# A Fractional Order Model for Viral Infection with Cure of Infected Cells and Humoral Immunity.

1. Introduction

The immune response plays an important role to control the dynamics of viral infections such as human immunodeficiency virus (HIV), hepatitis B virus (HBV), hepatitis C virus (HCV), and human T-cell leukemia virus (HTLV). Therefore, many mathematical models have been developed to incorporate the role of immune response in viral infections. Some of these models considered the cellular immune response mediated by cytotoxic T lymphocytes (CTL) cells that attack and kill the infected cells [1-5] and the others considered the humoral immune response based on the antibodies which are produced by the B-cells and are programmed to neutralize the viruses [6-11]. However, all these models have been formulated by using ordinary differential equations (ODEs) in which the memory effect is neglected while the immune response involves memory [12,13].

Fractional derivative is a generalization of integer derivative and it is a suitable tool to model real phenomena with memory which exists in most biological systems [14-16]. The fractional derivative is a nonlocal operator in contrast to integer derivative. This means that if we want to compute the fractional derivative at some point t = t1, it is necessary to take into account the entire history from the starting point t = t0 up to the point t = t1. For these reasons, modeling some real process by using fractional derivative has drawn attention of several authors in various fields [17-22]. In biology, it has been shown that the fractional derivative is useful to analyse the rheological proprieties of cells . Furthermore, it has been deduced that the membranes of cells of biological organism have fractional order electrical conductance . Recently, much works have been done on modeling the dynamics of viral infections with FDEs [25-31]. These works ignored the impact of the immune response and the majority of them deal only with the local stability.

In some viral infections, the humoral immune response is more effective than cellular immune response . For this reason, we improve the above ODE and FDE models by proposing a new fractional order model that describes the interactions between susceptible host cells, viral particles, and the humoral immune response mediated by the antibodies; that is,

[mathematical expression not reproducible], (1)

where x(t), l(t), y(t), v(t), and w(t) are the concentrations of susceptible host cells, latently infected cells (infected cells which are not yet able to produce virions), productive infected cells, free virus particles, and antibodies at time t, respectively. Susceptible host cells are assumed to be produced at a constant rate [lambda], die at the rate dx, and become infected by virus at the rate f(x, v)v. Latently infected cells die at the rate ml and return to the uninfected state by loss of all covalently closed circular DNA (cccDNA) from their nucleus at the rate [rho]l. Productive infected cells are produced from latently infected cells at the rate [gamma]l and die at the rate ay. Free virus particles are produced from productive infected cells at the rate ky, cleared at the rate [mu]v, and are neutralized by antibodies at the rate qvw. Antibodies are activated against virus at the rate gvw and die at the rate hw.

In system (1), [D.sup.[alpha]] represents the Caputo fractional derivative of order a defined for an arbitrary function [phi] by

[D.sup.[alpha]] [phi](t) = 1/[GAMMA](1 - [alpha]) [[integral].sup.t.sub.0] [phi]'(u)/[(t - u).sup.[alpha]] du, (2)

with 0 < [alpha] [less than or equal to] 1 . Further, the infection transmission processin(1) is modeled by Hattaf-Yousfi functional response  which was recently used in [35, 36] and has the form f(x, v) = [beta]x/([[alpha].sub.0] + [[alpha].sub.1] x + [[alpha].sub.2] v + [[alpha].sub.3] xv),where [[alpha].sub.0], [[alpha].sub.1], [[alpha].sub.2], [[alpha].sub.3] [greater than or equal to] 0 are the saturation factors measuring the psychological or inhibitory effect and [beta] > 0 is the infection rate. In addition, this functional response generalizes many common types existing in the literature such as the specific functional response proposed by Hattaf et al. in  and used in [2, 31] when [[alpha].sub.0] = 1; the Crowley-Martin functional response introduced in  and used in  when [[alpha].sub.0] = 1 and [[alpha].sub.3] = [[alpha].sub.1][[alpha].sub.2]; and the Beddington-DeAngelis functional response proposed in [40, 41] and used in [3, 4,10] when [[alpha].sub.0] = 1 and [[alpha].sub.3] = 0. Also, the Hattaf-Yousfi functional response is reduced to the saturated incidence rate used in  when [[alpha].sub.0] = 1 and [[alpha].sub.1] = [[alpha].sub.3] =0 and the standard incidence function used in  when [[alpha].sub.0] = [[alpha].sub.3] =0 and [[alpha].sub.1] = [[alpha].sub.2] =1, and it was simplified to the bilinear incidence rate used in [5, 6] when [[alpha].sub.0] = 1 and [[alpha].sub.1] = [[alpha].sub.2] = [[alpha].sub.3] = 0.

On the other hand, system (1) becomes a model with ODEs when [alpha] = 1, which improves and generalizes the ODE model with bilinear incidence rate , the ODE model with saturated incidence rate , and the ODE model with specific functional response .

The rest of the paper is organized as follows. The next section deals with some basic proprieties of the solutions and the existence of equilibria. The global stability of equilibria is established in Section 3. To verify our theoretical results, we provide some numerical simulations in Section 4, and we conclude in Section 5.

2. Basic Properties and Equilibria

In this section, we will show that our model is well-posed and we discuss the existence of equilibria.

Since system (1) describes the evolution of cells, then we need to prove that the cell numbers should remain nonnegative and bounded. For biological considerations, we assume that the initial conditions of (1) satisfy

[mathematical expression not reproducible]. (3)

Then we have the following result.

Theorem 1. Assume that the initial conditions satisfy (3). Then there exists a unique solution of system (1) defined on [0, +[infinity]). Moreover, this solution remains nonnegative and bounded for all t [greater than or equal to] 0.

Proof. First, system (1) can be written as follows:

[D.sup.[alpha]]X(t) = F(X), (4)

where

[mathematical expression not reproducible]. (5)

It is important to note that when [alpha] = 1, (4) becomes a system with ODEs. In this case, we refer the reader to  for the existence of solutions and to the works [46-50] for the stability of equilibria. In the case of FDEs, we will use Lemma

2.4 in  to prove the existence and uniqueness of solutions. Hence, we put

[mathematical expression not reproducible] (6)

We discuss four cases:

(i) If [[alpha].sub.0] [not equal to] 0, F(X) can be formulated as follows:

F(X) = [zeta] + AX [[alpha].sub.0]/ [[alpha].sub.0] + [[alpha].sub.1]x + [[alpha].sub.2]v + [[alpha].sub.3]xv v[B.sub.0]X + vCX, (7)

where

[mathematical expression not reproducible]. (8)

Hence,

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (9)

(ii) If [[alpha].sub.1] = 0, we can write F(X) in the form

F (X) = [zeta] + AX [[alpha].sub.1]x/ [[alpha].sub.0] + [[alpha].sub.1]x + [[alpha].sub.2]v + [[alpha].sub.3]xv v[B.sub.1]X + vCX, (8)

where

[mathematical expression not reproducible]. (11)

Moreover, we get

[mathematical expression not reproducible]. (12)

(iii) If [[alpha].sub.2] = 0, we have

F(X) = [zeta] + AX [[alpha].sub.2]v/ [[alpha].sub.0] + [[alpha].sub.1]x + [[alpha].sub.2]v + [[alpha].sub.3]xv v[B.sub.2]X + vCX, (13)

where

[mathematical expression not reproducible]. (14)

Further, we obtain

[mathematical expression not reproducible]. (15)

(iv) If [[alpha].sub.3] = 0, we have

F(X) = [zeta] + AX [[alpha].sub.3]xv/ [[alpha].sub.0] + [[alpha].sub.1]x + [[alpha].sub.2]v + [[alpha].sub.3]xv v[B.sub.3] + vCX, (14)

where

[mathematical expression not reproducible]. (17)

Then

[mathematical expression not reproducible]. (18)

Hence, the conditions of Lemma 2.4 in  are verified. Then system (1) has a unique solution on [0, +[infinity]). Now, we show the nonnegativity of solutions. By (1), we have

[mathematical expression not reproducible]. (19)

As in [31, Theorem 2.7], we deduce that the solution of (1) is nonnegative.

Finally, we prove the boundedness of solutions. We define the function

T (t) = x (t) + l(t) + y (t) + a/2k v(t) + aq/2kg w(t). (20)

Then, we have

[mathematical expression not reproducible], (21)

where [delta] = min{d, m, a/2, [mu], h}. Thus, we obtain

T(t) [less than or equal to] T(0) [E.sub.[alpha]] (-[delta][t.sup.[alpha]]) + [lambda]/[delta] [1 - [E.sub.[alpha]] (-[delta][t.sup.[alpha]])]. (22)

Since 0 [less than or equal to] [E.sub.[alpha]] (-[delta][t.sup.[alpha]]) [less than or equal to] 1, we get

T(t) [less than or equal to] T(0) + [lambda]/[delta]. (23)

This completes the proof.

Now, we discuss the existence of equilibria. It is clear that system (1) has always an infection-free equilibrium [E.sub.0]([lambda]/d, 0,0, 0,0). Then the basic reproduction number of (1) is as follows:

[R.sub.0] = k[beta][lambda][gamma]/ a[mu](m + [rho] + [gamma]) (d[[alpha].sub.0] + X[[alpha].sub.1]).

To find the other equilibria, we solve the following system:

[lambda] - dx - f (x, v) v + [rho]l = 0, (25)

f(x, v) v -(m + [rho] + [gamma])l = 0, (26)

[gamma]l - ay = 0, (27)

ky - [mu]v - qvw = 0, (28)

gvw - hw = 0. (29)

From (29), we get w = 0 or v = h/g. Then we discuss two cases.

If w = 0, by (25)-(28), we have I = ([lambda] - dx)/(m + [gamma]), y = [gamma]([lambda] - dx)/a(m + [gamma]), v = k[gamma]([lambda] - dx)/a[mu](m + [gamma]), and

f(x k[gamma] ([lambda] - dx)/a[mu](m + [gamma])) = a[mu](m + [rho] + [gamma])/k[gamma]. (30)

Since I [greater than or equal to] 0, y [greater than or equal to] 0, and v [greater than or equal to] 0, then x [less than or equal to] [lambda]/d. Consequently, there is no equilibrium when x > [lambda]/d.

We define the function [h.sub.1] on [0, [lambda]/d] by

[h.sub.1](x) = f(x k[gamma] ([lambda] - dx)/a[mu](m + [gamma])) - a[mu](m + [rho] + [gamma])/k[gamma] (31)

We have [mathematical expression not reproducible].

Hence if [R.sub.0] > 1, (30) has a unique root [x.sub.1] [member of] (0, [lambda]/d). As a result, when [R.sub.0] > 1 there exists an equilibrium [mathematical expression not reproducible].

If [mathematical expression not reproducible], and

f(x, h/g) = g(m + [rho] + [gamma])/h(m + [gamma]) ([lambda] - dx). (32)

Since l [less than or equal to] 0, y [less than or equal to] 0, and w [less than or equal to] 0, we have x [greater than or equal to] [lambda]/d - ah[mu](m + [gamma])/dkg[gamma]. Hence, there is no equilibrium if x > [lambda]/d-ah[mu](m + [gamma])/dkg[gamma].

We define the function h2 on [0, [lambda]/d - ah[mu](m + [gamma]) / dkg[gamma]] by

[h.sub.2](x) = f(x, h/g) - g(m + [rho] + [gamma])/h(m + [gamma]) ([lambda] - dx). (33)

We have [mathematical expression not reproducible].

Let us introduce the reproduction number for humoral immunity as follows:

[R.sub.1] = g[v.sub.1]/h, (34)

which 1/h denotes the average life expectancy of antibodies and [v.sub.1] is the number of free viruses at [E.sub.1]. For the biological significance, [R.sub.1] represents the average number of the antibodies activated by virus.

If [R.sub.1] < 1, we have [x.sub.1] > [lambda]/d - ah[mu](m + [gamma])/dkg[gamma] and

[h.sub.2]([lambda]/d - ah[mu](m +[gamma])/dkg[gamma]]) < [h.sub.1]([x.sub.1]). (35)

Therefore, there is no equilibrium when [R.sub.1] < 1.

If [R.sub.1] < 1, then [x.sub.1] < [lambda]/d - ah[mu](m + [gamma])/dkg[gamma] and

[h.sub.2]([lambda]/d - ah[mu](m +[gamma])/dkg[gamma]]) > [h.sub.1]([x.sub.1]). (36)

In this case, (32) has one root [x.sub.2] [member of] (0, [lambda]/d-ah[mu](m + [gamma])/dkg[gamma]). Consequently, when [R.sub.1] > 1, there exists an equilibrium [mathematical expression not reproducible].

We summarize the above discussions in the following theorem.

Theorem 2.

(i) If [R.sub.0] [less than or equal to] 1, then system (1) has one infection-free equilibrium of the form [E.sub.0]([x.sub.0], 0, 0, 0, 0), where [x.sub.0] = [lambda]/d.

(ii) If [R.sub.0] > 1, then system (1) has an infection equilibrium without humoral immunity of the form [mathematical expression not reproducible].

(iii) If [R.sub.1] > 1, then system (1) has an infection equilibrium with humoral immunity of the form [mathematical expression not reproducible].

3. Global Stability of Equilibria

In this section, we focus on the global stability of equilibria.

Theorem 3. If [R.sub.0] [less than or equal to] 1, then the infection-free equilibrium [E.sub.0] is globally asymptotically stable and it becomes unstable if [R.sub.0] > 1.

Proof. The proof of the first part of this theorem is based on the construction of a suitable Lyapunov functional that satisfies the conditions given in [51, Lemma 4.6]. Hence, we define a Lyapunov functional as follows:

[mathematical expression not reproducible], (37)

where [PHI](%) = x - 1 - ln(x) for x > 0. It is not hard to show that the functional [L.sub.0] is nonnegative. In fact, the function [PHI] has a global minimum at x = 1. Consequently, [PHI](%) [greater than or equal to] 0 for all x > 0.

Calculating the fractional derivative of [L.sub.0] (t) along solutions of system (1) and using the results in , we get

[mathematical expression not reproducible]. (38)

Using [lambda] = [dx.sub.0], we obtain

[mathematical expression not reproducible] (39)

Hence if [R.sub.0] < 1, then [D.sup.[alpha]][L.sub.0](t) < 0. In addition, the equality holds if and only if x = [x.sub.0],l = 0, y = 0,w = 0, and ([R.sub.0] - 1)v = 0. If [R.sub.0] < 1, then v = 0. If [R.sub.0] = 1, from (1), we get f([x.sub.0], v)v = 0 which implies that v = 0. Consequently, the largest invariant set of {(x, l, y, v, w) [member of] [R.sup.5.sub.+] : [D.sup.[alpha]][L.sub.0](t) = 0} is the singleton {[E.sub.0]}. Therefore, by the LaSalle's invariance principle , [E.sub.0] is globally asymptotically stable.

The proof of the instability of [E.sub.0] is based on the computation of the Jacobean matrix of system (1) and the results presented in [53-55]. The Jacobean matrix of (1) at any equilibrium E(x, l, y, v, w) is given by

[mathematical expression not reproducible]. (40)

We recall that E is locally asymptotically stable if the all eigenvalues [[xi].sub.i] of (40) satisfy the following condition [53-55]:

[absolute value of arg([[xi].sub.i])] > [alpha][pi]/2. (41)

From (40), the characteristic equation at [E.sub.0] is given as follows:

(d + [xi])(h + [xi])[g.sub.0] ([xi]) = 0, (42)

where

[mathematical expression not reproducible]. (43)

Obviously, (42) has the roots [[xi].sub.1] = - d and [[xi].sub.2] = -h. If [R.sub.0] > 1, we have [g.sub.0](0) = a[mu](m + [rho] + [gamma])(1 - [R.sub.0]) < 0 and [lim].sub.[xi][right arrow]+[infinity]] [g.sub.0] = +[infinity]. Then, there exists [[xi].sup.*] > 0 satisfying [g.sub.0]([[xi].sup.*]) = 0. In addition, we have [absolute value of arg([[xi].sup.*])] = 0 < [alpha][pi]/2. Consequently, when [R.sub.0] > 1, ii0 is unstable. ?

Theorem 4.

(i) The infection equilibrium without humoral immunity [E.sub.1] is globally asymptotically stable if [R.sub.0] > 1, [R.sub.1] [less than or equal to] 1, and

[mathematical expression not reproducible]. (44)

(ii) When [R.sub.1] > 1, [E.sub.1] is unstable.

Proof. Define a Lyapunov functional as follows:

[mathematical expression not reproducible]. (45)

Calculating the fractional derivative of [L.sub.1](t), we get

[mathematical expression not reproducible]. (46)

Using [mathematical expression not reproducible], we obtain

[mathematical expression not reproducible]. (47)

Hence,

[mathematical expression not reproducible]. (48)

Using the arithmetic-geometric inequality, we have

[mathematical expression not reproducible]. (49)

Since [R.sub.1] [less than or equal to] 1, we have [D.sup.[alpha]][L.sub.1](t) [less than or equal to] 0 if > [dx.sub.1] [greater than or equal to] [rho][l.sub.1]. It is easy to see that this condition is equivalent to (44). Furthermore, [D.sup.[alpha]][L.sub.1](t) = 0 if and only if x = [x.sub.1], l = [l.sub.1], y = [y.sub.1] v = [v.sub.1], and ([R.sub.1] - 1)w = 0. We discuss two cases: If [R.sub.1] < 1, then w = 0. If [R.sub.1] = 1, from(1), we get [D.sup.[alpha]][v.sub.1] = 0 = k[y.sub.1] - [mu][v.sub.1]w, - q[v.sub.1]w, and then w = 0. Hence, the largest invariant set of ((x, l, y, v, w) [member of] [R.sup.5.sub.+] : [D.sup.[alpha]][L.sub.1] (t) = 0} is the singleton {[L.sub.1]}. By the LaSalle's invariance principle, [E.sub.1] is globally asymptotically stable.

At [E.sub.1], the characteristic equation of (40) is given as follows:

(g[v.sub.1] - h - [xi]) [g.sub.1] (xi) = 0, (50)

where

[mathematical expression not reproducible]. (51)

We can easily see that (50) has the root [[xi].sub.1] = g[v.sub.1] - h. Then, when [R.sub.1] > 1, we have [[xi].sub.1] > 0. In this case, [E.sub.1] is unstable.

Theorem 5. The infection equilibrium with humoral immunity [E.sub.2] is globally asymptotically stable if [R.sub.1] > 1 and

f[beta]h [less than or equal to] d (m + [rho] + [gamma]) ([[alpha].sub.0]g + [[alpha].sub.2]h) + [rho][lambda] ([[alpha].sub.1]g + [[alpha].sub.3]h). (52)

Proof. Consider the following Lyapunov functional:

[mathematical expression not reproducible] (53)

Computing the fractional derivative of [L.sub.2](t) and using [mathematical expression not reproducible], we get

[mathematical expression not reproducible]. (54)

From (49), we have [D.sup.[alpha]][L.sub.2](t) [less than or equal to] 0 when d[x.sub.2] > [rho][l.sub.2]. This condition is equivalent to (52). In addition, [D.sup.[alpha]][L.sub.1](t) = 0 if % = [x.sub.2],l = [l.sub.2], y = [y.sub.2], and v = [v.sub.2]. Further, [D.sup.[alpha]][v.sub.2] = 0 = k[y.sub.2] - [mu][v.sub.2] - q[v.sub.2]w; then w = [w.sub.2]. Consequently, the largest invariant set of {(x, l, y, v, w) [member of] [R.sup.5.sub.+] : [D.sup.[alpha]][L.sub.2](t) = 0} is the singleton {[E.sub.2]}. By the LaSalle's invariance principle, [E.sub.2] is globally asymptotically stable.

It is important to note that when [rho] is sufficiently small or y is sufficiently large, the two conditions (44) and (52) are satisfied. Then, we have the following corollary.

Corollary 6. Assume that [R.sub.0] > 1. When [rho] is sufficiently small or [gamma] is sufficiently large, then we have the following:

(i) The infection equilibrium without humoral immunity [E.sub.1] is globally asymptotically stable if [R.sub.1] [less than or equal to] 1.

(ii) The infection equilibrium with humoral immunity [E.sub.2] is globally asymptotically stable if [R.sub.1] > 1.

4. Numerical Simulations

In this section, we validate our theoretical results to HIV infection. Firstly, we take the parameter values as shown in Table 1.

By calculation, we have [R.sub.0] = 0.4274 [less than or equal to] 1. Then system (1) has an infection-free equilibrium [E.sub.0] (719.4245,0, 0,0,0). By Theorem 3, the solution of (1) converges to [E.sub.0] (see Figure 1). Consequently, the virus is cleared and the infection dies out.

Now, we choose [beta] = 0.0012 and we keep the other parameter values. Hence, we obtain [R.sub.0] = 2.137, [R.sub.1] = 0.8334, and

[mathematical expression not reproducible]. (55)

Consequently, condition (44) is satisfied. Therefore, the infection equilibrium without humoral immunity [E.sub.1] (176.6853, 168.7712, 6.2508, 1666.9,0) is globally asymptotically stable. Figure 2 demonstrates this result. In this case, the infection becomes chronic.

Next, we take g = 0.0004 and do not change the other parameter values. In this case, we have [R.sub.1] = 3.3338, [rho][beta]h = 0.0000024, and d(m + [rho] + [gamma])([[alpha].sub.0]g + [[alpha].sub.2]h) + [rho][lambda]([[alpha].sub.1]g + [[alpha].sub.3]h) = 0.000006. Hence, condition (52) is satisfied. Consequently, system (1) has an infection equilibrium with humoral immunity [E.sub.2](423.4261, 92.0442, 3.4090, 500, 245.4473) which is globally asymptotically stable. Figure 3 illustrates this result. We can observe that the activation of the humoral immune response increases the healthy cells and decreases the productive infected cells and viral load to a lower levels but it is not able to eradicate the infection.

5. Conclusion

In the present paper, we have studied the dynamics of a viral infection model by taking into account the memory effect represented by the Caputo fractional derivative and the humoral immunity. We have proved that the solutions of the model are nonnegative and bounded which assure the well-posedness. We have shown that the proposed model has three infection equilibriums, namely, the infection-free equilibrium [E.sub.0], the infection equilibrium without humoral immunity [E.sub.1], and the infection equilibrium with humoral immunity [E.sub.2]. By constructing suitable Lyapunov functionals, the global stability of these equilibria is fully determined by two threshold parameters [R.SUB.Q] and [R.sub.1]. More precisely, when [R.sub.0] [less than or equal to] 1, [E.sub.0] is globally asymptotically stable, whereas if [R.sub.0] > 1, it becomes unstable and another equilibrium point appears, that is, [E.sub.1], which is globally asymptotically stable whenever [R.sub.1] [less than or equal to] 1 and condition (44) is satisfied. In the case that [R.sub.1] > 1, [E.sub.1] becomes unstable and there exists another equilibrium point [E.sub.2] which is globally asymptotically stable when condition (52) is satisfied. In addition, we remarked that when [rho] is sufficiently small or [gamma] is sufficiently large, conditions (44) and (52) are verified, and then the global stability of [E.sub.1] and [E.sub.2] is characterized only by [R.SUB.Q] and [R.sub.1].

From our theoretical and numerical results, we deduce that the order of the fractional derivative a has no effect on the dynamics of the model. However, when the value of [alpha] decreases (long memory), the solutions of our model converge rapidly to the steady states (see Figures 1-3). This behavior can be explained by the memory term 1/[GAMMA](1 - [alpha])[(t - u).sup.[alpha]] included in the fractional derivative which represents the time needed for the interaction between cells and viral particles and the time needed for the activation of humoral immune response. In fact, the knowledge about the infection and the activation of the humoral immune response in an early stage can help us to control the infection.

https://doi.org/10.1155/2018/1019242

Data Availability

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

Conflicts of Interest

The authors declare that they have no conflicts of interest.

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Adnane Boukhouima, (1) Khalid Hattaf (iD), (1,2) and Noura Yousfi (iD) (1)

(1) Laboratory of Analysis, Modeling and Simulation (LAMS), Faculty of Sciences Ben M'sik, Hassan II University, P. O Box 7955 Sidi Othman, Casablanca, Morocco

(2) Centre Regional des Metiers de l'Education et de la Formation (CRMEF), 20340 Derb Ghalef, Casablanca, Morocco

Correspondence should be addressed to Khalid Hattaf; k.hattaf@yahoo.fr

Received 9 September 2018; Accepted 7 November 2018; Published 2 December 2018

Guest Editor: Nurcan B. Savasaneril

Caption: FIGURE 1: Stability of the infection-free equilibrium [E.sub.0].

Caption: FIGURE 2: Stability of the infection equilibrium without humoral immunity [E.sub.1].

Caption: FIGURE 3: Stability of the infection equilibrium with humoral immunity [E.sub.2].
```Table 1: Parameter values of system (1).

parameters           values

[lambda]               10
d                    0.0139
[beta]              0.00024
[rho]                 0.01
m                    0.0347
a                     0.27
[gamma]               0.01
k                     800
[mu]                   3
q                     0.01
[[alpha].sub.3]     0.00001
h                     0.2
g                    0.0001
[[alpha].sub.0]        1
[[alpha].sub.1]       0.1
[[alpha].sub.2]       0.01
```
Title Annotation: Printer friendly Cite/link Email Feedback Research Article Boukhouima, Adnane; Hattaf, Khalid; Yousfi, Noura International Journal of Differential Equations Report Jan 1, 2018 5832 Numerical Solution of Piecewise Constant Delay Systems Based on a Hybrid Framework. Existence of Weak Solutions for Fractional Integrodifferential Equations with Multipoint Boundary Conditions. Differential equations Disease transmission Hepatitis B Host-virus relationships Infection Mathematical models Numerical analysis T cells