Effect of Time Delay and Antibodies on HCV Dynamics with Cure Rate and Two Routes of Infection ()
1. Introduction
Hepatitis C virus is considered one of the dangerous human viruses that infects the liver and causes the lever cirrhosis. Mathematical modeling and analysis of within-host HCV dynamics have been studied by many authors (see e.g. [1] - [12] ). These works can help researchers for better understanding the HCV dynamical behavior and providing new suggestions for clinical treatment. Immune response plays an important role in controlling the dynamics of several viruses (see e.g. [13] [14] [15] [16] [17] ). Cytotoxic T Lymphocyte (CTL) and antibodies play a central role of immune response. CTL cells attack and kill the infected cells. The B cell produces antibodies to neutralize the viruses. Mathematical models of HCV dynamics with antibody immune response have been proposed in [18] [19] [20] . The models presented in [18] [19] [20] assume that an uninfected hepatocyte becomes infected by contacting with HCV (virus-to-cell transmission). It has been reported in [21] [22] [23] that the HCV can also spread by cell-to-cell transmission.
The “cure” of infected cells has been considered in the virus dynamics models in several works (see e.g. [24] - [39] ). In [40] , both cure and cell-to-cell transmissions have been considered in the virus dynamics model, but without taking the immune response into account. In a very recent paper, Pan and Chakrabarty [41] have proposed the following mathematical model of HCV dynamics which incorporates 1) both virus-to-cell and cell-to-cell transmissions, 2) cure of infected hepatocytes, and 3) antibody immune response:
(1)
(2)
(3)
(4)
where, s, y, p and z represent the concentration of uninfected hepatocytes, infected hepatocytes, HCV particles and antibodies, respectively. The uninfected hepatocytes are generated at a constant rate β, die at rate
, where
is the natural death rate constant. The infection rate due to both virus-to-cell and cell-to-cell transmissions is given by
, where
and
are constants. The infected hepatocytes die at rate εy and cure at rate ρy, where ε and ρ are constants. Constant m is the generation rate of the HCV from infected hepatocytes. Antibodies attack the HCV at rate
, proliferate at rate
and die at rate μz, where q, r and μ are constants.
It is assumed in model (1)-(4) that, the hepatocytes can produce HCV particles once they are contacted by HCV or infected cells. However, there is a time period from the moment of the uninfected hepatocytes that are contacted by the HCV or infected cells and the moment of producing new active HCV particles [10] [11] .
The aim of this paper is to study the qualitative behavior of an HCV dynamics model with antibody immune response. We have incorporated distributed time delay and both virus-to-cell and cell-to-cell transmissions. We derive two threshold parameters and establish the global stability of the three steady states of the model using Lyapunov method.
2. The Model
We propose the following HCV dynamics model with distributed time delay:
(5)
(6)
(7)
(8)
We assume that, the HCV or infected cell contacts an uninfected hepatocyte at time
, the cell becomes infected at time t, where
is a distributed parameter over the time interval
. The factors
represents the probability of surviving the hepatocyte during the time delay period, where
is a constant.
is a probability distribution function satisfying
and
where
and h are positive constants. Let us denote
and
, thus
. Let the initial conditions for system (5)-(8) be given as:
(9)
where C is the Banach space of continuous functions mapping the interval
into
. Then, the uniqueness of the solution for
is guaranteed [42] .
2.1. Basic Properties
In this subsection, we investigate the nonnegativity and boundedness of solutions.
Proposition 1. The solutions of system (5)-(8) with the initial states (9) are nonnegative and ultimately bounded.
Proof. From Equation (5) we have
. Hence,
for all
. Moreover, for all
we have
By recursive argument we get
,
, and
, for all
.
Next, we establish the boundedness of the model’s solutions. The nonnegativity of the model’s solution implies that
We let
, then
where
. Hence
, if
where
. It follows that
and
if
. Moreover, let
, then
where
. It follows that,
, where
. Since
and
, then
and
, where
. Therefore,
and
are ultimately bounded. ,
According to Proposition 1, we can show that the region
is positively invariant with respect to system (5)-(8).
2.2. The Steady States and Threshold Parameters
Lemma 1. For system (5)-(8) there exist two threshold parameters
, and
, such that
1) if
, then there exists only one steady state
,
2) if
, then there exist only two steady states
and
,
3) if
and
, then there exist three steady states
,
and
.
Proof. Let
be any steady state satisfying
(10)
(11)
(12)
(13)
We find that system (10)-(13) admits three steady states.
1) Infection-free steady state
, where
.
2) Chronic-infection steady state without immune response
, where
Clearly
exists if
Let us define
In terms of
, we can write the steady state components for
as:
3) Chronic-infection steady state with humoral immune
, where
(14)
where
(15)
We note that
exists when
. Now we define
(16)
Then
. We define the basic reproduction number for the
humoral immune response
which comes from the limiting (linearized) z-dynamics near
as:
Lemma 2 1) if
, then
,
2) if
, then
,
3) if
then
.
Proof. 1) Let
, then from Equation (16) we have
, and then using Equation (14) we get
that leads to
Using Equation (15), we can get
then
then
. Similarly, one can proof 2) and 3) ,.
3. Global Stability
The following theorems investigate the global stability of the steady states of system (5)-(8). Let us define the function
as
. Denote
.
Theorem 1. Suppose that
, then the infection-free steady state
is globally asymptotically stable (GAS).
Proof. Constructing a Lyapunov functional
We calculate
along the solutions of model (5)-(8) as:
(17)
Collecting terms of Equation (17) and using
we obtain
(18)
We note that
Therefore
Since
, then
for all
. Moreover
if and only if
. Let
and
be the largest invariant subset of
. The solution of system (5)-(8) tend to
. For each element of
we have
, then
and Equation (6) we get
Then
. It follows that
contains a single point that is
. Appling LaSalle’s invariance principle (LIP), we get that is GAS.
Theorem 2. Suppose that, then is GAS.
Proof. Let us define a function as:
Calculating along the trajectories of system (5)-(8), we get
(19)
Collecting terms of Equation (19), we get
Applying condition of equilibrum:
we get
thus
We note that
Then
(20)
Consider the following equalities
(21)
Simplify Equation (20) and let, in Equation(21) we get
(22)
Equation (22) can be rewrite as:
(23)
We note that
From Lemma 2 we have, then, for all and, where if and only if and. Thus, the global asymptotic stability of follows from LIP when, and. ,
Theorem 3. Suppose that and, then is GAS .
Proof. Define a function as:
Calculating as:
(24)
Collecting terms of Equation (24) and applying the equilibrium conditions for:
we get
We note that
Using equalities (21) in case, we get
(25)
Equation (25) can be simplified as:
We note that, when, where occurs at. The global asymptotic stability of follows from LIP. ,
4. Numerical Simulations
This section is devoted to performing some numerical simulations for model (5)-(8). Let us choose
where is the Dirac delta function and is constant. Let, then we obtain
Moreover,
Hence, model (5)-(8), becomes
(26)
(27)
(28)
(29)
For model (26)-(29), the threshold parameters are given by:
(30)
where y2 is given by Equation (14). Model (26)-(29) will be solved using the values of the parameters listed in Table 1.
Now we investigate our theoretical results given in Theorem 1-3. We consider the following two cases:
Case I: Effect of α, μ and h on the asymptotic behaviors of steady states:
In this case, we have chosen three different initial conditions for model (26)-(29) as follows:
Initial-1:, (Solid lines in the figures)
Initial-2:, (Dashed lines in the figures)
Initial-3:,. (Dotted lines in the figures)
Further, we fix the value of and we use three sets of parameters and r to investigate the following five scenarios.
Scenario 1: and. For this set of parameters, we have and. From Figure 1 it can be seen that the solutions with all initial conditions converge to. This means that according to Theorem 1 is GAS. In this case the healthy state will be reached and the HCV particles will be removed.
Table 1. Some parameters and their values of model (26)-(29).
Scenario 2: and. With such choice we get,
and exists with
. This result confirms Lemma 1. Theorem 2 states that, is GAS and this is shown in Figure 2. This case represents the
persistence of the HCV particles but with inactive antibody immune response.
Scenario 3: and. Then, we calculate, and. Lemma 1 and Theorem 3 establish that, exists and it is globally asymptotically stable. From Figure 3, we find that the numerical results agree with the theoretical one presented in Theorem 3. For all initial conditions the states reach the steady state . This case corresponds to a chronic HCV infection with active antibody immune response.
Case II: Effect of the time delays on the free HCV particles dynamics:
Let us take the initial conditions (Initial-2). We choose the values and. we assume that. Table 2 contains the values of all threshold parameters and equilibria of system (26)-(29) with different values of.
From Table 2 we can see that, the values of, and are decreased as is increased. Moreover, has a significant effect on the stability of steady states of the system. Table 2 and Figure 4 show that a high value of
Table 2. The values of the threshold parameters and the equilibria of system (26)-(29) with different values of.
decreases the concentration of infected hepatocytes, free HCV particles, antibodies, and increases the population of uninfected hepatocytes. Therefore, the steady states of the system will eventually stabilized around the healthy state.