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Explore a new statistical distribution to model COVID-19 mortality more accurately.

April 10, 2026Open Access

Modeling COVID‐19 Outcomes Using a New Extended Inverse Burr Distribution

Key Points

Explore a new statistical distribution to model COVID-19 mortality more accurately.
Defined the extended odd-Weibull inverse Burr distribution (ExOW-IB).
Derived statistical properties such as quantile functions and moment-generating functions.
Discussed maximum likelihood methods for parameter estimation.
Conducted a simulation study to evaluate the estimation performance.
The new distribution demonstrated greater flexibility in modeling COVID-19 mortality data.
Its statistical properties were established, enhancing prediction accuracy over previous models.

Abstract

A new extension of the inverse Burr distribution, namely the extended odd‐Weibull inverse Burr distribution (ExOW‐IB), is proposed and defined. This new distribution is a member of the extended odd Weibull‐G family. Statistical properties, including linear representations, quantile functions, moment‐generating functions, and others, are derived. The method of maximum likelihood is discussed to estimate parameters of the proposed distribution. A simulation study is performed to assess the performance of the estimation method. The proposed distribution offers greater flexibility in modeling COVID‐19 mortality data in comparison to other existing distributions.

Modeling COVID‐19 Outcomes Using a New Extended Inverse Burr Distribution

Key Points

Abstract

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