Evidence map›Paper›PMID 38605097›Full record

ArticleScientific reports2024

Analyzing the dynamic patterns of COVID-19 through nonstandard finite difference scheme.

Abeer Aljohani, Ali Shokri, Herbert Mukalazi

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.

0numbers the graph read from it
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

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

3 authors.

Abeer AljohaniDepartment of Computer Science, Applied College, Taibah University, Medina, 42353, Kingdom of Saudi Arabia.
Ali ShokriDepartment of Mathematics, Faculty of Science, University of Maragheh, Maragheh, 83111-55181, Iran.
Herbert MukalaziDepartment of Mathematics and Statistics, Kyambogo University, Kampala, Uganda. hmukalazi@kyu.ac.ug.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper presents a novel approach to analyzing the dynamics of COVID-19 using nonstandard finite difference (NSFD) schemes. Our model incorporates both asymptomatic and symptomatic infected individuals, allowing for a more comprehensive understanding of the epidemic's spread. We introduce an unconditionally stable NSFD system that eliminates the need for traditional Runge-Kutta methods, ensuring dynamical consistency and numerical accuracy. Through rigorous numerical analysis, we evaluate the performance of different NSFD strategies and validate our analytical findings. Our work demonstrates the benefits of using NSFD schemes for modeling infectious diseases, offering advantages in terms of stability and efficiency. We further illustrate the dynamic behavior of COVID-19 under various conditions using numerical simulations. The results from these simulations demonstrate the effectiveness of the proposed approach in capturing the epidemic's complex dynamics.

Indexed as

COVID-19HumansAsymptomaticCOVID-19Differential equationsDiscretizedEpidemicEstablishNonstandard finite difference schemeOverall behaviorSymptomaticUnconditionally stable

Identifiers

PMID38605097
PMCPMC11009417

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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.