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
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
8640794Informations de copyright
Copyright © 2021 Abdisalam Hassan Muse et al.
Déclaration de conflit d'intérêts
The authors declare no conflicts of interest.
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