical modeling and (ii) forecasting. For modeling time-to-events data, we introduce a new statistical model by combining the flexible Weibull model with the Z-family approach. The new model is called the Z flexible Weibull extension (Z-FWE) model, where the characterizations of the Z-FWE model are obtained. The maximum likelihood estimators of the Z-FWE distribution are obtained. The evaluation of the estimators of the Z-FWE model is assessed in a simulation study. The Z-FWE distribution is applied to analyze the mortality rate of COVID-19 patients. Finally, for forecasting the COVID-19 data set, we use machine learning (ML) techniques i.e., artificial neural network (ANN) and group method of data handling (GMDH) with the autoregressive integrated moving average model (ARIMA). Based on our findings, it is observed that ML techniques are more robust in terms of forecasting than the ARIMA model.
- flexible Weibull extension model,
- COVID-19 data,
- statistical modeling,
- machine learning techniques,
- ARIMA,
- forecasting
Citation: Rashad A. R. Bantan, Zubair Ahmad, Faridoon Khan, Mohammed Elgarhy, Zahra Almaspoor, G. G. Hamedani, Mahmoud El-Morshedy, Ahmed M. Gemeay. Predictive modeling of the COVID-19 data using a new version of the flexible Weibull model and machine learning techniques[J]. Mathematical Biosciences and Engineering, 2023, 20(2): 2847-2873. doi: 10.3934/mbe.2023134

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Abstract

Statistical modeling and forecasting of time-to-events data are crucial in every applied sector. For the modeling and forecasting of such data sets, several statistical methods have been introduced and implemented. This paper has two aims, i.e., (i) statistical modeling and (ii) forecasting. For modeling time-to-events data, we introduce a new statistical model by combining the flexible Weibull model with the Z-family approach. The new model is called the Z flexible Weibull extension (Z-FWE) model, where the characterizations of the Z-FWE model are obtained. The maximum likelihood estimators of the Z-FWE distribution are obtained. The evaluation of the estimators of the Z-FWE model is assessed in a simulation study. The Z-FWE distribution is applied to analyze the mortality rate of COVID-19 patients. Finally, for forecasting the COVID-19 data set, we use machine learning (ML) techniques i.e., artificial neural network (ANN) and group method of data handling (GMDH) with the autoregressive integrated moving average model (ARIMA). Based on our findings, it is observed that ML techniques are more robust in terms of forecasting than the ARIMA model.

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