A Discrete-time Mathematical Model of Smoking Dynamics with Two Sub-populations of Smokers

We analyze smoking dynamics with two sub-populations of smokers using a discrete-time mathematical model with a standard difference scheme. We divide the smokers' populations into beginners and heavy smokers. The boundedness of the solution is obtained. The equilibrium stability is assessed through the Jury stability conditions. We additionally present numerical simulations to validate the analytical results by giving several examples to depict the stability of all equilibriums. The sensitivity analysis of the model's parameters is performed to that can offer recommendations for regulators to reduce the number of smokers.

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Publication Details

Journal
Journal of Mathematical Epidemiology
Published
2025-06-30
DOI
https://doi.org/10.64891/jome.3
Citations
1
Primary Topic
Mathematical Biology Tumor Growth
Type
article
Field-Weighted Citation Impact
1.37
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article

A Discrete-time Mathematical Model of Smoking Dynamics with Two Sub-populations of Smokers

Moch. Fandi Ansori
1 citations
Journal of Mathematical Epidemiology
Mathematical Biology Tumor Growth
1.37
article

A Discrete-time Mathematical Model of Smoking Dynamics with Two Sub-populations of Smokers

Moch. Fandi Ansori
article en
1 citations

Abstract

We analyze smoking dynamics with two sub-populations of smokers using a discrete-time mathematical model with a standard difference scheme. We divide the smokers' populations into beginners and heavy smokers. The boundedness of the solution is obtained. The equilibrium stability is assessed through the Jury stability conditions. We additionally present numerical simulations to validate the analytical results by giving several examples to depict the stability of all equilibriums. The sensitivity analysis of the model's parameters is performed to that can offer recommendations for regulators to reduce the number of smokers.

Journal of Mathematical Epidemiology
Diponegoro University (ID)
Openalex Percentile: Top 17%
Mathematical Biology Tumor Growth
1.37
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