Evidence map›Paper›PMID 38734768›Full record

ArticleScientific reports2024

Modeling COVID-19 data with a novel neutrosophic Burr-III distribution.

Farrukh Jamal, Shakaiba Shafiq, Muhammad Aslam, Sadaf Khan, Zawar Hussain, Qamer Abbas

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Farrukh JamalDepartment of Statistics, The Islamia University of Bahawalpur, Bahawalpur, Pakistan.
Shakaiba ShafiqDepartment of Statistics, The Islamia University of Bahawalpur, Bahawalpur, Pakistan.
Muhammad AslamDepartment of Statistics, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia. aslam_ravian@hotmail.com.
Sadaf KhanDepartment of Statistics, The Islamia University of Bahawalpur, Bahawalpur, Pakistan.
Zawar HussainDepartment of Statistics, The Islamia University of Bahawalpur, Bahawalpur, Pakistan.
Qamer AbbasDepartment of Statistics, The Islamia University of Bahawalpur, Bahawalpur, Pakistan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this study, we have presented a novel probabilistic model called the neutrosophic Burr-III distribution, designed for applications in neutrosophic surface analysis. Neutrosophic analysis allows for the incorporation of vague and imprecise information, reflecting the reality that many real-world problems involve ambiguous data. This ability to handle vagueness can lead to more robust and realistic models especially in situation where classical models fall short. We have also explored the neutrosophic Burr-III distribution in order to deal with the ambiguity and vagueness in the data where the classical Burr-III distribution falls short. This distribution offers valuable insights into various reliability properties, moment expressions, order statistics, and entropy measures, making it a versatile tool for analyzing complex data. To assess the practical relevance of our proposed distribution, we applied it to real-world data sets and compared its performance against the classical Burr-III distribution. The findings revealed that the neutrosophic Burr-III distribution outperformed than the classical Burr-III distribution in capturing the underlying data characteristics, highlighting its potential as a superior modeling toolin various fields.

Indexed as

COVID-19Models, StatisticalHumansSARS-CoV-2Burr-III distributionIndetermincyNes-BrIII distributionNeutrosophic analysis

Identifiers

PMID38734768
PMCPMC11631972

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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.