Evidence map›Paper›PMID 40425741›Full record

ArticleScientific reports2025

Development of a novel extension of Rayleigh distribution with application to COVID-19 data.

Danish Qayoom, Aafaq A Rather, Ohud A Alqasem, Zahoor Ahmad, M Nagy, Abdirashid M Yousuf, A H Mansi, Eslam Hussam, Ahmed M Gemeay

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

9 authors.

Danish QayoomSymbiosis Statistical Institute, Symbiosis International (Deemed University), Pune-411004, India.
Aafaq A RatherSymbiosis Statistical Institute, Symbiosis International (Deemed University), Pune-411004, India.
Ohud A AlqasemDepartment of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
Zahoor AhmadDepartment of Statistics, University of Kashmir, Srinagar, India.
M NagyDepartment of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, 11451, Riyadh, Saudi Arabia.
Abdirashid M YousufResearch and Innovation Center, Amoud University, Borama, 25263, Somaliland. gees@amoud.edu.so.
A H MansiDICA, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20131, Milan (MI), Italy.
Eslam HussamDepartment of Accounting, College of Business Administration in Hawtat Bani Tamim, Prince Sattam Bin Abdulaziz University, Hawtat Bani Tamim, Saudi Arabia.
Ahmed M GemeayDepartment of Mathematics, Faculty of Science, Tanta University, Tanta 31527, Egypt.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Effective analysis of medical data is essential for understanding complex healthcare phenomena. Probability distribution models offer a structured approach to uncover patterns in such data, particularly for studying disease progression, survival analysis and many more. In this study, we explore a novel probability distribution model, derived by applying the DUS transformation to the standard Rayleigh distribution. We thoroughly investigate the statistical properties of the proposed model and derive key reliability measures to demonstrate its applicability in reliability analysis. To ensure precise parameter estimation, various estimation methods are evaluated, and their effectiveness is assessed through a detailed simulation study using bias, mean squared error, and mean relative error as performance criteria. The developed model's practical applicability is demonstrated with an analysis of COVID-19 data, comparing its performance with several well-known distributions. The results highlight the flexibility and accuracy of the model, establishing it as a powerful and reliable tool for advanced statistical modelling in healthcare research.

Indexed as

COVID-19Models, StatisticalComputer SimulationHumansReproducibility of ResultsSARS-CoV-2

Identifiers

PMID40425741
PMCPMC12117054

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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.