Bayesian probability

Gumbel distribution

Pathology

Organic chemistry

Disease

Coronavirus disease 2019 (COVID-19)

Mathematics

Hazard

Infectious disease (medical specialty)

Evolutionary biology

Biology

Gene

Parametric statistics

Statistics

Quantile

Extreme value theory

Function (biology)

Econometrics

Parametric model

Chemistry

Transformation (genetics)

Applied mathematics

Biochemistry

Medicine

Alexandria Engineering Journal

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 Exponentiated transformation 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. Additionally, 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.

Exponentiated transformation of Gumbel Type-II distribution for modeling COVID-19 data