Evidence map›Paper›PMID 38378902›Full record

ArticleScientific reports2024

A novel technique to study the solutions of time fractional nonlinear smoking epidemic model.

K Pavani, K Raghavendar

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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. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

K PavaniDepartment of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, 632014, India.
K RaghavendarDepartment of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, 632014, India. raghavendar248@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The primary goal of the current work is to use a novel technique known as the natural transform decomposition method to approximate an analytical solution for the fractional smoking epidemic model. In the proposed method, fractional derivatives are considered in the Caputo, Caputo-Fabrizio, and Atangana-Baleanu-Caputo senses. An epidemic model is proposed to explain the dynamics of drug use among adults. Smoking is a serious issue everywhere in the world. Notwithstanding the overwhelming evidence against smoking, it is nonetheless a harmful habit that is widespread and accepted in society. The considered nonlinear mathematical model has been successfully used to explain how smoking has changed among people and its effects on public health in a community. The two states of being endemic and disease-free, which are when the disease dies out or persists in a population, have been compared using sensitivity analysis. The proposed technique has been used to solve the model, which consists of five compartmental agents representing various smokers identified, such as potential smokers V, occasional smokers G, smokers T, temporarily quitters O, and permanently quitters W. The results of the suggested method are contrasted with those of existing numerical methods. Finally, some numerical findings that illustrate the tables and figures are shown. The outcomes show that the proposed method is efficient and effective.

Indexed as

EpidemicsTobacco SmokingAdultHabitsHumansSmokersSmokingAtangana–Baleanu–Caputo derivativeCaputo derivativeCaputo–Fabrizio derivativeNatural transform decomposition methodSmoking model

Identifiers

PMID38378902
PMCPMC11306355

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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.