ArticleComputational intelligence and neuroscience2021
Modelling the COVID-19 Mortality Rate with a New Versatile Modification of the Log-Logistic Distribution.
Article in Computational intelligence and neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Sinusoidal exponential distribution for modeling elevated creatinine kinase level as the initial presentation of Covid-19.Scientific reports · 2025Article
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- Article
- The balanced discrete Burr-Hatke model and mixing INAR(1) process: properties, estimation, forecasting and COVID-19 applications.Journal of applied statistics · 2024Article
- Statistical inference with joint progressive censoring for two populations using power Rayleigh lifetime distribution.Scientific reports · 2023Article
- The Gull Alpha Power Lomax distributions: Properties, simulation, and applications to modeling COVID-19 mortality rates.PloS one · 2023Article
- The distribution of COVID-19 mortality.Infectious Disease Modelling · 2022Article
- Mathematical modeling of the impact of Omicron variant on the COVID-19 situation in South Korea.Genomics & informatics · 2022Article
- A new versatile modification of the Rayleigh distribution for modeling COVID-19 mortality rates.Results in physics · 2022Article
- A Flexible Bayesian Parametric Proportional Hazard Model: Simulation and Applications to Right-Censored Healthcare Data.Journal of healthcare engineering · 2022Article
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The goal of this paper is to develop an optimal statistical model to analyze COVID-19 data in order to model and analyze the COVID-19 mortality rates in Somalia. Combining the log-logistic distribution and the tangent function yields the flexible extension log-logistic tangent (LLT) distribution, a new two-parameter distribution. This new distribution has a number of excellent statistical and mathematical properties, including a simple failure rate function, reliability function, and cumulative distribution function. Maximum likelihood estimation (MLE) is used to estimate the unknown parameters of the proposed distribution. A numerical and visual result of the Monte Carlo simulation is obtained to evaluate the use of the MLE method. In addition, the LLT model is compared to the well-known two-parameter, three-parameter, and four-parameter competitors. Gompertz, log-logistic, kappa, exponentiated log-logistic, Marshall-Olkin log-logistic, Kumaraswamy log-logistic, and beta log-logistic are among the competing models. Different goodness-of-fit measures are used to determine whether the LLT distribution is more useful than the competing models in COVID-19 data of mortality rate analysis.
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