Exponentiated Transformation of Gumbel Type-II Distribution for Modeling COVID-19 Data

Sindhu, Tabassum Naz; Shafiq, Anum; Al-Mdallal, Qasem M.

The aim of this study is to analyze the number of deaths due to COVID-19 for Europe and China. For this purpose, we proposed a novel three parametric model named as Exponentiatedtransformation of Gumbel Type-II (ETGT-II) for modeling the two data sets of death cases due to COVID-19. Specific statistical attributes are derived and analyzed along with moments and associated measures, moments generating functions, uncertainty measures, complete/incomplete moments, survival function, quantile function and hazard function etc. Additionaly, model parameters are estimated by utilizing maximum likelihood method and Bayesian paradigm. To examine efficiency of the ETGT-II model a simulation analysis is performed. Finally, using the data sets of death cases of COVID-19 of Europe and China to show adaptability of suggested model. The results reveal that it may fit better than other well-known models.