In this article, we develop a generator to suggest a generalization of the Gumbel type-II model known as generalized log-exponential transformation of Gumbel Type-II (GLET-GTII), which extends a more flexible model for modeling life data. Owing to basic transformation containing an extra parameter, every existing lifetime model can be made more flexible with suggested development. Some specific statistical attributes of the GLET-GTII are investigated, such as quantiles, uncertainty measures, survival function, moments, reliability, and hazard function etc. We describe two methods of parametric estimations of GLET-GTII discussed by using maximum likelihood estimators and Bayesian paradigm. The Monte Carlo simulation analysis shows that estimators are consistent. Two real life implementations are performed to scrutinize the suitability of our current strategy. These real life data is related to Infectious diseases (COVID-19). These applications identify that by using the current approach, our proposed model outperforms than other well known existing models available in the literature.

在本文中，我们开发了一个生成器，以建议对称为Gumbel II型广义对数指数变换（GLET-GTII）的Gumbel II型II模型进行推广，该模型扩展了用于建模寿命数据的更灵活的模型。由于包含一个额外参数的基本转换，可以通过建议的开发使每个现有的寿命模型更加灵活。研究了GLET-GTII的一些特定统计属性，例如分位数，不确定性度量，生存函数，矩，可靠性和危险函数等。我们描述了使用最大似然估计器和贝叶斯方法讨论的GLET-GTII参数估计的两种方法。范例。蒙特卡洛模拟分析表明估计量是一致的。执行了两个现实生活中的实施，以检查我们当前策略的适用性。这些现实生活数据与传染病（COVID-19）有关。这些应用程序表明，通过使用当前方法，我们提出的模型优于文献中提供的其他众所周知的现有模型。