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The Odd Weibull Inverse Topp-Leone Distribution with Applications to COVID-19 Data.

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Summary
This summary is machine-generated.

Researchers developed a new statistical model, the odd Weibull inverted Topp-Leone (OWITL) distribution, for COVID-19 data analysis in the UK and Canada. This model offers a flexible approach to understanding disease spread.

Keywords:
COVID-19Inverted Topp–Leone distributionMaximum likelihood estimationMaximum product spacingOdd Weibull family

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Area of Science:

  • Statistics
  • Epidemiology
  • Probability Theory

Background:

  • Accurate statistical modeling is crucial for understanding and managing infectious disease outbreaks like COVID-19.
  • Existing distributions may not fully capture the complex patterns of disease spread.

Purpose of the Study:

  • To introduce and define a novel statistical distribution, the odd Weibull inverted Topp-Leone (OWITL) distribution.
  • To apply this new distribution for modeling COVID-19 data in the United Kingdom and Canada.

Main Methods:

  • Formulation of the three-parameter OWITL distribution by combining the inverted Topp-Leone and odd Weibull families.
  • Application of various parameter estimation techniques: maximum likelihood, least-square, weighted least-squares, maximum product spacing, Cramér-von Mises, and Anderson-Darling.
  • Utilizing Monte Carlo simulations to evaluate the performance of the estimation methods.

Main Results:

  • The OWITL distribution exhibits desirable properties, including a simple linear hazard rate function and moment function.
  • The study provides a framework for estimating parameters of this new distribution using established statistical methods.
  • Simulation results offer insights into the efficacy of different estimation techniques for the OWITL model.

Conclusions:

  • The proposed OWITL distribution offers a promising new tool for statistical modeling of lifetime data, particularly relevant for epidemiological studies.
  • The comprehensive evaluation of estimation methods provides guidance for practical application in analyzing COVID-19 distribution.
  • This work contributes to the advancement of statistical methodologies for disease modeling.