Smoking as Epidemic: Modeling and Simulation Study

Matintu, Sintayehu Agegnehu

doi:10.11648/j.ajam.20170501.14

Cited by 12 publications

(3 citation statements)

References 6 publications

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“…While in [1] a model of five nonlinear differential equations is formulated, distinguishing between to classes of smokers according the the frequency of consumption, high or low. Similar to [1], Sintayehu Matintu [9] studies a non-constant population but holding the other characteristics constant that make a great difference to the model when varied as proposed in this article.…”

Section: Introductionmentioning

confidence: 99%

Analysis of a mathematical model of smoking

Pulecio-Montoya¹,

López-Montenegro²,

Benavides³

2019

ces

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This study presents a mathematical model that represents the population growth dynamics of tobacco consumers based on a system of ordinary nonlinear differential equations. The model is used to determine the Basic Reproductive Number (R 0). Two points of equilibrium are found and their local stability is classified. Finally, the Matlab software is used to present numerical simulations using the fourth-order Runge-Kutta method, and it is shown that the solutions approach an asymptotically stable point, under the variation of R 0 .

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Section: Introductionmentioning

confidence: 99%

Analysis of a mathematical model of smoking

Pulecio-Montoya¹,

López-Montenegro²,

Benavides³

2019

ces

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“…Dalam model epidemi 𝑆𝐼𝑅 , individu dikategorikan menjadi tiga sub populasi; 𝑆 merepresentasikan sub populasi yang rentan terhadap penyakit, 𝐼 merepresentasikan sub populasi yang terinfeksi penyakit pada waktu tertentu dan 𝑅 merepresentasikan sub populasi yang telah pulih setelah terinfeksi suatu penyakit. Penelitian terkait model epidemik perokok sudah pernah dilakukan oleh beberapa peneliti (Agegnehu Matintu, 2017;Alkhudari et al, 2014).…”

Section: Pendahuluanunclassified

Strategi Kontrol Optimal Pada Sistem Dinamik Perokok

Asmianto,

Pusawidjayanti,

Kusumasari

2024

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This study aimed to investigate optimal strategies in dynamic models of smoking. The smoking dynamic model is divided into 3 subpopulations including potential smokers (non-smokers), active smokers who smoke daily, and people who have quit smoking permanently. There are five variables of the smoking dynamic model control strategy, including education related to the dangers of smoking for health, vaccination, tobacco taxation, treatment, and rehabilitation. In solving optimization problems, this study uses the Maximum Pontryagin Principle method. Furthermore, the 4th-order runge kutta method was used to implement numerical solutions and Matlab Software to simulate a control model of smoking dynamics. Based on the simulation results, it can be seen that the control provided is effective in reducing the number of smokers and increasing the number of people who quit smoking.

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“…In relation to the optimal control and application of the maximum principle of Pontryagin, deterministic applications are found in Cancer, HIV AIDS, Tuberculosis, Dengue, Malaria and dynamics in population ecology [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19].…”

Section: Introductionmentioning

confidence: 99%

Problem for the optimal control of cigarette addiction

Munoz¹,

Abello²,

Colorado³

2019

ces

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The formulation and analysis of a deterministic optimal control problem by the Pontryagin maximum principle is presented, using a cost functional attached to a two-dimensional system of non-linear differential equations that interpret the dynamics of cigarette addiction with variable population, mortality due to tobacco consumption (lung cancer and other pathologies) and control for prevention of addiction to smoking. The problem of contour is solved in MATLAB choosing hypothetical parameter values.

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Smoking as Epidemic: Modeling and Simulation Study

Cited by 12 publications

References 6 publications

Analysis of a mathematical model of smoking

Analysis of a mathematical model of smoking

Strategi Kontrol Optimal Pada Sistem Dinamik Perokok

Problem for the optimal control of cigarette addiction

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