Evidence map›Paper›PMID 34900522›Full record

ArticleResults in physics2022

The new discrete distribution with application to COVID-19 Data.

Ehab M Almetwally, Doaa A Abdo, E H Hafez, Taghreed M Jawa, Neveen Sayed-Ahmed, Hisham M Almongy

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PubMed Full text at PMC Publisher
In one paragraph

Article in Results in physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Ehab M AlmetwallyFaculty of Business Administration, Delta University of Science and Technology, Gamasa, 11152, Egypt.
Doaa A AbdoFaculty of Commerce, Mansoura University, Mansoura, 35516, Egypt.
E H HafezDepartment of Mathematics, Faculty of Science, Helwan university, Cairo, Egypt.
Taghreed M JawaDepartment of Mathematics, College of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.
Neveen Sayed-AhmedDepartment of Mathematics, College of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.
Hisham M AlmongyFaculty of Commerce, Mansoura University, Mansoura, 35516, Egypt.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This research aims to model the COVID-19 in different countries, including Italy, Puerto Rico, and Singapore. Due to the great applicability of the discrete distributions in analyzing count data, we model a new novel discrete distribution by using the survival discretization method. Because of importance Marshall-Olkin family and the inverse Toppe-Leone distribution, both of them were used to introduce a new discrete distribution called Marshall-Olkin inverse Toppe-Leone distribution, this new distribution namely the new discrete distribution called discrete Marshall-Olkin Inverse Toppe-Leone (DMOITL). This new model possesses only two parameters, also many properties have been obtained such as reliability measures and moment functions. The classical method as likelihood method and Bayesian estimation methods are applied to estimate the unknown parameters of DMOITL distributions. The Monte-Carlo simulation procedure is carried out to compare the maximum likelihood and Bayesian estimation methods. The highest posterior density (HPD) confidence intervals are used to discuss credible confidence intervals of parameters of new discrete distribution for the results of the Markov Chain Monte Carlo technique (MCMC).

Indexed as

60E0562F10Bayesian estimationInverse Toppe–LeoneMarshall–Olkin familyMaximum likelihood estimationSurvival discretization

Identifiers

PMID34900522
PMCPMC8645255

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.