Abstract
In this research, we discuss a new lifespan model that extends the Frechet (F) distribution by utilizing the sine-generated family of distributions, known as the sine Frechet (SF) model. The sine Frechet distribution, which contains two parameters, scale and shape, aims to give an SF model for data fitting. The sine Frechet model is more adaptable than well-known models such as the Frechet and inverse exponential models. The sine Frechet distribution is used extensively in medicine, physics, and nanophysics. The SF model's statistical properties were computed, including the quantile function, moments, moment generating function (MGF), and order statistics. To estimate the model parameters for the SF distribution, the maximum likelihood (ML) estimation technique is applied. As a result of the simulation, the performance of the estimations may be compared. We use it to examine a current dataset of interest: COVID-19 death cases observed in the Kingdom of Saudi Arabia (KSA) from 14 April to 22 June 2020. In the future, the SF model could be useful for analyzing data on COVID-19 cases in a variety of nations for possible comparison studies. Finally, the numerical results are examined in order to assess the flexibility of the new model.