Bifurcation and Stability Analysis of HIV Infectious Model with Two Time Delays ()
1. Introduction
The serious infectious problems of the human immunodeficiency virus (HIV) which can attack human immune system to infect human body healthy cells have drawn more attentions in the fields of dynamical investigation. Through its slowly replicating retrovirus process to cause the acquired immunodeficiency syndrom (AIDs) of human health problem, the body becomes gradually very susceptible to opportunistic infections while CD4+ T cells fall below a critical level or oscillate for a long period with the lower level [1] [2] [3]. The most devastating thing is that people have made big efforts in mathematical models to give a deep understanding of the inherent dynamical mechanism of HIV viral infection [4] [5]. Especially, the classic mathematical model of HIV infection dynamics which is originally modeled by Anderson and May and developed subsequently by Nowak and Bangham is well accepted as the basic model(ODEs) to be investigated both by theorists and experimentalists [6] [7] [8].
As is well known, HIV gene expression products can produce toxic substances which indirectly or directly lead to apoptosis of uninfected CD4+ T cells [1] [2] [8]. It is verified in a laboratory experiment that the apoptosis may occur without virus replication while both uninfected cells and infected cells are cultured together [9] [10]. It is suggested that viral proteins associate with uninfected CD4+ T cells can induce an apoptotic signal which induces the death of uninfected CD4+ T cells [11] [12]. People also have made consideration of the interaction relationship acts between infected cells and uninfected T cells via Holling type III functional response, Beddington-Deangelis functional response, and bilinear infection rate or more general nonlinear infection rate as suggested in papers [7] [13] [14] [15].
Accordingly, based on the biological meanings, we develop the following HIV infection model with a viral infection and replication kinetics
(1.1)
where
denote uninfected and infected CD4+T cells respectively, whilst
represents the concentration of virus. The explainition of system (1.1) biologically meaningful lies that virus replication is fractionally positive to the increasing number of infected T cells. In addition, the bilinear incidence rate of uninfected T cells and infected cells induces the increasing number of infected T cells with time period
. The virus replication exaggerates the number of infected T cells through virus replicating time
though the apoptosis of virus is reduced with time
. In comparison with other HIV infection model, system (1.1) is delay differential equations(DDEs) which contains double time delay
and
. With multiple time delay, the dynamics of system is complicated since instability produces period oscillation phenomena. With the attempt to track the period varying and bifurcation phenomena of the limit cycle as varying parameters continuously, the DDE-Biftool is a technology tool of art in the dynamical investigation field [16] [17].
The extending geometrical criterion to work out the eigenvalue problem of DDEs with multiple time delay is recently given in papers [18] [19] [20]. Hopf lines are tracked by varying time delay in parameter space and the transversal condition is also tidily outlined. The state of instability switching phenomenon happening is associated with the characteristic roots with zero real parts appearing in complex plane and limit cycle always arise near the threshold value. With time delay, the quanititive property of the evolution dynamical behavior of system (1.1) is investigated with the estimated value of loss rate k varying.
Periodical oscillation phenomena in every respect of T cells population arise as system equilibrium loses its stability. It is verified that T cells population oscillating in a time period with delayed feedback control of state variable. It is explained that difference estimation of T healthy cells of past history and present time may induce periodically evolutional behavior of system states. In another respect, in some extent, the difference estimation is regarded as disease symptom proof. Therefore, we introduce the delayed feedback control in system (1.1) as the following
(1.2)
Based on the fundamental theory of DDEs, the dynamics of disease model (1.2) are studied as varying time delays. We also apply the geometrical criterion to deduce the quantitative property of system (1.2) to derive the instability condition with k regraded as free parameter. The periodical solution is bifurcating from the threshold value of Hopf bifurcation hence Hopf bifurcation lines are tracked as varying free parameters continuously on the parameter plane. The associated normal form is also computed via using Schmidt dimension reduction scheme combined with center manifold theory [21] [22].
The whole paper is organized as the following listed. In Section 2, the stability property of disease infectious equilibrium is analyzed by applying the geometrical criterion to derive Hopf bifurcation with regarded as time delay varying. In Section 3, the normal form is computed by applying center manifold theory and combined with dimension reduction method near Hopf point. Finally, the periodical solutions bifurcated from Hopf point and the continuous calculation of periodical solution is carried out as varying time delay continuously.
2. Study of System Stability
System (1.2) has two equilibrium solutions which are denoted as
and
with the expression
Do axis transformation by setting
then the linearized equation of Equation (1.2) is listed as
(2.1)
The characteristic coefficient matrix of Equation (2.1) is
(2.2)
Compute the determination of the matrix A, the characteristic equation of the equilibrium solution
has root
and another branch is
(2.3)
It is seen that the stability of
is determined at time delay
by the above coefficient matrix with
That is, Equilibrium solution
is asymptotically stable if
; or otherwise unstable as
. Hence, we have the conclusion that
is also asymptotically stable if
for any
or
.
The stability of equilibrium solution
is successively studied as varying time delay
which is the period of the occurrence of disease. Compute the determination of the matrix A, the characteristic equation of the equilibrium solution
is written as
(2.4)
with
Set
, with the assumption
, then separate the real part from the imaginary part in Equation (2.2), one has
(2.5)
and introducing two angle variables
and
, by solving
from Equation (2.4), one gets Hopf surfaces
(2.6)
with
By the relationship of triage function, one gets the following equality
(2.7)
We also assume
for
, and define new mapping
(2.8)
For given
, we seek for the finite values of
which determined by the inverse function of
with more than one branch. However, what’s necessary is to find the admissible value of the range that parameter
chosen. With this attempt, by differentiating Y with respect to
, one gets
(2.9)
with the assumption
, we get the value of
of the bottom of the curve
, for some
and
. In another respect,
, and we assume
to assure the solvability condition for Equation (2.6).
Subsequently, for some
, for given
, we derive the solution
of mapping (2.8) since the interSection of curve
with the curve
. Therefore, one gets the threshold value of time delay
determined by Equation (2.5) with the formula
(2.10)
for
.
To obtain the transversal condition at
, we apply the mathematical technique as shown by geometrical criterion given in paper [20] and rewrite Equation (2.2) as
(2.11)
Furthermore, differentiate Equation (2.11) with respect to
to get
(2.12)
Solving
from Equation (2.12) to get
(2.13)
with
In another respect, Set
, differentiate
with respect to
to get
(2.14)
and differentiate Equation (2.13) with respect to
and k, respectively, to get
(2.15)
and
(2.16)
Noticed that
, then we have
(2.17)
Further, we have
(2.18)
Therefore we give the following result of the stability analysis of equilibrium solution
,
Theorem 2.1
losses its stability at the threshold value
with the fixed parameter
with
satisfies the solvability condition. The transversal condition
given by formula (2.19) determines the transverse direction of the characteristic roots associated with Hopf bifurcation. That is, there is a pair of imaginary roots that can transverse from the left half plane to the right half plane if
, or otherwise transverse from the right half plane to the left half plane if
.
3. Stability of Periodic Solution
In Section 2, stability of infectious disease equilibrium solution
loss as parameter cross over Hopf line given that the transversal condition
. The stability of bifurcating periodic solution is analyzed by perturbation technique near Hopf point
. We suppose that Hopf bifurcation happens at the critical parameter pairs
. Do axis transformation
(for simplicity the overbar is ignored), to write Equation (1.1) as the following truncated system with Taylor expansion to three orders,
(3.1)
Equation (3.1) is defined on Banach space
with the supreme norm defined as
, herein
.
We define the phase space to be the extended phase space
with a possible jump discontinuity at
. Set
and
and the linearized equation is written as
(3.2)
with
Set
and
for
, Equation (3.2) can be rewritten as
(3.3)
By Rieze representation theorem, there exists a
matrix function
of bounded variation and
to express
(3.4)
with
and
(3.5)
with
and
Based on the fundamental theory of DDEs, define
to be the infinitesimal generator of the solution semigroup associated with linear operator
such that
(3.6)
for
. For
, the adjoint operator of
is defined as
(3.7)
Define the inner product
(3.8)
Suppose that the eigenvector q and
satisfy
(3.9)
with
For example, we choose
with
We also write Equation (3.1) into an operator differential equation
(3.10)
with nonlinear terms
(3.11)
and
(3.12)
Since Hopf bifurcation occurs at the critical parameter pairs
with the imaginary roots
, it is supposed
be the corresponding eigenspace and Q is its complementary subspace. By decomposition,
, and we define
be the projection operator and
(3.13)
Therefore, set
with
, then by Equation (3.5) one gets
(3.14)
henceforth, set
, Equation (3.14) is written as Taylor expansion to be truncated to 3 order with the expression
(3.15)
with
, and
(3.16)
Define the operator
on the space of homogeneous polynomial
by
(3.17)
On the center manifold, the normal form of Equation (3.15) is expressed as
(3.18)
with
(3.19)
By choosing
one gets
(3.20)
and the normal form (3.17) is derived as
(3.21)
The bases of
is expressed as
The bases of
is expressed as
Hence, we choose
(3.22)
Set
By the above definition of
in Equation (3.7), one gets
(3.23)
which satisfies the initial condition
(3.24)
By the computation of integral of Equation (3.23), we derive all of the coeffcients
and
. Based on Equation (3.8), Equation (3.15), Equation (3.17), one gets
(3.25)
By calculation, the coefficients in Equation (3.21) is derived as
and
By the above analysis, we conclude that
Theorem 3.1 There is a periodical solution with small amplitude bifurcating from Hopf point
which is stable if the first Lyapunov exponent
, otherwise unstable if
and the bifurcating direction is determined by the signature of
, which is supercritical if
or subcritical if
.
4. Conclusion
The stability property of the disease infectious equilibrium solution of a type of HIV mathematical model with delay feedback control was investigated by varying parameter pairs
on parameter space. The Hopf bifurcation lines were tracked via using geometrical criterion of DDEs with multiple time delays. By using Schmidt dimensional reduction method combined with center manifold analytical technique, the universal norm form was computed near Hopf point. As period time delay
is prolonged, stability of the disease infectious equilibrium solution loss and the stable periodical oscillation solutions arise near Hopf point.