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Abstract <p>In this article, we introduce the exponentiated version of the Komal distribution, developed and analyzed in the context of COVID-19 data applications. This extended distribution is proposed to enhance flexibility and applicability in modeling real-world data, particularly in pandemic-related scenarios. We derive various fundamental statistical properties of the exponentiated Komal distribution, including its survival function, hazard rate function, reversed hazard rate function, moments and order statistics. Furthermore, the parameters of the proposed distribution are estimated using the maximum likelihood estimation (MLE) method, ensuring robust statistical inference. To validate the practical utility and efficiency of the model, we apply it to an actual COVID-19 dataset. The results demonstrate that the exponentiated Komal distribution provides an improved fit and deeper insights into the underlying patterns of pandemic-related data. The results highlight its superior ability to model and analyze pandemic-related data, making it a valuable tool for statistical modeling in epidemiological research.</p>

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A Novel Statistical Approach to COVID-19 Data Using the Exponentiated Komal Distribution

  • 期刊论文

  • 发表日期：

    2025年10月1日

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  • Suvarna Ranade,
  • Aafaq A. Rather,
  • Eid Sadun Alotaibi

摘要

Abstract

In this article, we introduce the exponentiated version of the Komal distribution, developed and analyzed in the context of COVID-19 data applications. This extended distribution is proposed to enhance flexibility and applicability in modeling real-world data, particularly in pandemic-related scenarios. We derive various fundamental statistical properties of the exponentiated Komal distribution, including its survival function, hazard rate function, reversed hazard rate function, moments and order statistics. Furthermore, the parameters of the proposed distribution are estimated using the maximum likelihood estimation (MLE) method, ensuring robust statistical inference. To validate the practical utility and efficiency of the model, we apply it to an actual COVID-19 dataset. The results demonstrate that the exponentiated Komal distribution provides an improved fit and deeper insights into the underlying patterns of pandemic-related data. The results highlight its superior ability to model and analyze pandemic-related data, making it a valuable tool for statistical modeling in epidemiological research.