Research Article  Open Access
Omar Khyar, Jaouad Danane, Karam Allali, "Mathematical Analysis and Optimal Control of Giving up the Smoking Model", International Journal of Differential Equations, vol. 2021, Article ID 8673020, 13 pages, 2021. https://doi.org/10.1155/2021/8673020
Mathematical Analysis and Optimal Control of Giving up the Smoking Model
Abstract
In this study, we are going to explore mathematically the dynamics of giving up smoking behavior. For this purpose, we will perform a mathematical analysis of a smoking model and suggest some conditions to control this serious burden on public health. The model under consideration describes the interaction between the potential smokers , the occasional smokers , the chain smokers , the temporarily quit smokers , and the permanently quit smokers . Existence, positivity, and boundedness of the proposed problem solutions are proved. Local stability of the equilibria is established by using Routh–Hurwitz conditions. Moreover, the global stability of the same equilibria is fulfilled through using suitable Lyapunov functionals. In order to study the optimal control of our problem, we will take into account a two controls’ strategy. The first control will represent the government prohibition of smoking in public areas which reduces the contact between nonsmokers and smokers, while the second will symbolize the educational campaigns and the increase of cigarette cost which prevents occasional smokers from becoming chain smokers. The existence of the optimal control pair is discussed, and by using Pontryagin minimum principle, these two optimal controls are characterized. The optimality system is derived and solved numerically using the forward and backward difference approximation. Finally, numerical simulations are performed in order to check the equilibria stability, confirm the theoretical findings, and show the role of optimal strategy in controlling the smoking severity.
1. Introduction
Smoking is a behavior in which a substance is heated or burned, and the derived smoke is breathed through the mouth or the nose and goes directly to the lungs. The active substances of the smoke are rapidly transmitted from the lungs to the bloodstream and therefore to the brain where they operate. The most common smoking substance is known as tobacco which is consumed by nearly 1.3 billion smokers worldwide [1]. More than of all deaths are related to tobacco consumption [2], making the burden of smoking a global health priority. Smoking is one of the main risk factors of many noncommunicable diseases such as diabetes, cancer, cardiovascular, and chronic respiratory diseases [3–8].
Smoking dynamics were modelled by several authors in order to study the interaction between the model compartments. The simplest system that describes such dynamics was introduced in 1997 by CastilloGarsow et al. [9], in which we find three main classes representing the potential smokers , the chain smokers , and the permanently quit smokers . The authors studied the local stability of the equilibria and suggest a nonlinear smoking relapse model. Later, in 2008, another compartment of temporarily quit smokers was added to the previous one [10]. Both the local and the global stability of the equilibria were performed depending on the smoking generation number. The stochastic study of the smoking model was tackled in [11]; the authors consider Brownian motion as perturbation of their equations and establish the sufficient condition for mean square stability. Recently, the class of occasional smokers was taken into account in [12]. The authors studied the following smoking model:where is the recruitment rate, is the transmission rate of potential smokers to occasional smoking class, denotes the rate of becoming chain smoker for a occasional smoker, and is the rate of becoming quit smoker. Furthermore, the natural death rate of all of the subpopulations is denoted by and their death rate related to smoking behavior is denoted by . Here, the incidence rate is given by . We note that for squareroot incidence function , and the smoking problem is tackled by [13]; while with bilinear incidence function , the smoking problem is studied in [14].
In this paper and inspired by the previous works, we are interested in studying smoking problem by taking into consideration the saturated incidence rate ; indeed, this saturated rate describes the crowding effect of potential and occasional smokers in smoking areas. The problem under consideration will take the following form:
The reversion to the smoking phenomenon is highlighted in this model by the parameter which represents the ratio of temporarily quit smokers who start to smoke again, while is the portion of quitters who quit smoking permanently. The flowchart of our suggested smoking model is illustrated in Figure 1. Our main interest is to study the equilibria stability of model (2) and to study the role of the optimal control strategy in reducing the number of smokers. For this last reason, we will introduce two controls; the first will represent the government prohibition of smoking in public areas, while the second control will symbolize the educational campaigns and the increase of cigarette cost. It will worthy to notice that the optimal control strategy have shown its efficiency in controlling many diseases such as tuberculosis and COVID19 [15, 16]. Also, the optimal control has been studied in some fractional mathematical models [17]. Finally, as applications of many fractional derivatives on smoking modelling issues, we can cite [18–23].
Different studies on smoking [16] have shown the clear impact of tobacco consumption on the rapid deterioration of health in COVID19 patients. In fact, smokers are more vulnerable to COVID19 because they already have respiratory problems, which considerably increase the risk that their condition worsens more quickly than nonsmokers. Hence, our main ambition is to check the role of governement of prohibition of smoking in public area, education campaign, and the increase of cigarette cost in controlling the severity of the smokers’ addiction.
This paper is organized as follows. In Section 2, we will establish the wellposedness of our suggested mathematical model by proving the existence, positivity, and boundedness of solutions. We fulfilled the mathematical analysis of the model, by proving the local and global stability of the equilibria in Sections 3 and 4. The optimization analysis of our model is given in Section 5; in Section 6 an appropriate numerical algorithm is constructed and some numerical simulations are given. The numerical results will show the importance of the optimal control in reducing the smokers’ addiction. In Section 7, we will give concluding remarks.
2. The Wellposedness Result and Smokers’ Generation Number
2.1. The Wellposedness Result
Let be the Banach space of continuous functions mapping into with the topology of uniform convergence. By using the fundamental theory of functional differential equations [24], we prove that there exists a unique local solution of system (2) with initial data . The following proposition guarantees that this solution exists globally.
Proposition 1. The solution of the studied model (2) remains positive and bounded for all . Moreover, we have
Proof. The solution is nonnegative, for all . Supposing the contrary, let be the first time such that and . From the first equation of system (2), we have , which leads a contradiction. Similar proofs show the positivity of , , , and , for all . Indeed, according to system (2), we have , , , and . This completes the proof of positivity.
About boundedness, let the total population .
From system (2), we have
Therefore,
At , we have
Then,
Consequently,
Since , for all , we conclude that .
2.2. The Smokers’ Generation Number
The smokers’ generation number is defined as the average number of new smokers resulting from one smoker, in a population consisting of potential smokers only. We use the next generation matrix to calculate the smokers’ generation number [25].
The formula that gives us the smokers’ generation number is , where stands for the spectral radius, is the nonnegative matrix of new smokers, and is the matrix of smoking transition associated with model (2). After a simple resolution, we obtain
3. The Problem Equilibria and Local Stability
3.1. The Smoking Problem Equilibria
It is straightforward to check that model (2) has two steady states developed as follows:(i)The smokingfree equilibrium state is .(ii)The smokingpresent equilibrium point is such that
3.1.1. Local Stability of the SmokingFree Equilibrium
The local stability of the smokingfree equilibrium is given by the following result.
Proposition 2. (1)If , then the smokingfree equilibrium, , is locally asymptotically stable.(2)If , then is unstable.
Proof. The Jacobian matrix of system (2) at is given by
The characteristic polynomial of is
Therefore, the eigenvalues of are given as follows:with . It is clear that and if .
We conclude that is locally asymptotically stable when .
3.1.2. Local Stability of the SmokingPresent Equilibrium
In order to study the local stability of the smokingpresent equilibrium , let us first give the Jacobian matrix of system (2) at :
The characteristic polynomial of iswithsuch that and (remark that ).
The first eigenvalue of the above characteristic polynomial is ; the second eigenvalue is .
If , then ; consequently, .
Thus, it is easy to verify that , , and . Then, by application of Routh–Hurwitz theorem, the other eigenvalues, , , and , have negative real parts.We conclude that is locally asymptotically stable in case when .
4. The Smoking Problem Global Stability Analysis
4.1. Global Stability of the SmokingFree Equilibrium
Theorem 1. If , then the smokingfree equilibrium is globally asymptotically stable.
Proof. First, we denote , , and we consider the following Lyapunov function defined in by
The time derivative is given by
If , then ; consequently, according to the Lyapunov theorem, the smokingfree equilibrium is globally asymptotically stable.
4.2. Global Stability of the SmokingPresent Equilibrium
Theorem 2. If , then the smokingpresent equilibrium point is globally asymptotically stable.
Proof. We consider the following Lyapunov function defined in as follows:
The time derivative is given by
It is easy to verify that
So,
Then,
Therefore,
By application of the relation between geometric and arithmetic means, we obtain
Since , we have that . Consequently, .
We conclude that the smokingpresent equilibrium point is globally asymptotically stable when .
5. Optimal Control of Smoking Problem
5.1. The Smoking Optimization Problem
In order to formulate our smoking optimization problem, we will introduce to problem (2) two controls and . The smoking problem with controls becomeswith , , , , and .
The first control represents the government prohibition of smoking in public area, while the second control symbolizes the education campaign and the increase of cigarette cost. The aim of the first control is to reduce the contact between nonsmokers and smokers; the role of the second control is to prevent occasional smokers to become chain smokers. Since the two controls represent the effectiveness of each strategy, we will have the fact that .
The smoking optimization problem consists in maximizing the objective functional defined as follows:where represents the time needed for the undertaken control measures and the two positive constants and are based on the costs of each strategy and , respectively. The interest is to maximize the objective functional (27) which would mean that we want to increase the nonsmokers’ number and to decrease the cost of each strategy. More precisely, we are looking for optimal controls such thatwhere is the control set defined by
5.2. The Smoking Optimality System
Pontryagin’s minimum principle [26] transforms our optimization problem into a Hamiltonian maximization problem, where the Hamiltonian is given by with
The optimality system is summarized by the following result.
Proposition 3. For any solution of the smoking optimization problem, there exist the adjoint variables, , and , satisfying the system of equationsunder the transversality conditions,In addition, the optimal control is given by
Proof. The five adjoint equations and the transversality conditions can be obtained via Pontryagin’s minimum principle [26] such thatSimple calculations lead toThe two optimal controls and can be found from the following optimality conditions:However, we haveSince the two controls belong to the interval , we will haveIn addition, we have
6. Numerical Simulations
In order to solve numerically the optimization system, we will use the classical discretized scheme based on forward and backward finitedifference approximation scheme [28–30]. The numerical method will be implemented under Algorithm 1. The parameters values of our numerical simulations are given in Table 1.

The smokingfree equilibrium stability result is illustrated numerically in Figure 2. In fact, we clearly see that the number of potential smokers increase and reach its maximum value. However, the other components of our smoking problem representing occasional smokers, chain smokers, temporarily quit smokers, and permanently quit smokers vanish. Within the used parameters in this figure (see Table 1), we can easily calculate the smokers’ generation number, , which means that this number is less than unity, and we have confirmation, from our theoretical findings, that the smokingfree equilibrium is stable. Moreover, we observe that all the curves converge toward the smokingfree equilibrium which means that our numerical result supports the theoretical result concerning the stability of the first steady state.
On the contrary, the smokingpresent equilibrium stability is represented in Figure 3. Here, all the smoking problem compartments remain at strictly positive levels. From Table 1, we can calculate the smokers’ generation number relative to this figure, . Since it is greater than unity, we have confirmation from our theoretical results that the smokingpresent equilibrium is stable. Both the numerical and the theoretical results agree about the stability of the smokingpresent equilibrium. From this same figure, we see the convergence of the different curves toward the smokingpresent equilibrium which means this second steady state can be found numerically or calculated theoretically. We conclude that the local and global theoretical results concerning the stability of the smokingfree and the smokingpresent equilibria are in good agreement with our numerical results.
Our third numerical result concerns the comparison between the model with control strategy and the model without any control. Indeed, Figure 4 shows the evolution of potential smokers, occasional smokers, chain smokers, temporarily quit smokers, permanently quit smokers, and the two controls as the function of time. We clearly observe that, with control strategy, we will have more potential smokers than without control measures. Also, it is clearly seen that, with control measures, a significant reduce of occasional and chain smokers is observed. This means the roles of control strategies are to reduce the number of occasional and chain smokers which can lead to save many lives in smoking population. Besides, with control strategy, we observe that, without any control strategy, the curves representing the different smoking compartment converge toward . However, with taking significant control measures, those curves converge toward which confirms the role of control in reducing the smoking habit. In addition, we remark from the two controls’ behavior that we should give more importance to the first control than the second. It is also observed that the two controls should be also maintained at a constant level in order to have the desired good result.
(a)
(b)
(c)
(d)
(e)
(f)
7. Conclusion
We have studied in this paper mathematically and numerically the dynamics of giving up smoking behavior. The suggested smoking model consists of a system of five differential equations representing the potential smokers, the occasional smokers, the chain smokers, the temporarily quit smokers, and the permanently quit smokers. We have established the wellposedness result of our smoking problem by proving the existence, positivity, and boundedness of the smoking problem solution. Both the equilibria local and the global stability results are fulfilled. To study the role of control measures on our smoking problem dynamics, two different controls are introduced to the model. The role of the first control is to reduce the contact between nonsmokers and smokers, while the objective of the second is to prevent occasional smokers to become chain smokers. The existence and the characterization of the two optimal controls are discussed by using the Pontryagin minimum principle. The optimality system is solved numerically using the classical forward and backward difference numerical scheme. It was established that the numerical simulations concerning the stability of the smokingfree and the smokingpresent equilibria are in good agreement with the local and global theoretical results. Moreover, the numerical results confirm the important role of control measures in reducing the number of occasional and chain smokers which may save significant number of lives in the smoking population.
Data Availability
No data were used to support this study.
Conflicts of Interest
The authors declare that there are no conflicts of interest regarding the publication of this study.
References
 P. Jha, “Smoking cessation and ecigarettes in China and India,” BMJ, vol. 367, p. l6016, 2019. View at: Publisher Site  Google Scholar
 M. T. PerezWarnisher, M. P. C. de Miguel, and L. M. Seijo, “Tobacco use worldwide, legislative efforts to curb consumption,” Annals of Global Health, vol. 85, no. 1, pp. 1–9, 2019. View at: Publisher Site  Google Scholar
 A. Boutayeb and S. Boutayeb, “The burden of non communicable diseases in developing countries,” International Journal for Equity in Health, vol. 4, no. 1, pp. 2–8, 2005. View at: Publisher Site  Google Scholar
 K. Anand, B. Shah, K. Yadav et al., “Are the urban poor vulnerable to noncommunicable diseases? A survey of risk factors for noncommunicable diseases in urban slums of Faridabad,” The National Medical Journal of India, vol. 20, no. 3, pp. 115–120, 2007. View at: Google Scholar
 I. Berlin, D. Thomas, A. L. Le Faou, and J. Cornuz, “COVID19 and smoking,” Nicotine Tobacco Research. Official Journal of the Society for Research on Nicotine and Tobacco, vol. 22, no. 9, pp. 1650–1652, 2020. View at: Publisher Site  Google Scholar
 J. S. Alqahtani, T. Oyelade, A. M. Aldhahir et al., “Prevalence, severity and mortality associated with COPD and smoking in patients with COVID19: a rapid systematic review and metaanalysis,” PLoS One, vol. 15, no. 5, Article ID e0233147, 2020. View at: Publisher Site  Google Scholar
 M. W. Higgins, J. B. Keller, and H. L. Metzner, “Smoking, socioeconomic status, and chronic respiratory Disease1,2,” American Review of Respiratory Disease, vol. 116, no. 3, pp. 403–410, 1977. View at: Publisher Site  Google Scholar
 J.E. Park, S. Jung, A. Kim, and J.E. Park, “MERS transmission and risk factors: a systematic review,” BMC Public Health, vol. 18, no. 1, p. 574, 2018. View at: Publisher Site  Google Scholar
 C. CastilloGarsow, G. JordanSalivia, and A. R. Herrera, “Mathematical models for the dynamics of tobacco use, recovery and relapse,” Cornell Uneversity, Ithaca NY, USA, 1997, Technical Report Series; BU1505M. View at: Google Scholar
 O. Shoromi and A. B. Gumel, “Curtailing smoking dynamics, a mathematical modeling approach,” Applied Mathematics and Computation, vol. 195, no. 2, pp. 475–499, 2008. View at: Google Scholar
 A. Lahrouz, L. Omari, D. Kiouach, and A. Belmaâti, “Deterministic and stochastic stability of a mathematical model of smoking,” Statistics & Probability Letters, vol. 81, no. 8, pp. 1276–1284, 2011. View at: Publisher Site  Google Scholar
 G. ur Rahman, R. P. Agarwal, Q. Din, and Q. Din, “Mathematical analysis of giving up smoking model via harmonic mean type incidence rate,” Applied Mathematics and Computation, vol. 354, no. C, pp. 128–148, 2019. View at: Publisher Site  Google Scholar
 A. Zeb, G. Zaman, and S. Momani, “Squareroot dynamics of a giving up smoking model,” Applied Mathematical Modelling, vol. 37, no. 7, pp. 5326–5334, 2013. View at: Publisher Site  Google Scholar
 H. F. Huo and C. C. Zhu, “Influence of relapse in a giving up smoking model,” Abst Appl Anal Hindawi January, vol. 2013, Article ID 525461, 12 pages, 2013. View at: Publisher Site  Google Scholar
 M. Elhia, O. Balatif, L. Boujallal, and M. Rachik, “Optimal control problem for a tuberculosis model with multiple infectious compartments and time delays,” An International Journal of Optimization and Control: Theories & Applications, vol. 11, no. 1, pp. 75–91, 2021. View at: Publisher Site  Google Scholar
 N. Moussouni and M. Aliane, “Optimal control of COVID19,” An International Journal of Optimization and Control: Theories & Applications, vol. 11, no. 1, pp. 114–122, 2021. View at: Publisher Site  Google Scholar
 B. B. I Eroglu, D. Avci, and N. Özdemir, “Optimal control problem for a conformable fractional heat conduction equation,” Acta Physica Polonica A, vol. 132, no. 3, pp. 658–662, 2017. View at: Google Scholar
 E. Uçar, S. Uçar, F. Evirgen, and N. Özdemir, “Investigation of Ecigarette smoking model with mittagleffler kernel,” Foundations of Computing and Decision Sciences, vol. 46, no. 1, pp. 97–109, 2021. View at: Publisher Site  Google Scholar
 S. Uçar, E. Uçar, N. Özdemir, and Z. Hammouch, “Mathematical analysis and numerical simulation for a smoking model with AtanganaBaleanu derivative,” Chaos, Solitons & Fractals, vol. 118, pp. 300–306, 2019. View at: Publisher Site  Google Scholar
 J. Singh, D. Kumar, M. A. Qurashi, and D. Baleanu, “A new fractional model for giving up smoking dynamics,” Advances in Difference Equations, vol. 2017, no. 1, pp. 1–16, 2017. View at: Publisher Site  Google Scholar
 H. Singh, D. Baleanu, J. Singh, and H. Dutta, “Computational study of fractional order smoking model,” Chaos, Solitons & Fractals, vol. 142, Article ID 110440, 2021. View at: Publisher Site  Google Scholar
 H. Alrabaiah, A. Zeb, E. Alzahrani, and K. Shah, “Dynamical analysis of fractionalorder tobacco smoking model containing snuffing class,” Alexandria Engineering Journal, vol. 60, no. 4, pp. 3669–3678, 2021. View at: Publisher Site  Google Scholar
 V. F. MoralesDelgadoa, J. F. GomezAguilar, M. A. TanecoHernandez, R. F. EscobarJimenezc, and V. H. OlivaresPeregrino, “Mathematical modeling of the smoking dynamics using fractional differential equations with local and nonlocal kernel,” The Journal of Nonlinear Science and Applications, vol. 11, no. 8, pp. 994–1014, 2018. View at: Publisher Site  Google Scholar
 J. K. Hale, S. M. V. Lunel, and L. S. Verduyn, “Introduction to functional differential equations,” Springer Sci Business Media, vol. 99, 1993. View at: Publisher Site  Google Scholar
 O. Diekmann, J. A. P. Heesterbeek, and M. G. Roberts, “The construction of nextgeneration matrices for compartmental epidemic models,” Journal of The Royal Society Interface, vol. 7, no. 47, pp. 873–885, 2010. View at: Publisher Site  Google Scholar
 L. Göllmann, D. Kern, and H. Maurer, “Optimal control problems with delays in state and control variables subject to mixed controlstate constraints,” Optimal Control Applications and Methods, vol. 30, no. 4, pp. 341–365, 2009. View at: Publisher Site  Google Scholar
 L. Pang, Z. Zhao, S. Liu, and X. Zhang, “A mathematical model approach for tobacco control in China,” Applied Mathematics and Computation, vol. 259, pp. 497–509, 2015. View at: Publisher Site  Google Scholar
 A. B. Gumel, P. N. Shivakumar, and B. M. Sahai, “A mathematical model for the dynamics of HIV1 during the typical course of infection,” Nonlinear Analysis: Theory, Methods & Applications, vol. 47, no. 3, pp. 1773–1783, 2001. View at: Publisher Site  Google Scholar
 H. Laarabi, A. Abta, and K. Hattaf, “Optimal control of a delayed SIRS epidemic model with vaccination and treatment,” Acta Biotheoretica, vol. 63, no. 2, pp. 87–97, 2015. View at: Publisher Site  Google Scholar
 J. Danane, A. Meskaf, and K. Allali, “Optimal control of a delayed hepatitis B viral infection model with HBV DNAcontaining capsids and CTL immune response,” Optimal Control Applications and Methods, vol. 39, no. 3, pp. 1262–1272, 2018. View at: Publisher Site  Google Scholar
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Copyright © 2021 Omar Khyar et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.