Evidence map›Paper›PMID 37287570›Full record

ArticleSoft computing2023

An optimization method for studying fractional-order tuberculosis disease model via generalized Laguerre polynomials.

Z Avazzadeh, H Hassani, P Agarwal, S Mehrabi, M J Ebadi, M Sh Dahaghin

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Soft computing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
3.5field-weighted citation impact, top 7% of its field
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

1 citing paper in PubMed, 16 citations in OpenAlex.

  1. 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

6 authors at 5 institutions in 3 countries.

Z AvazzadehDepartment of Mathematical Sciences, University of South Africa, Pretoria, Florida South Africa.
H HassaniDepartment of Mathematics, Anand International College of Engineering, Jaipur, 303012 India.
P AgarwalDepartment of Mathematics, Anand International College of Engineering, Jaipur, 303012 India.
S MehrabiDepartment of Internal Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.
M J EbadiDepartment of Mathematics, Chabahar Maritime University, Chabahar, Iran.
M Sh DahaghinFaculty of Mathematical Sciences, Department of Applied Mathematics, Shahrekord University, Shahrekord, Iran.
Inspiration Innovation Synergy University · INChabahar Maritime University · IRShahrekord University · IRShiraz University of Medical Sciences · IRUniversity of South Africa · ZA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tuberculosis (TB) is a deadly contagious disease that affects vital organs of the body, especially the lungs. Although the disease is preventable, there are still concerns about its continued spread. Without effective prevention or appropriate treatment, TB infection can be fatal to humans. This paper presents a fractional-order TB disease (FTBD) model to analyze TB dynamics and a new optimization method to solve it. The method is based on the basis functions of generalized Laguerre polynomials (GLPs) and some new operational matrices of derivatives in the Caputo sense. Finding the optimal solution to the FTBD model is reduced to solving a system of nonlinear algebraic equations with the aid of GLPs using the Lagrange multipliers method. A numerical simulation is also carried out to determine the impact of the presented method on the susceptible, exposed, infected without treatment, infected with treatment, and recovered cases in the population.

Indexed as

Fractional-order tuberculosis modelGeneralized Laguerre polynomialsInfected populationRecovered population

Identifiers

PMID37287570
PMCPMC10131579
OpenAlexW4367050024

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.