Evidence map›Paper›PMID 42752456›Full record

ArticlePloS one2026

Stochastic delay derivatives of Newcastle disease application in epidemic model: Stability analysis and approximation.

Naveed Shahid, Ali Raza, Marek Lampart, Sana Iqbal, Nauman Ahmed, Eman Ghareeb Rezk

Abstract read
In one paragraph

Article in PloS one, 2026. 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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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

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

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.

Naveed ShahidDepartment of Mathematics and Statistics, The University of Lahore, Lahore, Pakistan.
Ali RazaDepartment of Mathematics, Firat University, Elazığ, Türkiye.ORCID https://orcid.org/0000-0002-6443-9966
Marek LampartIT4Innovations, VSB-Technical University of Ostrava, Ostrava, Czech Republic.
Sana IqbalDepartment of Mathematics and Statistics, The University of Lahore, Lahore, Pakistan.
Nauman AhmedDepartment of Mathematics and Statistics, The University of Lahore, Lahore, Pakistan.
Eman Ghareeb RezkMathematical Science Department, College of Science, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The illicit trade in wildlife for pet purposes poses a direct risk to animal populations through overharvesting, but it also serves as an indirect conduit for the spread of contagious diseases. The present study assessed the effects of the hypothetical release of captured, infected individuals of Newcastle disease into the natural population of the white-winged parakeet, the most trafficked psittacine species in Peru. This study analysed the computational dynamical analysis of the stochastic susceptible-exposed-infected-recovered model of Newcastle disease. We take two approaches to stochastic modelling: transition probabilities and the perturbation method. To investigate dynamical features such as positivity, boundedness, consistency, and stability, we introduced a stochastic non-standard finite-difference (SNSFD) scheme. Conventional numerical approaches such as Euler-Maruyama, stochastic Euler, and stochastic Runge-Kutta of order four were applied, but they failed to preserve the system's essential dynamical properties. Consequently, we formulated the SNSFD method to address this limitation. To support the proposed method, we presented several theorems that demonstrate it satisfies all dynamical properties of the model. In the end, we presented several simulations to compare the proposed method's efficiency to that of an existing method.

Indexed as

EpidemicsModels, BiologicalNewcastle DiseaseAnimalsComputer SimulationPeruStochastic ProcessesWildlife Trade

Identifiers

PMID42752456
PMCPMC13585341

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.