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Among the available classical distributions, the inverse Weibull distribution has received considerable attention. In the practice of distribution theory, numerous methods have been studied and suggested\/introduced to increase the flexibility level of the traditional probability distributions. In this paper, we implement different distribution methods to obtain five new different versions of the inverse Weibull model. The new modifications of the inverse Weibull model are called the logarithm transformed\u2010inverse Weibull, a flexible reduced logarithmic\u2010inverse Weibull, the weighted TX\u2010inverse Weibull, a new generalized\u2010inverse Weibull, and the alpha power transformed extended\u2010inverse Weibull distributions. To illustrate the flexibility and applicability of the new modifications of the inverse Weibull model, a biomedical data set is analyzed. The data set consists of 108 observations and represents the mortality rate of the COVID\u201019\u2010infected patients. The practical application shows that the new generalized\u2010inverse Weibull is the best modification of the inverse Weibull distribution.<\/jats:p>","DOI":"10.1155\/2022\/1325825","type":"journal-article","created":{"date-parts":[[2022,8,19]],"date-time":"2022-08-19T19:05:07Z","timestamp":1660935907000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["New Statistical Approaches for Modeling the COVID\u201019 Data Set: A Case Study in the Medical Sector"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8427-8760","authenticated-orcid":false,"given":"Mohammed M. 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