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Data analysis for COVID-19 deaths using a novel statistical model: Simulation and fuzzy application

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  • El-Sayed A El-Sherpieny
  • Ehab M Almetwally
  • Abdisalam Hassan Muse
  • Eslam Hussam

Abstract

This paper provides a novel model that is more relevant than the well-known conventional distributions, which stand for the two-parameter distribution of the lifetime modified Kies Topp–Leone (MKTL) model. Compared to the current distributions, the most recent one gives an unusually varied collection of probability functions. The density and hazard rate functions exhibit features, demonstrating that the model is flexible to several kinds of data. Multiple statistical characteristics have been obtained. To estimate the parameters of the MKTL model, we employed various estimation techniques, including maximum likelihood estimators (MLEs) and the Bayesian estimation approach. We compared the traditional reliability function model to the fuzzy reliability function model within the reliability analysis framework. A complete Monte Carlo simulation analysis is conducted to determine the precision of these estimators. The suggested model outperforms competing models in real-world applications and may be chosen as an enhanced model for building a statistical model for the COVID-19 data and other data sets with similar features.

Suggested Citation

  • El-Sayed A El-Sherpieny & Ehab M Almetwally & Abdisalam Hassan Muse & Eslam Hussam, 2023. "Data analysis for COVID-19 deaths using a novel statistical model: Simulation and fuzzy application," PLOS ONE, Public Library of Science, vol. 18(4), pages 1-17, April.
    • Handle: RePEc:plo:pone00:0283618
    DOI: 10.1371/journal.pone.0283618
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    References listed on IDEAS

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    1. Lalmuanawma, Samuel & Hussain, Jamal & Chhakchhuak, Lalrinfela, 2020. "Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
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