Modelling the COVID-19 Mortality Rate with a New Versatile Modification of the Log-Logistic Distribution

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...

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Autores principales:	Abdisalam Hassan Muse, Ahlam H. Tolba, Eman Fayad, Ola A. Abu Ali, M. Nagy, M. Yusuf
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/e5b72db8232f40abbd839731fb007c4b
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spelling	oai:doaj.org-article:e5b72db8232f40abbd839731fb007c4b2021-11-22T01:11:15ZModelling the COVID-19 Mortality Rate with a New Versatile Modification of the Log-Logistic Distribution1687-527310.1155/2021/8640794https://doaj.org/article/e5b72db8232f40abbd839731fb007c4b2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/8640794https://doaj.org/toc/1687-5273The 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.Abdisalam Hassan MuseAhlam H. TolbaEman FayadOla A. Abu AliM. NagyM. YusufHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7Neurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENComputational Intelligence and Neuroscience, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Abdisalam Hassan Muse Ahlam H. Tolba Eman Fayad Ola A. Abu Ali M. Nagy M. Yusuf Modelling the COVID-19 Mortality Rate with a New Versatile Modification of the Log-Logistic Distribution
description	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.
format	article
author	Abdisalam Hassan Muse Ahlam H. Tolba Eman Fayad Ola A. Abu Ali M. Nagy M. Yusuf
author_facet	Abdisalam Hassan Muse Ahlam H. Tolba Eman Fayad Ola A. Abu Ali M. Nagy M. Yusuf
author_sort	Abdisalam Hassan Muse
title	Modelling the COVID-19 Mortality Rate with a New Versatile Modification of the Log-Logistic Distribution
title_short	Modelling the COVID-19 Mortality Rate with a New Versatile Modification of the Log-Logistic Distribution
title_full	Modelling the COVID-19 Mortality Rate with a New Versatile Modification of the Log-Logistic Distribution
title_fullStr	Modelling the COVID-19 Mortality Rate with a New Versatile Modification of the Log-Logistic Distribution
title_full_unstemmed	Modelling the COVID-19 Mortality Rate with a New Versatile Modification of the Log-Logistic Distribution
title_sort	modelling the covid-19 mortality rate with a new versatile modification of the log-logistic distribution
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/e5b72db8232f40abbd839731fb007c4b
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Modelling the COVID-19 Mortality Rate with a New Versatile Modification of the Log-Logistic Distribution

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