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


Journal

Computational intelligence and neuroscience
ISSN: 1687-5273
Titre abrégé: Comput Intell Neurosci
Pays: United States
ID NLM: 101279357

Informations de publication

Date de publication:
2021
Historique:
received: 02 09 2021
accepted: 05 10 2021
entrez: 16 11 2021
pubmed: 17 11 2021
medline: 18 11 2021
Statut: epublish

Résumé

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.

Identifiants

pubmed: 34782836
doi: 10.1155/2021/8640794
pmc: PMC8590594
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

8640794

Informations de copyright

Copyright © 2021 Abdisalam Hassan Muse et al.

Déclaration de conflit d'intérêts

The authors declare no conflicts of interest.

Références

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pubmed: 33728260
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pubmed: 32670391
Comput Intell Neurosci. 2021 Oct 11;2021:5820435
pubmed: 34671390
PLoS One. 2021 Mar 25;16(3):e0249037
pubmed: 33765088
An Acad Bras Cienc. 2015 Apr-Jun;87(2):539-68
pubmed: 26131628
Lifetime Data Anal. 2018 Apr;24(2):328-354
pubmed: 28349290

Auteurs

Abdisalam Hassan Muse (AH)

Department of Mathematics (Statistics Option) Programme, Pan African University, Institute of Basic Science, Technology and Innovation (PAUSTI), Nairobi 6200-00200, Kenya.

Ahlam H Tolba (AH)

Department of Mathematics, Faculty of Science, Mansoura University, Mansoura 35516, Egypt.

Eman Fayad (E)

Department of Biotechnology, College of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.

Ola A Abu Ali (OA)

Department of Chemistry, College of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.

M Nagy (M)

Department of Statistics and Operation Research, Faculty of Science, King Saud University, Riyadh, Saudi Arabia.
Department of Mathematics, Faculty of Science, Fayoum University, Fayoum, Egypt.

M Yusuf (M)

Department of Mathematics, Faculty of Science, Helwan University, Cairo, Egypt.

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