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Exploring the Dynamics of COVID-19 with a Novel Family of Models

Author

Listed:
  • Abdulaziz S. Alghamdi

    (Department of Mathematics, College of Science & Arts, King Abdulaziz University, P.O. Box 344, Rabigh 21911, Saudi Arabia)

  • M. M. Abd El-Raouf

    (Basic and Applied Science Institute, Arab Academy for Science, Technology and Maritime Transport (AASTMT), Alexandria P.O. Box 1029, Egypt)

Abstract

Much effort has recently been expended in developing efficient models that can depict the true picture for COVID-19 mortality data and help scientists choose the best-fit models. As a result, this research intends to provide a new G family for both theoretical and practical scientists that solves the concerns typically encountered in both normal and non-normal random events. The new-G distribution family is able to generate efficient continuous univariate and skewed models that may outperform the baseline model. The analytic properties of the new-G family and its sub-model are investigated and described, as well as a theoretical framework. The parameters were estimated using a classical approach along with an extensive simulation study to assess the behaviour of the parameters. The efficiency of the new-G family is discussed using one of its sub-models on COVID-19 mortality data sets.

Suggested Citation

  • Abdulaziz S. Alghamdi & M. M. Abd El-Raouf, 2023. "Exploring the Dynamics of COVID-19 with a Novel Family of Models," Mathematics, MDPI, vol. 11(7), pages 1-29, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:7:p:1641-:d:1109870
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    References listed on IDEAS

    as
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    Full references (including those not matched with items on IDEAS)

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