Modeling the Combined Effect of Hepatitis B Infection and Heavy Alcohol Consumption on the Progression Dynamics of Liver Cirrhosis

Liver cirrhosis is the liver scarring in human body which causes the liver organ failure to function its regular activities eﬀectively and normally. In this paper, we proposed and analyzed the combined eﬀect of hepatitis B virus (HBV) infection and heavy alcohol consumption on the progression dynamics of liver cirrhosis. In order to study the progression dynamics of cirrhosis and to describe the eﬀect of alcohol intake variation on a chronic hepatitis B patients a deterministic model and a logistic function are considered, respectively. The detailed proof of the positivity, boundedness, and biological feasibility of the proposed model is presented. The disease endemic equilibrium point and the existence of bifurcation were investigated in detail. We established and proved the existence theorems for forward and backward bifurcations, respectively. Finally, we performed large scale numerical simulations to verify the analytic work and the result of the numerical simulations reveal that heavy alcohol consumption signiﬁcantly accelerates the progression of liver cirrhosis in chronic hepatitis B infected individuals.


Introduction
Liver cirrhosis is a late stage of liver diseases where a healthy liver tissue is possibly transformed into scar tissue when forced to process alcohol or subjected to viral infections, which in turn makes the liver organ fail to function normally. It is characterized by high morbidity and mortality in the world where one million deaths are attributable to cirrhosis every year [1]. Liver cirrhosis is caused by alcohol abuse, chronic viral hepatitis (B and C), and nonalcohol fatty diseases [2]. Although liver cirrhosis has no signs until liver damage is extensive, acute infection individuals gradually develop symptoms such as easily bleeding, nausea, loss of appetite, and sudden weight loss. e two main etiologic factors of liver cirrhosis include HBV infection and heavy alcohol consumption [3]. Between 350 and 400 million people are chronic carriers and 2 billion people are living with HBV infection in the world [4]. Hepatitis B, one of the major risk factors of liver cirrhosis which originates from hepatitis B virus, is more likely to occur among people with long term viral hepatitis. e HBV infection involves two phases: acute infection and chronic HBV carriers [5]. About 90% of acute infection individuals are capable of clearing HBV from their blood within 6 months [6,7]. However, the chronic phase is the end-stage liver disease which results in liver inflammation and can lead to life-long illness. is contagious disease is spreading either vertically from infected mothers to her infants during delivery or horizontally through unsafe sexual practices, blood transfusion with another infected human, and exposures to infectious blood and body fluids [6,8]. Liver cirrhosis is heavily prevalent in hepatocellular carcinoma (HCC) when compared with HBV patients [9], Liver cirrhosis development is higher in chronic HBV patients with high viral load than in those with low viral load [10].
Heavy alcohol consumption is another major risk factor of chronic liver cirrhosis development in which about 38% of people older than 15 years consume approximately more than 17 litres of pure alcohol annually (see [4] and references therein). About 10%-20% of heavy drinkers of alcohol commonly develop cirrhosis after 10 or more years. Specifically, drinking 50 grams of pure alcohol daily for 10 to 20 years is the minimum amount of alcohol required to generate cirrhosis. Heavy alcohol consumption causes rapid progression of liver disease, weakens innate and adaptive immunity, and allows HBV to persist chronically [3]. Many studies reveal that there is no significant increase to cirrhosis for people who consume less than 50 grams of pure alcohol per day. Consumption of greater than 50 grams of pure alcohol per day for both males and females accelerates the progression of cirrhosis in chronic hepatitis B carriers and consumption of greater than 300 grams of alcohol per day is poisonous and lethal for a 60 kg weighing person [4,5,11,12]. Moreover, alcohol abuse exponentially increases the risk for liver cirrhosis [10,13].
Khatun and Biswas [2] clarified the transmission dynamics of liver cirrhosis from HBV and concluded that the disease can be controlled through vaccination and treatment using a mathematical model and optimal control strategy. Ganesan et al. [3] suggested that the combination of HBV infection and alcohol abuses can provide detrimental consequences in rapid end-stage liver disease. Iida-Ueno et al. [4] investigated the association between hepatitis B virus infection and alcohol consumption. e authors concluded that heavy alcohol consumption within chronic HBV infected patients significantly increases the progression of liver disease to cirrhosis, on average fivefold increased risk. Din et al. [5] formulated an epidemic model to investigate the viral dynamics of HBV by considering the impact of acutely infected individuals in the vertical transmission of HBV infection. e authors incorporated two time dependent controls in their model and concluded that effective implementation of the control strategies for the long-run is possible enough in order to eradicate HBV in the community. Khan et al. [6] developed a deterministic mathematical model to qualitatively analyze the impact of migration, vaccination, and hospitalization on the spread and control of HBV. e authors concluded that the migration, vaccination, and hospitalization are effective measures to predict the intervention strategies. Macías-Díaz et al. [7] presented a nonstandard numerical design to analyze a discrete three-dimensional HBV epidemic model. Mokdad et al. [8] suggested preventive measures to control and reduce liver cirrhosis risk factors should be urgently strengthened. Vonghia et al. [11] related binge drinking with body mass index and suggested that for a 60 kg person drinking 300 g of pure alcohol per day which is the lethal amount can kill the person or poison his blood. Yang et al. [9] studied the prevalence of cirrhosis in HBV and hepatocellular carcinoma (HCC) and the result shows that it was 88% and 97% among HBV and hepatitis C virus (HCV) patients, respectively. Khajji et al. [14] analyzed the impact of the addiction treatment centers on system stability and awareness creation programs on the drinkers.
Mathematical models are widely used and can be employed to study the dynamics behavior of infectious diseases such as HBV infection, liver cirrhosis, cholera, and so on. e studies on these infectious diseases highlight how to control the spread and predict its future damage on the lives and economies of the country. Interestingly, there is a large scale of mathematical models developed on the transmission dynamics and control mechanisms of HBV infection, alcohol drinking pattern, and liver cirrhosis reduction. e authors in [14,15] formulated a continuous and discrete mathematical model to study the dynamical behavior of alcohol drinking with the impact of private and public addiction treatment centers. Park et al. [16] discussed the factors associated with alcohol consumption in HBV carriers in Korea and identified high-risk alcohol consumption as consuming at least 60 g of alcohol on one occasion and 40 g of alcohol on more than one occasion for male and female, respectively. Ullah et al. [17] analyzed the dynamics of HBV transmission model with hospitalized class. Zou et al. [18] developed a deterministic model to analyze the impact of sexual transmission on the disease prevalence. Khan et al. [19] illustrated the merits of media coverage with the help of dynamical model for hepatitis B. Zhao et al. [20] studied the application of vaccination strategies in China against HBV infection and found that long term effective immunization is needed by providing higher vaccination coverage. Zhou et al. [21] compared excessive drinking and moderate alcohol consumption and concluded that alcohol abuse in patients with chronic HBV infection increases liver inflammation which finally accelerates the progression of liver cirrhosis while moderate alcohol consumption has no significant effect on its progression.
Even though there is a safe and effective vaccine available for HBV, HBV related mortality and morbidity are still there in the top list and the progression of liver cirrhosis is increasing in the world. e efficacy of drugs aimed at breaking the progression of liver fibrosis was studied by Friedman and Hao [22] by developing a mathematical model of liver cirrhosis. Hence, we will formulate a deterministic model along with logistic alcohol intake variation to investigate the combined effect of HBV infection and heavy alcohol consumption on the progression dynamics of chronic liver cirrhosis, which is not considered by any scholar yet, to the best of the knowledge of the authors.
is article is organized as follows. Section 2 is devoted to model formulation and presents the fundamental properties of the model. In Section 3, positivity of the solution, boundedness of the feasible region, disease free-equilibrium, and the existence of disease endemic equilibrium will be presented and the stabilities of disease free-equilibrium (DFE) and endemic equilibrium (EE) are checked in Section 4. In Section 5, we will analyze the bifurcation phenomena and prove the existence of forward and backward bifurcation of the model. In Section 6, the local sensitivity analysis of the model will be calculated and its biological interpretation will be provided. Section 7 is devoted to explore the simulation of the liver cirrhosis model. Finally, conclusion of the overall work is presented in Section 8.

Model Formulation
Here, we develop a deterministic mathematical model that consists of four ordinary differential equations along with a logistic model with alcohol consumption variation to study the progression dynamics of chronic liver cirrhosis. We partitioned the whole population into four compartments: the susceptible individuals S(t), the acutely infected individuals I(t), the liver cirrhotic individuals C(t), and the recovered individuals Susceptible individuals S(t) are individuals who are not yet infected with the disease at time t. e susceptible population do not have an immunity against a potential infection. is class is generated by the recruitment rate Λ and decreased by disease transmission coefficient β(A), where A(t) is the amount of pure alcohol consumed in grams per day. Further, it is decreased by the natural death rate μ. Hence, the above hypotheses lead to a differential equation of the susceptible population at time t e parameters ] and c represent the vaccination rate of newborn infants and the rate of relative infectiousness of cirrhosis over acute infection, respectively. All population classes are subjected to the natural death rate μ. e acutely infected populations I(t) are individuals who are currently infected with the disease, develop strong enough immunity to clear the disease from the body, and are capable of spreading the disease to those in the susceptible phase. is class is generated by the transmission coefficient β(A) and it is decreased by a spontaneous recovery rate η, a cirrhosis progression rate σ, and natural and disease related death rates μ and μ 1 , respectively. e acute infection compartment is given by the following differential equation: (2) Liver cirrhotic individuals C(t) are asymptomatic infection individuals who are infected with the end-stage liver disease and can transmit the disease at any time t. e cirrhotic compartment is increased by a cirrhosis progression rate σ and a proportion q, (0 < q ≤ 1) of relapse rate. It is decreased by a cirrhosis recovery rate δ and natural and disease related death rates μ and μ 2 , respectively. e differential equation governing the liver cirrhosis compartment is given by Finally, the recovered population R(t) is generated by cirrhosis recovery rate δ and a spontaneous acute recovery rate η. is class is further decreased by natural death rate μ. Hence, the recovery class is governed by the following equation: Moreover, we assumed the variation in the amount of pure alcohol consumption of young adults is supposed to be given by a logistic model with an alcohol consumption growth rate r. e rate of change of alcohol consumption at any time t is given by where A 0 represents a minimum amount of pure alcohol consumed and A max represents a maximum amount of pure alcohol consumed in grams. Furthermore, we considered the following assumptions to enhance the model formulation for the progression dynamics of chronic liver cirrhosis.
(i) All state variables are nonnegative and known quantities. (ii) e transmission coefficient depends on the variation of alcohol consumption of HBV infection patient.
β 0 is the mean transmission rate of liver cirrhosis and β 1 is the incremental rate of pure alcohol consumption. (iii) Individuals aged above 15 years are considered. (iv) e average amount of alcohol consumption for male and female is the same. (v) Alcohol consumption with meal is taken into account. Figure 1 represents the transfer diagram for chronic liver cirrhosis.
Journal of Mathematics is is done along with the subsidiary condition

Model Analysis
In this section, we present the solution of the model under discussion and affirm that the solution is nonnegative for long time in future.

Positivity and Boundedness of Solution
Proof. To show the positivity of the state S(t), I(t), C(t), R (t), and A(t), we first present the solution of the first equation of system (7).
Integration of (9) with respect t yields From equation (10), we observe that S(t) is nonnegative for all t > 0. In a similar procedure, one can verify that It is biologically feasible region of the initial value problems (7) and (8).
Proof. For the sake of convenience, we let r 1 � μ + ], r 2 � μ + μ 1 + η + σ, r 3 � μ + μ 2 + δ throughout this paper. Recall that the total population N(t) at any time t is given by Differentiation of equation (12) with respect t gives Simplification of equation (13) in the absence of infectious yields After a slight arrangement and integration of equation (14) with respect to t we obtain By taking limit as t ⟶ ∞ on the inequality in equation (15) we obtain Hence, each solution of the initial value problems (7) and (8) remains in equation (11) for all t > 0. Figure 1: Transfer diagram of liver cirrhosis. Hence, combining equations (1)-(5) above together with the assumptions (i)-(v), finally, we obtain the following system of differential equations for the progression dynamics of liver cirrhosis.

Basic Reproduction Number (R 0 ).
To calculate the expression for basic reproduction number (R 0 ), we want to determine the DFE of system (7). For this purpose, we set the right hand side (RHS) of system (7) equal to zero and substitute S(t) � S 0 > 0, erefore, ε 0 � ε 01 , ε 02 is the set of DFE point of system (7). By using DFE, we can calculate the basic reproduction number (R 0 ). We follow the work of Watmough and Driessche method [23] to calculate R 0 . Consider the system and the transition matrix V is given by Hence, the next generation matrix calculated from equations (18) and (19) becomes Now, the dominant eigenvalue ρ(FV − 1 ) represents (R 0 ) of system (7) which is given by From equation (21), we calculate two different expressions for R 0 . e expression for the first basic reproduction number R 01 is calculated when a chronic hepatitis B patient consumes a minimum amount of alcohol (i.e., A(t) � A 0 ).
e expression for the second basic reproduction number R 02 of system (7) is calculated when a chronic hepatitis B carrier consumes maximum amount of alcohol (i.e., A(t) � A max ).

Existence of Endemic Equilibrium.
In this current section, we investigate the disease endemic equilibrium of system (7). One interesting idea behind the endemic equilibrium is that it is used to determine the persistence of liver cirrhosis in the population. Let us denote the steady state solution of system (7) by us, the reduced part of system (7) will take the form Hence, after a tiresome calculation we worked out the first endemic equilibrium ε * 1 from equation (24) which is given by where A * (t) � A 0 . Hence, the existence of the first endemic equilibrium E * 1 in equation (25) depends on R 01 . is implies that there is at least one positive endemic equilibrium point if and only if R 01 > 1.
Similarly, the second endemic equilibrium ε * 2 when , , Journal of Mathematics 5 Hence, the existence of E * 2 in equation (26) depends on R 02 . erefore, the disease endemic equilibrium

Stability Analysis
To present the local and global asymptotic stability of the two equilibria of system (7), we use the Jacobian matrices at DFE and EE for local stability and the Lyapunov function for the global stability of both equilibria. (7) is locally asymptotically stable if R 01 < R 02 < 1 and unstable otherwise.

Local Stability Analyses
Proof.
e desired Jacobian matrix evaluated at E 0 when where e characteristic polynomial of equation (27) becomes e first three eigenvalues of equation (28) are 5 . ey are negative and the existence of the remaining eigenvalues is determined using the Routh-Hurwitz condition such that hold. us, the necessary condition for the Routh-Hurwitz criteria is satisfied whenever R 02 < 1. erefore, the DFE E 0 of system (7) is locally asymptotically stable if (7) is locally asymptotically stable in Ω if R 02 > R 01 > 1 and unstable otherwise.
Proof. We compute the desired Jacobian matrix J(ε * ) calculated at E * which becomes e first two roots of equation (30) are λ � − μ < 0 and λ � − r 5 < 0 and the remaining roots can be calculated from the polynomial equation e coefficient a i (i � 1, 2, 3) of equation (31) becomes Hence, the values a i (i � 1, 2, 3) are positive terms if R 02 > R 01 > 1. e necessary and sufficient condition for the local asymptotic stability of E * of system (7) holds if H i > 0 of the third-order degree polynomial in equation (31) is satisfied for i � 1, 2, 3.
Clearly, H I in equation (33) is positive if R 02 > R 01 > 1. Hence, the disease endemic equilibrium E * of system (7) is locally asymptotically stable in the biological feasible region if R 02 > R 01 > 1.

Global Stability Analysis.
In the following part of work, we discuss the global behavior of both equilibria of system (7) using the well-known LaSalle's invariant principle [24] by developing an appropriate Lyapunov functions.
Proof. To examine the global behavior of E * , we develop a Lyapunov function of the form (see [25]) By differentiating equation (37) along its trajectories with respect to t we obtain Further, it follows that Without loss of generality S * + I * + C * + R * ≤ (Λ/μ), and substituting equations (12) and (14) in the above equation gives Upon simplification and rearrangement of equation (39) we obtain After simplification and a slight arrangement of equation (43) we obtain us, (dV/dt) ≤ 0 when R 02 > R 01 > 1. Clearly, (dV/dt) � 0 if and only if S � S * , I � I * , C � C * , and R � R * . Hence, the largest compact set in (S, I, C, R) ∈ Ω: (dV/dt) � 0 { } is the singleton ε * � ε * 1 , ε * 2 , which is a disease endemic equilibrium. erefore, by LaSalle's invariant principle [24], E * is globally asymptotically stable in the biological feasible region when R 02 > R 01 > 1.

Bifurcation Analysis
In this section, we investigate the bifurcation phenomena of system (7). We employ the center manifold theory presented Journal of Mathematics in [26] to discuss the existence of bifurcation. We establish the existence theorem for backward bifurcation and present detail proof. A backward bifurcation is maintained when stable DFE and stable EE coexist in a specified biological region. In our model, both forward bifurcation and backward bifurcation are sufficiently presented.
Let us denote us, in vector notation it becomes x → � (x 1 , x 2 , x 3 , x 4 , x 5 ) and Let f i (i � 1, . . . , 5) be a continuous twice differential function defined on R 5 × R.
us, equation (44) can be written in the following dynamical system form Now setting R 0 � 1 and solving the expression for the bifurcation parameter β(A * ) and finally replacing the result by β * (A * ) yield e linearization matrix of equation (45) evaluated at the DFE E 0 and β * (A * ) becomes e characteristic polynomial of equation (47) becomes We observe that equation (48) has four negative real part eigenvalues and one zero eigenvalue which assures us the existence of bifurcation phenomena for the proposed system (7).
Next, we will establish the existence of forward and backward bifurcations for the model under discussion.
Proof. We investigate the bifurcation phenomenon for the transmission of liver cirrhosis of the model under discussion. We set R 01 � 1 and calculate the bifurcation parameter β 0 and consequently replace it with β * 0 . us, Let us represent the right eigenvector w � (w 1 , w 2 , w 3 , w 4 , w 5 ) T corresponding to a zero eigenvalue (i.e., λ � 0), Simplification and rearrangement of equation (50) Similarly, we represent the left eigenvector v � (v 1 , v 2 , v 3 , v 4 , v 5 ) corresponding to a zero eigenvalue λ � 0.

Substitution of equations (51) and (55) into equation (54) yields
Similarly, substitution of equations (53) and (56) into equation (54) yields Hence, the local dynamics of system (7) exhibits forward bifurcation around the first disease free-equilibrium, E 01 , which implies that there is at least one positive endemic equilibrium at R 01 � 1 if R 01 < 1.
Simplifying and rearranging equation (62) gives Substituting the above equations S * (t) and I * (t) in the first equation of system (7) for A * � [A 0 , A max ]C * (t) ≠ 0, we get where Clearly, a > 0 and b > 0 if R 0 < 1. Hence, the coefficients of the quadratic equation are positive but b may be negative. Existence of nonnegative solution of the above quadratic equation ultimately depends on the sign of b since a is positive term. is ultimately suggests that the steady state solution E * of system (7) depends on R 0 and setting Q(C) � 0 yields It is obvious to notice that if the term under the square root is negative (i.e., b 2 < 4ac), then (1) the above quadratic equation has no solution, and (2) there is no positive endemic equilibrium for system (7). Hence, there is a unique endemic equilibrium E * in the biological feasible region Ω since b < 0 if R 02 > R 01 > 1.

Local Sensitivity Analysis
In this section, we present the sensitivity analysis of the proposed model (7). It measures the relative contribution of each model parameter which is responsible for the transmission and prevalence of disease. Since the initial disease transmission is directly related to R 0 , then we perform a sensitivity analysis on R 0 to identify the most critical parameters that assist us to break the spread of the disease. We use the normalized forward sensitivity index to measure the relative change of the model parameter to the disease control and spreading. Definition 1. A normalized forward local sensitivity index of R 0 of system (7), depending on the differentiability with respect to a model parameter q, is given by By applying equation (73) we calculate the sensitivity analysis of R 01 and R 02 with respect to the model parameters. For instance, the sensitivity indices of R 01 and R 02 with respect to β 0 are given by

Journal of Mathematics
e sensitivity indices of the remaining basic model parameters evaluated at the parameter's value are presented in Table 1. Table 1 presents the sensitivity indices of each model parameter with respect to the basic reproduction numbers R 01 and R 02 . From observation of Table 1, we noticed some parameters have positive sign and some other parameters have negative sign. is helps us to give the biological interpretation of each model parameter in R 01 and R 02 . Parameters with positive sign have positive impact on R 01 and R 02 . On the contrary, parameters with negative sign have negative impact on R 01 and R 02 . For instances, parameters such as β 0 , β 1 , Λ, and c have positive signs and have direct relationship with R 01 or R 02 . Biologically, this means the increase (or decrease) of the parameter's value automatically increases (or decreases) R 01 and R 02 . Likewise, parameters such as σ, μ, η, μ 1 , δ, q, and μ 2 have negative signs and are inversely related to the basic reproduction numbers.
is also mean that the increase (or decrease) of the parameter's value directly decreases (or increases) R 01 and R 02 . We observe that the sensitivity indices enable us to determine the various factors of disease transmission and its control. ese indices also allow us to find the most essential parameters that strongly affect the basic reproduction numbers R 01 and R 02 . For example, if one can increase the parameter β 0 by 10%, then R 01 will increase by 10% and R 02 will also increase by 4.81%. On the other hand, if one can increase the parameter σ by 10%, so R 01 and R 02 will decrease by 42.98%. e influence of parameters c, β 0 , and β 1 is clearly depicted in Figures 2(a) and 2(b) as discussed above. If we increase the values of c and β 0 only by keeping the value of other parameters constant, then the basic reproduction number R 0 will increase. is implies that the parameters c and β 0 are directly related to R 02 . Figure 3 shows the diagrammatic representation of sensitivity indices of basic model parameters with their relative contribution on liver cirrhosis management. In Figure 3(b), the impact of β 1 is stronger than the impact of β 0 on R 02 while β 1 has zero contribution on R 01 .

Simulation of the Liver Cirrhosis Model
We employed MATLAB programming of ODE45 solver built-in function to carry out the simulation of the proposed model (7). For the purpose of simulation, we considered a set of positive initial data 100, 40, 20, and 10 for the states in equation (7), S(t), I(t), C(t), R(t), respectively. We assumed a period of 0-60 years as time interval. e values of the parameters are given in Table 2. e majority of the parameter's value are taken from published journals and the remaining parameter's values are assumed in biologically feasible ways. e plots in Figure 4 represent the dynamic behavior of susceptible, acute infection, liver cirrhotic, and recovered individuals. It can be observed that the susceptible population decrease while the acute infection and liver cirrhotic individuals increase for over 15 years and start falling sharply to zero afterward. In both figures, all trajectories are converging to (0.8, 0, 0) when large amount of alcohol and low amount of alcohol are consumed, which verifies the local stability of DFE of system (7). Figure 5 shows the impact of alcohol consumption on the progression dynamics of liver cirrhosis. As clearly seen from Figure 5(a) alcohol abuse in an HBV infected person accelerates the progression of liver cirrhosis. e number of acute infection individuals in Figure 5(a) and the number of liver cirrhotic individuals in Figure 5(b) increase for the first few years and then return to zero.
In Figures 6(a) and 6(b), the simulation result for the variation of the mean transmission rate is presented. Maximization of this rate resulted in maximization of acute infection and liver cirrhotic individuals. One has to minimize the increment rate of alcohol consumption parallel to the minimization policy for minimizing the transmission rate. e plot in Figure 7 is a demonstration showing the variation in the parameters $\beta_{0}$ and $\beta_{1}$ which yield tremendous change in the number of acute infection and liver cirrhotic individuals. It shows that the increase in the mean transmission rate increases the liver cirrhotic population. Figure 8 reveals chronic liver progression is directly related to the progression rates. It indicates that if we keep the other model parameters fixed and vary the progression rate alone, we apparently observe the reduction of acute infection and the increment of liver cirrhotic individuals. Figure 9 demonstrates the the phase portrait of the state variables in the model. e number of acute infection individuals increases sharply for the first years whenever the number of susceptible population increases as depicted in Figure 9(a). Likewise, Figure 9(b) shows the number of liver cirrhotic individuals increases when the number of acutely infected population increases and after a while it experiences a recyclic manner. Figure 9(d) indicates the change in liver cirrhotic individuals when we vary alcohol consumption over a period of 20 years. It rises for the first few years and starts falling after a certain period of time.

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Conclusion
We proposed a deterministic epidemic model to study the combined effect of HBV infection and heavy alcohol consumption on the dynamics of liver cirrhosis progression along with a logistic model to describe the alcohol consumption variation over a period of time. e basic mathematical properties are presented to elaborate the biological feasibility of the proposed model. We analyzed the basic reproduction number, local and global stabilities of disease free-equilibrium, and endemic equilibrium. e local sensitivity indices are employed in order to investigate the impact of various parameters in the proposed model of liver cirrhosis transmission dynamics. We proved the local stability using the Jacobian matrix approach and the global stability of the two equilibria using the Lyapunov function approach. We established and proved the existence of forward and backward bifurcations with the help of central manifold theory. Finally, large scale of numerical simulations was performed for the proposed model and verified the analytical work. e numerical simulation reveals the impacts of the combination of HBV infection and heavy alcohol consumption and it accelerates the progression of liver cirrhosis unless alcohol intake reduction policy is not implemented. e authors recommend that any interested researcher can apply optimal control strategy in order to minimize the risk of alcohol consumption in chronic HBV carriers to eliminate liver cirrhosis in the population.

Data Availability
e data used to support the findings of this study are available from the corresponding author upon request.