Modeling and analysis of recovery time for the COVID-19 patients: a Bayesian approach. Issue 1 (31st December 2023)

• Record Type: Journal Article
• Authors: Ateeq, Kahkashan; Altaf, Saima; Aslam, Muhammad
• Is Part Of: Arab journal of basic and applied sciences. Volume 30:Issue 1(2023)
• Page Start: 1
• Page End: 12
• Publication Date: 2023-12-31
• Subjects: Bayesian estimation -- COVID-19 patients -- Fréchet distribution -- Lindley method -- recovery time -- survival time -- Tierney–Kadane approximation method; 60E05 -- 62F15 -- 62N05; Science -- Arab countries -- Periodicals; Science -- Periodicals; Science; Arab countries; Periodicals; Electronic journals; 500
• DOI: 10.1080/25765299.2022.2148439
• Languages: English
• ISSNs: 2576-5299

Abstract

Abstract: The ongoing pandemic of COVID-19 has changed every aspect of life. Most of the people who become a victim of COVID-19 experience mild to moderate symptoms, but some people may become seriously ill. This illness, sometimes, may lead to a very painful death. The Fréchet distribution is one of the flexible distribution for survival time. Hence, in this article, the recovery time of COVID-19 patients is modeled by a new Fréchet-exponential (FE) distribution, and the parameters of the distribution are estimated in the classical and Bayesian paradigms. Since the Bayes estimators using informative priors are not in the closed form, the Lindley and Tierney–Kadane approximation methods are used for their evaluation. The results obtained through simulation studies and the COVID-19 data set assess the superiority of the Bayes estimators over the classical estimators in terms of minimum risks. Mathematically and graphically, it is shown that our proposed model appropriately fits the data set. The minimum values of Akaike information criterion, Bayesian information criterion, corrected Akaike information criterion, and Hannan-Quinn information criterion proves that the FE distribution better fit than the competitors' distribution for the data set about the recovery time of COVID-19 patients.