
Citation: A. M. Elaiw, N. H. AlShamrani, A. D. Hobiny. Mathematical modeling of HIV/HTLV co-infection with CTL-mediated immunity[J]. AIMS Mathematics, 2021, 6(2): 1634-1676. doi: 10.3934/math.2021098
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During the last decades different dangerous viruses have been recognized which attack the human body and causes many fatal diseases. As an example of these viruses, the human immunodeficiency virus (HIV) which is the causative agent for acquired immunodeficiency syndrome (AIDS). According to global health observatory (GHO, 2018) data of HIV/AIDS published by WHO [1] that says, globally, about 37.9 million HIV-infected people in 2018, 1.7 million newly HIV-infected and 770,000 HIV-related death in the same year. HIV is a retrovirus that infects the susceptible CD4+T cells which play a central role in immune system defence. During the last decades, mathematical modeling of within-host HIV infection has witnessed a significant development [2]. Nowak and Bangham [3] have introduced the basic HIV infection model which describes the interaction between three compartments, susceptible CD4+T cells (S), actively HIV-infected cells (I) and free HIV particles (V). Latent viral reservoirs remain one of the major hurdles for eradicating the HIV by current antiviral therapy. Latently HIV-infected cells include HIV virions but do not produce them until they become activated. Mathematical modeling of HIV dynamics with latency can help in predicting the effect of antiviral drug efficacy on HIV progression [4]. Rong and Perelson [5] have incorporated the latently infected cells in the basic HIV model presented in [3] as:
{˙S=ρ−αS−η1SV,˙L=(1−β)η1SV−(λ+γ)L,˙I=βη1SV+λL−aI,˙V=bI−εV, | (1.1) |
where S=S(t), L=L(t), I=I(t) and V=V(t) are the concentrations of susceptible CD4+T cells, latently HIV-infected cells, actively HIV-infected cells and free HIV particles at time t, respectively. The susceptible CD4+T cells are produced at specific constant rate ρ. The HIV virions can replicate using virus-to-cell (VTC) transmission. The term η1SV refers to the rate at which new infectious appears by VTC contact between free HIV particles and susceptible CD4+T cells. Latently HIV-infected cells are transmitted to be active at rate λL. The free HIV particles are generated at rate bI. The natural death rates of the susceptible CD4+T cells, latently HIV-infected cells, actively HIV-infected cells and free HIV particles are given by αS, γL, aI and εV, respectively. A fraction β∈(0,1) of new HIV-infected cells will be active, and the remaining part 1−β will be latent. During the last decades, mathematical modeling and analysis of HIV mono-infection with both latently and actively HIV-infected cells have witnessed a significant development [6,7,8,9,10,11,12].
Model (1.1) assumed that the HIV can only spread by VTC transmission. However, several works have reported that there is another mode of transmission called cell-to-cell (CTC) where the HIV can be transmitted directly from an infected cell to a healthy CD4+T cell through the formation of virological synapses [13]. Sourisseau et al. [14] have shown that CTC transmission plays an efficient role in the HIV replication. Sigal et al. [15] have demonstrated the importance of CTC transmission in the HIV infection process during the antiviral treatment. Iwami et al. [13] have shown that about 60% of HIV infections are due to CTC transmission. In addition, CTC transmission can increase the HIV fitness by 3.9 times and decrease the production time of HIV particles by 0.9 times [16]. HIV dynamics model with latency and both VTC and CTC transmissions is given by [17,18]:
{˙S=ρ−αS−η1SV−η2SI,˙L=(1−β)(η1SV+η2SI)−(λ+γ)L,˙I=β(η1SV+η2SI)+λL−aI,˙V=bI−εV, | (1.2) |
where, the term η2SI refers to the rate at which new infectious appears by CTC contact between HIV-infected cells and susceptible CD4+T cells.
Another example of the dangerous human viruses is called Human T-lymphotropic virus type Ⅰ (HTLV-I) which can lead to two diseases, adult T-cell leukemia (ATL) and HTLV-I-associated myelopathy/tropical spastic paraparesis (HAM/TSP). The discovery of the first human retrovirus HTLV-I is back to 1980, and after 3 years the HIV was determined [19]. HTLV-I is global epidemic that infects about 10-25 million persons [20]. The infection is endemic in the Caribbean, southern Japan, the Middle East, South America, parts of Africa, Melanesia and Papua New Guinea [21]. HTLV-I is a provirus that targets the susceptible CD4+T cells. HTLV-I can spread to susceptible CD4+T cells from CTC through the virological synapse. HTLV-infected cells can be divided into two kinds based on the presence of Tax inside the cell or not: (i) Tax−, or latently HTLV-infected cells are resting CD4+T cells that contain a provirus and do not express Tax, and (ii) Tax+, or actively HTLV-infected cells are activated provirus-carrying CD4+T cells that do express Tax [22]. During the primary infection stage of HTLV-I, the proviral load can reach high level, approximately 30-50% [23]. Unlike in the case of HIV infection, however, only a small percentage of infected individuals develop the disease and 2-5% percent of HTLV-I carriers develop symptoms of ATL and another 0.25-3% develop HAM/TSP [24]. Stilianakis and Seydel [25] have formulated an HTLV-I model to describe the interaction of susceptible CD4+T cells, latently HTLV-infected cells, Tax-expressing HTLV-infected cells (actively HTLV-infected cells) and leukemia cells (ATL cells) as:
{˙S=ρ−αS−η3SY,˙E=η3SY−(ψ+ω)E,˙Y=ψE−(ϑ+δ)Y,˙Z=ϑY+ℓZ(1−ZZmax)−θZ, | (1.3) |
where S=S(t), E=E(t), Y=Y(t) and Z=Z(t) are the concentrations of susceptible CD4+T cells, latently HTLV-infected cells, Tax-expressing HTLV-infected cells and ATL cells, at time t, respectively. In contrast of HIV, the transmission of HTLV-I can be only from CTC that is the HTLV virions can only survive inside the host CD4+T cells and cannot be detectable in the plasma. The rate at which new infectious appears by CTC contact between Tax-expressing HTLV-infected cells and susceptible CD4+T cells is assumed to be η3SY. The natural death rate of the latently HTLV-infected cells, Tax-expressing HTLV-infected cells and ATL cells are represented by ωE, δY and θZ, respectively. The term ψE accounts for the rate of latently HTLV-infected cells that become Tax-expressing HTLV-infected cells. ϑY is the transmission rate at which Tax-expressing HTLV-infected cells convert to ATL cells. The logistic term ℓZ(1−ZZmax) denotes the proliferation rate of the ATL cells, where Zmax is the maximal concentration that ATL cells can grow. The parameter ℓ is the maximum proliferation rate constant of ATL cells. Many researchers have been concerned to study mathematical modeling and analysis of HTLV-I mono-infection in several works [26,27,28].
Cytotoxic T lymphocytes (CTLs) are recognized as the significant component of the human immune response against viral infections. CTLs inhibit viral replication and kill the cells which are infected by viruses. In fact, CTLs are necessary and universal to control HIV infection [29]. During the recent years, great efforts have been made to formulate and analyze the within-host HIV mono-infection models under the influence of CTL immune response (see e.g. [2,3]). In [30], latently HIV-infected cells have been included in the HIV dynamics models with CTL immune response. In case of HTLV-I infection, it has been reported in [31] that the CTLs play an effective role in controlling such infection. CTLs can recognize and kill the Tax-expressing HTLV-infected cells, moreover, they can reduce the proviral load. In the literature, several mathematical models have been proposed to describe the dynamics of HTLV-I under the effect of CTL immune response (see e.g. [21,32,33,34,35,36]). In [20,37,38], HTLV-I dynamics models have been presented by incorporating latently HTLV-infected cells and CTL immune response.
Simultaneous infection by HIV and HTLV-I and the etiology of their pathogenic and disease outcomes have become a global health matter over the past 10 years [39]. It is commonly that HIV/HTLV-I co-infection can be endemic in areas where individuals experience high risk attitudes; such as unprotected sexual contact and unsafe injection practices; that cause transmission of contaminated body fluids between individuals. This shed a light on the importance of studying HIV/HTLV-I co-infection [40]. Although CD4+T cells are the major targets of both HIV and HTLV-I, however, these viruses present a different biological behavior that causes diverse impacts on host immunity and ultimately lead to numerous clinical diseases [41]. It has been reported that the HTLV-I co-infection rate among HIV infected patients as increase as 100 to 500 times in comparison with the general population [42]. In seroepidemiologic studies, it has been recorded that HIV-infected patients are more exposure to be co-infected with HTLV-I, and vice versa compared to the general population [43]. HIV/HTLV-I co-infection is usually found in individuals of specific ethnic or who belonged to geographic origins where these viruses are simultaneously endemic [44]. As an example, the co-infection rates in individuals living in Bahia have reached 16% of HIV-infected patients [45]. The prevalence of dual infection with HIV and HTLV-I has become more widely in several geographical regions throughout the world such as South America, Europe, the Caribbean, Bahia (Brazil), Mozambique (Africa), and Japan [39,43,45,46,47]. HIV and HTLV-I dual infection appears to have an overlap on the course of associated clinical outcomes with both viruses [43]. Several reports have concluded that HIV/HTLV-I co-infected patients were found to have an increase of CD4+T cells count in comparison with HIV mono-infected patients, although there is no evident to result in a better immune response [41,48]. Indeed, simultaneous infected patients by both viruses with CD4+T counts greater than 200 cells/mm3 are more exposure to have other opportunistic infections as compared with HIV mono-infected patients who have similar CD4+T counts [48]. Studies have reported that higher mortality and shortened survival rates were accompany with co-infected individuals more than mono-infected individuals [46]. Considering the natural history of HIV, many researchers have noted that co-infection with HIV and HTLV-I can accelerate the clinical progression to AIDS. On the other hand, HIV can adjust HTLV-I expression in co-infected individuals which leads them to a higher risk of developing HTLV-I related diseases such as ATL and TSP/HAM [42,43,46].
Great efforts have been made to develop and analyze mathematical models of HIV and HTLV-I mono-infections, however, modeling of HIV/HTLV-I co-infection has not been studied. In fact, such co-infection modeling and its analysis will be needed to help clinicians on estimating the appropriate time to initiate treatment in co-infected patients. Therefore, the aim of the present paper is to formulate a new HIV/HTLV-I co-infection model. We show that the model is well-posed by establishing that the solutions of the model are nonnegative and bounded. We derive a set of threshold parameters which govern the existence and stability of the equilibria of the model. Global stability of all equilibria is proven by constructing suitable Lyapunov functions and utilizing Lyapunov-LaSalle asymptotic stability theorem. We conduct some numerical simulations to illustrate the theoretical results.
The results of this work, such as co-infection model and its analysis will help clinicians estimate the appropriate time for patients with co-infection to begin treatment. On the other hand, this study, from a certain point of view, illustrate the complexity of this co-infection model and the model is helpful to clinic treatment. It is worth mentioning, if we look at research perspectives, that appropriate developments of the model presented in this paper can be focused on the within host modeling of the competition between COVID19 virus and the immune system by a complex dynamics described in [49]. This dynamics which occurs, in human lungs, once the virus, after contagion, has gone over the biological barriers which protect each individuals, see [47].
We set up an ordinary differential equation model that describes the change of concentrations of eight compartments with respect to time t; susceptible CD4+T cells S(t), latently HIV-infected cells L(t), actively HIV-infected cells I(t), latently HTLV-infected cells E(t), Tax-expressing HTLV-infected cells Y(t), free HIV particles V(t), HIV-specific CTLs CI(t) and HTLV-specific CTLs CY(t). The dynamics of HIV/HTLV-I co-infection is schematically shown in the transfer diagram given in Figure 1. Our proposed model is given by the following form:
{˙S=ρ−αS−η1SV−η2SI−η3SY,˙L=(1−β)(η1SV+η2SI)−(λ+γ)L,˙I=β(η1SV+η2SI)+λL−aI−μ1CII,˙E=φη3SY−(ψ+ω)E,˙Y=ψE−δY−μ2CYY,˙V=bI−εV,˙CI=σ1CII−π1CI,˙CY=σ2CYY−π2CY, | (2.1) |
where (S,L,I,E,Y,V,CI,CY)=(S(t),L(t),I(t),E(t),Y(t),V(t),CI(t),CY(t)). The term μ1CII is the killing rate of actively HIV-infected cells due to their specific immunity. The term μ2CYY is the killing rate of Tax-expressing HTLV-infected cells due to their specific immunity. The proliferation and death rates for both effective HIV-specific CTLs and HTLV-specific CTLs are given by σ1CII, σ2CYY, π1CI and π2CY, respectively. All remaining parameters have the same biological meaning as explained in the previous section. All parameters and their definitions are summarized in Table 1.
Parameter | Description |
ρ | Recruitment rate for the susceptible CD4+T cells |
α | Natural mortality rate constant for the susceptible CD4+T cells |
η1 | Virus-cell incidence rate constant between free HIV particles and susceptible CD4+T cells |
η2 | Cell-cell incidence rate constant between HIV-infected cells and susceptible CD4+T cells |
η3 | Cell-cell incidence rate constant between Tax-expressing HTLV-infected cells andsusceptible CD4+T cells |
β∈(0,1) | Fraction coefficient accounts for the probability of new HIV-infected cells could be active, and the remaining part 1−β will be latent |
γ | Death rate constant of latently HIV-infected cells |
a | Death rate constant of actively HIV-infected cells |
μ1 | Killing rate constant of actively HIV-infected cells due to HIV-specific CTLs |
μ2 | Killing rate constant of Tax-expressing HTLV-infected cells due to HTLV-specific CTLs |
φ∈(0,1) | Probability of new HTLV infections could be enter a latent period |
λ | Transmission rate constant of latently HIV-infected cells that become actively HIV-infected cells |
ψ | Transmission rate constant of latently HTLV-infected cells that become Tax-expressing HTLV-infected cells |
ω | Death rate constant of latently HTLV-infected cells |
δ | Death rate constant of Tax-expressing HTLV-infected cells |
b | Generation rate constant of new HIV particles |
ε | Death rate constant of free HIV particles |
σ1 | Proliferation rate constant of HIV-specific CTLs |
σ2 | Proliferation rate constant of HTLV-specific CTLs |
π1 | Decay rate constant of HIV-specific CTLs |
π2 | Decay rate constant of HTLV-specific CTLs |
Let Ωj>0, j=1,...,5 and define
Θ={(S,L,I,E,Y,V,CI,CY)∈R8≥0:0≤S(t),L(t),I(t)≤Ω1, 0≤E(t),Y(t)≤Ω2, 0≤V(t)≤Ω3, 0≤CI(t)≤Ω4, 0≤CY(t)≤Ω5}. |
Proposition 1. The compact set Θ is positively invariant for system (2.1).
Proof. We have
˙S∣S=0=ρ>0, ˙L∣L=0=(1−β)(η1SV+η2SI)≥0 for all S,V,I≥0,˙I∣I=0=βη1SV+λL≥0 for all S,V,L≥0,˙E∣E=0=φη3SY for all S,Y≥0, ˙Y∣Y=0=ψE≥0 for all E≥0,˙V∣V=0=bI≥0 for all I≥0, ˙CI∣CI=0=0, ˙CY∣CY=0=0. |
This ensures that, (S(t),L(t),I(t),E(t),Y(t),V(t),CI(t),CY(t))∈R8≥0 for all t≥0 when (S(0),L(0),I(0),E(0),Y(0),V(0),CI(0),CY(0))∈R8≥0. To show the boundedness of all state variables, we let
Ψ=S+L+I+1φ(E+Y)+a2bV+μ1σ1CI+μ2φσ2CY. |
Then
˙Ψ=ρ−αS−γL−a2I−ωφE−δφY−aε2bV−μ1π1σ1CI−μ2π2φσ2CY≤ρ−ϕ[S+L)+I+1φ(E+Y)+a2bV+μ1σ1CI+μ2φσ2CY]=ρ−ϕΨ, |
where ϕ=min{α,γ,a2,ω,δ,ε,π1,π2}. Hence, 0≤Ψ(t)≤Ω1 if Ψ(0)≤Ω1 for t≥0, where Ω1=ρϕ. Since S, L, I, E, Y, V, CI, and CY are all nonnegative then 0≤S(t),L(t),I(t)≤Ω1, 0≤E(t),Y(t)≤Ω2, 0≤V(t)≤Ω3, 0≤CI(t)≤Ω4, 0≤CY(t)≤Ω5 if S(0)+L(0)+I(0)+1φ(E(0)+Y(0))+a2bV(0)+μ1σ1CI(0)+μ2φσ2CY(0)≤Ω1, where Ω2=φΩ1, Ω3=2bΩ1a, Ω4=σ1Ω1μ1 and Ω5=φσ2Ω1μ2.
In this section, we derive eight threshold parameters which guarantee the existence of the equilibria of the model. Let (S,L,I,E,Y,V,CI,CY) be any equilibrium of system (2.1) satisfying the following equations:
0=ρ−αS−η1SV−η2SI−η3SY, | (4.1) |
0=(1−β)(η1SV+η2SI)−(λ+γ)L, | (4.2) |
0=β(η1SV+η2SI)+λL−aI−μ1CII, | (4.3) |
0=φη3SY−(ψ+ω)E, | (4.4) |
0=ψE−δY−μ2CYY, | (4.5) |
0=bI−εV, | (4.6) |
0=(σ1I−π1)CI, | (4.7) |
0=(σ2Y−π2)CY. | (4.8) |
The straightforward calculation finds that system (2.1) admits eight equilibria.
(ⅰ) Infection-free equilibrium, Đ0=(S0,0,0,0,0,0,0,0), where S0=ρ/α. This case describes the situation of healthy state where both HIV and HTLV are absent.
(ⅱ) Chronic HIV mono-infection equilibrium with inactive immune response, Đ1=(S1,L1,I1,0,0,V1,0,0), where
S1=aε(γ+λ)(βγ+λ)(η1b+η2ε), L1=aεα(1−β)(βγ+λ)(η1b+η2ε)[S0(βγ+λ)(η1b+η2ε)aε(γ+λ)−1],I1=εαη1b+η2ε[S0(βγ+λ)(η1b+η2ε)aε(γ+λ)−1], V1=αbη1b+η2ε[S0(βγ+λ)(η1b+η2ε)aε(γ+λ)−1]. |
Therefore, Đ1 exists when
S0(βγ+λ)(η1b+η2ε)aε(γ+λ)>1. |
At the equilibrium Đ1 the chronic HIV mono-infection persists while the immune response is unstimulated. The basic HIV mono-infection reproductive ratio for system (2.1) is defined as:
ℜ1=S0(βγ+λ)(η1b+η2ε)aε(γ+λ)=ℜ11+ℜ12, |
where
ℜ11=S0η1b(βγ+λ)aε(γ+λ), ℜ12=S0η2(βγ+λ)a(γ+λ). |
The parameter ℜ1 determines whether or not a chronic HIV infection can be established. In fact, ℜ11 measures the average number of secondary HIV infected generation caused by an existing free HIV particles, while ℜ12 measures the average number of secondary HIV infected generation caused by an HIV-infected cell. Therefore, ℜ11 and ℜ12 are the basic HIV mono-infection reproductive ratio corresponding to VTC and CTC infections, respectively. In terms of ℜ1, we can write
S1=S0ℜ1, L1=aεα(1−β)(βγ+λ)(η1b+η2ε)(ℜ1−1), I1=εαη1b+η2ε(ℜ1−1), V1=αbη1b+η2ε(ℜ1−1). |
(ⅲ) Chronic HTLV mono-infection equilibrium with inactive immune response, Đ2=(S2,0,0,E2,Y2,0,0,0), where
S2=δ(ψ+ω)φη3ψ, E2=αδη3ψ[φη3ψS0δ(ψ+ω)−1],Y2=αη3[φη3ψS0δ(ψ+ω)−1]. |
Therefore, Đ2 exists when
φη3ψS0δ(ψ+ω)>1. |
At the equilibrium Đ2 the chronic HTLV mono-infection persists while the immune response is unstimulated. The basic HTLV mono-infection reproductive ratio for system (2.1) is defined as:
ℜ2=φη3ψS0δ(ψ+ω). |
The parameter ℜ2 decides whether or not a chronic HTLV infection can be established. In terms of ℜ2, we can write
S2=S0ℜ2, E2=αδη3ψ(ℜ2−1), Y2=αη3(ℜ2−1). |
Remark 1. We note that both ℜ1 and ℜ2 does not depend of parameters σi, πi and μi, i=1,2. Therefore, without treatment CTLs will not able to clear HIV or HTLV-I from the body.
(ⅳ) Chronic HIV mono-infection equilibrium with only active HIV-specific CTL, Đ3=(S3,L3,I3,0,0,V3,CI3,0), where
S3=εσ1ρπ1(η1b+η2ε)+αεσ1, L3=ρπ1(1−β)(η1b+η2ε)(γ+λ)[π1(η1b+η2ε)+αεσ1], I3=π1σ1,V3=bεI3=bπ1εσ1, CI3=aμ1[σ1ρ(βγ+λ)(η1b+η2ε)a(γ+λ){π1(η1b+η2ε)+αεσ1}−1]. |
We note that Đ3 exists when σ1ρ(βγ+λ)(η1b+η2ε)a(γ+λ)[π1(η1b+η2ε)+αεσ1]>1. The HIV-specific CTL-mediated immunity reproductive ratio in case of HIV mono-infection is stated as:
ℜ3=σ1ρ(βγ+λ)(η1b+η2ε)a(γ+λ)[π1(η1b+η2ε)+αεσ1]. |
Thus, CI3=aμ1(ℜ3−1). The parameter ℜ3 determines whether or not the HIV-specific CTL-mediated immune response is stimulated in the absent of HTLV infection.
(ⅴ) Chronic HTLV mono-infection equilibrium with only active HTLV-specific CTL, Đ4=(S4,0,0,E4,Y4,0,0,CY4), where
S4=σ2ρπ2η3+ασ2, Y4=π2σ2, E4=π2η3ρφ(ψ+ω)(π2η3+ασ2),CY4=δμ2[σ2ρφη3ψδ(ψ+ω)(π2η3+ασ2)−1]. |
We note that Đ4 exists when σ2ρφη3ψδ(ψ+ω)(π2η3+ασ2)>1. The HTLV-specific CTL-mediated immunity reproductive ratio in case of HTLV mono-infection is stated as:
ℜ4=σ2ρφη3ψδ(ψ+ω)(π2η3+ασ2). |
Thus, CY4=δμ2(ℜ4−1). The parameter ℜ4 determines whether or not the HTLV-specific CTL-mediated immune response is stimulated in the absent of HIV infection.
(ⅵ) Chronic HIV/HTLV co-infection equilibrium with only active HIV-specific CTL, Đ5=(S5,L5,I5,E5,Y5,V5,CI5,0), where
S5=δ(ψ+ω)φη3ψ=S2, I5=π1σ1=I3,V5=bπ1εσ1=V3, L5=δπ1(1−β)(ψ+ω)(η1b+η2ε)εη3σ1φψ(γ+λ),E5=δ[π1(η1b+η2ε)+αεσ1]εη3σ1ψ[ρφεη3σ1ψδ(ψ+ω){π1(η1b+η2ε)+αεσ1}−1],Y5=π1(η1b+η2ε)+αεσ1εη3σ1[ρφεη3σ1ψδ(ψ+ω){π1(η1b+η2ε)+αεσ1}−1],CI5=aμ1[δ(η1b+η2ε)(βγ+λ)(ψ+ω)aεφη3ψ(γ+λ)−1]=aμ1(ℜ1/ℜ2−1). |
We note that Đ5 exists when ℜ1/ℜ2>1 and ρφεη3σ1ψδ(ψ+ω)[π1(η1b+η2ε)+αεσ1]>1. The HTLV infection reproductive ratio in the presence of HIV infection is stated as:
ℜ5=ρφεη3σ1ψδ(ψ+ω)[π1(η1b+η2ε)+αεσ1]. |
Thus, E5=δ[π1(η1b+η2ε)+αεσ1]εη3σ1ψ(ℜ5−1), Y5=π1(η1b+η2ε)+αεσ1εη3σ1(ℜ5−1). The parameter ℜ5 determines whether or not HIV-infected patients could be co-infected with HTLV.
(ⅶ) Chronic HIV/HTLV co-infection equilibrium with only active HTLV-specific CTL, Đ6=(S6,L6,I6,E6,Y6,V6,0,CY6), where
S6=aε(γ+λ)(βγ+λ)(η1b+η2ε)=S1, L6=aε(1−β)(π2η3+ασ2)σ2(βγ+λ)(η1b+η2ε)[ρσ2(βγ+λ)(η1b+η2ε)aε(γ+λ)(π2η3+ασ2)−1],I6=ε(π2η3+ασ2)σ2(η1b+η2ε)[ρσ2(βγ+λ)(η1b+η2ε)aε(γ+λ)(π2η3+ασ2)−1], E6=aεφπ2η3(γ+λ)σ2(βγ+λ)(ψ+ω)(η1b+η2ε),Y6=π2σ2=Y4, V6=b(π2η3+ασ2)σ2(η1b+η2ε)[ρσ2(βγ+λ)(η1b+η2ε)aε(γ+λ)(π2η3+ασ2)−1],CY6=δμ2[aεφη3ψ(γ+λ)δ(βγ+λ)(ψ+ω)(η1b+η2ε)−1]=δμ2(ℜ2/ℜ1−1). |
We note that Đ6 exists when ℜ2/ℜ1>1 and ρσ2(βγ+λ)(η1b+η2ε)aε(γ+λ)(π2η3+ασ2)>1. The HIV infection reproductive ratio in the presence of HTLV infection is stated as:
ℜ6=ρσ2(βγ+λ)(η1b+η2ε)aε(γ+λ)(π2η3+ασ2). |
Thus, L6=aε(1−β)(π2η3+ασ2)σ2(βγ+λ)(η1b+η2ε)(ℜ6−1), I6=ε(π2η3+ασ2)σ2(η1b+η2ε)(ℜ6−1), V6=b(π2η3+ασ2)σ2(η1b+η2ε)(ℜ6−1). The parameter ℜ6 determines whether or not HTLV-infected patients could be co-infected with HIV.
(ⅷ) Chronic HIV/HTLV co-infection equilibrium with active HIV-specific CTL and HTLV-specific CTL, Đ7=(S7,L7,I7,E7,Y7,V7,CI7,CY7), where
S7=εσ1σ2ρπ1σ2(η1b+η2ε)+π2η3εσ1+αεσ1σ2, L7=π1σ2ρ(1−β)(η1b+η2ε)(γ+λ)[π1σ2(η1b+η2ε)+π2η3εσ1+αεσ1σ2],E7=π2η3εσ1ρφ(ψ+ω)[π1σ2(η1b+η2ε)+π2η3εσ1+αεσ1σ2], I7=π1σ1=I3=I5, Y7=π2σ2=Y4=Y6,V7=bπ1εσ1=V3=V5, CI7=aμ1[σ1σ2ρ(βγ+λ)(η1b+η2ε)a(γ+λ){π1σ2(η1b+η2ε)+π2η3εσ1+αεσ1σ2}−1],CY7=δμ2[φη3εσ1σ2ρψδ(ψ+ω){π1σ2(η1b+η2ε)+π2η3εσ1+αεσ1σ2}−1]. |
It is obvious that Đ7 exists when σ1σ2ρ(βγ+λ)(η1b+η2ε)a(γ+λ)[π1σ2(η1b+η2ε)+π2η3εσ1+αεσ1σ2]>1 and φη3εσ1σ2ρψδ(ψ+ω)[π1σ2(η1b+η2ε)+π2η3εσ1+αεσ1σ2]>1. Now we define
ℜ7=σ1σ2ρ(βγ+λ)(η1b+η2ε)a(γ+λ)[π1σ2(η1b+η2ε)+π2η3εσ1+αεσ1σ2],ℜ8=φη3εσ1σ2ρψδ(ψ+ω)[π1σ2(η1b+η2ε)+π2η3εσ1+αεσ1σ2]. |
Clearly, Đ7 exists when ℜ7>1 and ℜ8>1 and we can write CI7=aμ1(ℜ7−1) and CY7=δμ2(ℜ8−1). The parameter ℜ7 refers to the competed HIV-specific CTL-mediated immunity reproductive ratio in case of HIV/HTLV co-infection. On the other hand, the parameter ℜ8 refers to the competed HTLV-specific CTL-mediated immunity reproductive ratio case of HIV/HTLV co-infection.
The eight threshold parameters are given as follows:
ℜ1=S0(βγ+λ)(η1b+η2ε)aε(γ+λ), ℜ2=φη3ψS0δ(ψ+ω),ℜ3=σ1ρ(βγ+λ)(η1b+η2ε)a(γ+λ)[π1(η1b+η2ε)+αεσ1], ℜ4=σ2ρφη3ψδ(ψ+ω)(π2η3+ασ2),ℜ5=ρφεη3σ1ψδ(ψ+ω)[π1(η1b+η2ε)+αεσ1], ℜ6=ρσ2(βγ+λ)(η1b+η2ε)aε(γ+λ)(π2η3+ασ2),ℜ7=σ1σ2ρ(βγ+λ)(η1b+η2ε)a(γ+λ)[π1σ2(η1b+η2ε)+π2η3εσ1+αεσ1σ2],ℜ8=φη3εσ1σ2ρψδ(ψ+ω)[π1σ2(η1b+η2ε)+π2η3εσ1+αεσ1σ2]. |
According to the above discussion, we sum up the existence conditions for all equilibria in Table 2.
Equilibrium point | Definition | Existence conditions |
Đ0=(S0,0,0,0,0,0,0,0) | Infection-free equilibrium | None |
Đ1=(S1,L1,I1,0,0,V1,0,0) | Chronic HIV mono-infection equilibriumwith inactive immune response | ℜ1>1 |
Đ2=(S2,0,0,E2,Y2,0,0,0) | Chronic HTLV mono-infection equilibriumwith inactive immune response | ℜ2>1 |
Đ3=(S3,L3,I3,0,0,V3,CI3,0) | Chronic HIV mono-infection equilibriumwith only active HIV-specific CTL | ℜ3>1 |
Đ4=(S4,0,0,E4,Y4,0,0,CY4) | Chronic HTLV mono-infection equilibriumwith only active HTLV-specific CTL | ℜ4>1 |
Đ5=(S5,L5,I5,E5,Y5,V5,CI5,0) | Chronic HIV/HTLV co-infection equilibriumwith only active HIV-specific CTL | ℜ5>1 and ℜ1/ℜ2>1 |
Đ6=(S6,L6,I6,E6,Y6,V6,0,CY6) | Chronic HIV/HTLV co-infection equilibriumwith only active HTLV-specific CTL | ℜ6>1 and ℜ2/ℜ1>1 |
Đ7=(S7,L7,I7,E7,Y7,V7,CI7,CY7) | Chronic HIV/HTLV co-infectionequilibrium with active HIV-specificCTL and HTLV-specific CTL | ℜ7>1 and ℜ8>1 |
In this section we prove the global asymptotic stability of all equilibria by constructing Lyapunov functional following the method presented in [50]. We will use the arithmetic-geometric mean inequality
1nn∑i=1χi≥n√n∏i=1χi, χi≥0, i=1,2,... |
which yields
SjS+SILjSjIjL+LIjLjI≥3, j=1,3,5,6,7, | (5.1) |
SjS+SVIjSjVjI+IVjIjV≥3, j=1,3,5,6,7, | (5.2) |
SjS+SVLjSjVjL+LIjLjI+IVjIjV≥4, j=1,3,5,6,7, | (5.3) |
SjS+SYEjSjYjE+EYjEjY≥3, j=2,4,5,6,7. | (5.4) |
Theorem 1. If ℜ1≤1 and ℜ2≤1, then Đ0 is globally asymptotically stable (G.A.S).
Proof. We construct a Lyapunov function Φ0(S,L,I,E,Y,V,CI,CY) as:
Φ0=S0ϝ(SS0)+λβγ+λL+γ+λβγ+λI+1φE+ψ+ωφψY+η1S0εV+μ1(γ+λ)σ1(βγ+λ)CI+μ2(ψ+ω)φψσ2CY, |
where,
ϝ(υ)=υ−1−lnυ. |
Clearly, Φ0(S,L,I,E,Y,V,CI,CY)>0 for all S,L,I,E,Y,V,CI,CY>0, and Φ0(S0,0,0,0,0,0,0,0)=0. Calculating dΦ0dt along the solutions of system (2.1) as:
dΦ0dt=(1−S0S)[ρ−αS−η1SV−η2SI−η3SY]+λβγ+λ[(1−β)(η1SV+η2SI)−(λ+γ)L]+γ+λβγ+λ[β(η1SV+η2SI)+λL−aI−μ1CII]+1φ[φη3SY−(ψ+ω)E]+ψ+ωφψ[ψE−δY−μ2CYY]+η1S0ε(bI−εV)+μ1(γ+λ)σ1(βγ+λ)[σ1CII−π1CI]+μ2(ψ+ω)φψσ2[σ2CYY−π2CY]=(1−S0S)(ρ−αS)+η2S0I+η3S0Y−a(γ+λ)βγ+λI−δ(ψ+ω)φψY+η1bS0εI−μ1π1(γ+λ)σ1(βγ+λ)CI−μ2π2(ψ+ω)φψσ2CY. |
Using S0=ρ/α, we obtain
dΦ0dt=−α(S−S0)2S+a(γ+λ)βγ+λ(ℜ1−1)I+δ(ψ+ω)φψ(ℜ2−1)Y−μ1π1(γ+λ)σ1(βγ+λ)CI−μ2π2(ψ+ω)φψσ2CY. |
Therefore, dΦ0dt≤0 for all S,I,Y,CI,CY>0 and dΦ0dt=0 when S=S0 and I=Y=CI=CY=0. Define Υ0={(S,L,I,E,Y,V,CI,CY):dΦ0dt=0} and let Υ′0 be the largest invariant subset of Υ0. The solutions of system (2.1) converge to Υ′0. The set Υ′0 includes elements with S=S0 and I=Y=CI=CY=0, and hence ˙S=˙Y=0. The first and fifth equations of system (2.1) yield
0=˙S=ρ−αS0−η1S0V,0=˙Y=ψE. |
Thus, V(t)=E(t)=0 for all t. In addition, we have ˙I=0 and from the third equation of system (2.1) we obtain
0=˙I=λL, |
which yields L(t)=0 for all t. Therefore, Υ′0={Đ0} and by applying Lyapunov-LaSalle asymptotic stability theorem [51,52,53] we get that Đ0 is G.A.S.
Theorem 2. Let ℜ1>1, ℜ2/ℜ1≤1 and ℜ3≤1, then Đ1 is G.A.S.
Proof. Define a function Φ1(S,L,I,E,Y,V,CI,CY) as:
Φ1=S1ϝ(SS1)+λβγ+λL1ϝ(LL1)+γ+λβγ+λI1ϝ(II1)+1φE+ψ+ωφψY+η1S1εV1ϝ(VV1)+μ1(γ+λ)σ1(βγ+λ)CI+μ2(ψ+ω)φψσ2CY. |
Calculating dΦ1dt as:
dΦ1dt=(1−S1S)[ρ−αS−η1SV−η2SI−η3SY]+λβγ+λ(1−L1L)[(1−β)(η1SV+η2SI)−(λ+γ)L]+γ+λβγ+λ(1−I1I)[β(η1SV+η2SI)+λL−aI−μ1CII]+1φ[φη3SY−(ψ+ω)E]+ψ+ωφψ[ψE−δY−μ2CYY]+η1S1ε(1−V1V)[bI−εV]+μ1(γ+λ)σ1(βγ+λ)[σ1CII−π1CI]+μ2(ψ+ω)φψσ2[σ2CYY−π2CY]=(1−S1S)(ρ−αS)+η2S1I+η3S1Y−λ(1−β)βγ+λ(η1SV+η2SI)L1L+λ(γ+λ)βγ+λL1−a(γ+λ)βγ+λI−β(γ+λ)βγ+λ(η1SV+η2SI)I1I−λ(γ+λ)βγ+λLI1I+a(γ+λ)βγ+λI1+μ1(γ+λ)βγ+λCII1−δ(ψ+ω)φψY+η1S1bIε−η1S1bIεV1V+η1S1V1−μ1π1(γ+λ)σ1(βγ+λ)CI−μ2π2(ψ+ω)φψσ2CY. |
Using the equilibrium conditions for Đ1, we get
ρ=αS1+η1S1V1+η2S1I1, λ(1−β)βγ+λ(η1S1V1+η2S1I1)=λ(γ+λ)βγ+λL1,η1S1V1+η2S1I1=a(γ+λ)βγ+λI1, V1=bI1ε. |
Then, we obtain
dΦ1dt=(1−S1S)(αS1−αS)+(η1S1V1+η2S1I1)(1−S1S)+η3S1Y−λ(1−β)βγ+λη1S1V1SVL1S1V1L−λ(1−β)βγ+λη2S1I1SIL1S1I1L+λ(1−β)βγ+λ(η1S1V1+η2S1I1)−β(γ+λ)βγ+λη1S1V1SVI1S1V1I−β(γ+λ)βγ+λη2S1I1SS1−λ(1−β)βγ+λ(η1S1V1+η2S1I1)LI1L1I+η1S1V1+η2S1I1+μ1(γ+λ)βγ+λCII1−δ(ψ+ω)φψY−η1S1V1IV1I1V+η1S1V1−μ1π1(γ+λ)σ1(βγ+λ)CI−μ2π2(ψ+ω)φψσ2CY=−α(S−S1)2S+λ(1−β)βγ+λη1S1V1(4−S1S−SVL1S1V1L−LI1L1I−IV1I1V)+λ(1−β)βγ+λη2S1I1(3−S1S−SIL1S1I1L−LI1L1I)+β(γ+λ)βγ+λη1S1V1(3−S1S−SVI1S1V1I−IV1I1V)+β(γ+λ)βγ+λη2S1I1(2−S1S−SS1)+δ(ψ+ω)φψ(φη3ψS1δ(ψ+ω)−1)Y+μ1(γ+λ)βγ+λ(I1−π1σ1)CI−μ2π2(ψ+ω)φψσ2CY. | (5.5) |
Therefore, Eq (5.5) becomes
dΦ1dt=−(α+βη2I1(γ+λ)βγ+λ)(S−S1)2S+λ(1−β)βγ+λη1S1V1(4−S1S−SVL1S1V1L−LI1L1I−IV1I1V)+λ(1−β)βγ+λη2S1I1(3−S1S−SIL1S1I1L−LI1L1I)+β(γ+λ)βγ+λη1S1V1(3−S1S−SVI1S1V1I−IV1I1V)+δ(ψ+ω)φψ(ℜ2/ℜ1−1)Y+μ1(γ+λ)[π1(η1b+η2ε)+αεσ1]σ1(βγ+λ)(η1b+η2ε)(ℜ3−1)CI−μ2π2(ψ+ω)φψσ2CY. | (5.6) |
Since ℜ2/ℜ1≤1 and ℜ3≤1, then using inequalities (5.1)–(5.3) we get dΦ1dt≤0 for all S,L,I,Y,V,CI,CY>0. Moreover, dΦ1dt=0 when S=S1, L=L1, I=I1, V=V1 and Y=CI=CY=0. The solutions of system (2.1) converge to Υ′1 the largest invariant subset of Υ1={(S,L,I,E,Y,V,CI,CY):dΦ1dt=0}. The set Υ′1 includes Y=0, and then ˙Y=0. The fifth equation of system (2.1) implies
0=˙Y=ψE, |
which yields E(t)=0 for all t. Hence, Υ′1={Đ1} and Đ1 is G.A.S using Lyapunov-LaSalle asymptotic stability theorem.
Theorem 3. If ℜ2>1, ℜ1/ℜ2≤1 and ℜ4≤1, then Đ2 is G.A.S.
Proof. We define Φ2(S,L,I,E,Y,V,CI,CY) as:
Φ2=S2ϝ(SS2)+λβγ+λL+γ+λβγ+λI+1φE2ϝ(EE2)+ψ+ωφψY2ϝ(YY2)+η1S2εV+μ1(γ+λ)σ1(βγ+λ)CI+μ2(ψ+ω)φψσ2CY. |
We calculate dΦ2dt as:
dΦ2dt=(1−S2S)[ρ−αS−η1SV−η2SI−η3SY]+λβγ+λ[(1−β)(η1SV+η2SI)−(λ+γ)L]+γ+λβγ+λ[β(η1SV+η2SI)+λL−aI−μ1CII]+1φ(1−E2E)[φη3SY−(ψ+ω)E]+ψ+ωφψ(1−Y2Y)[ψE−δY−μ2CYY]+η1S2ε[bI−εV]+μ1(γ+λ)σ1(βγ+λ)[σ1CII−π1CI]+μ2(ψ+ω)φψσ2[σ2CYY−π2CY]=(1−S2S)(ρ−αS)+η2S2I+η3S2Y−a(γ+λ)βγ+λI−η3SYE2E+ψ+ωφE2−δ(ψ+ω)φψY−ψ+ωφEY2Y+δ(ψ+ω)φψY2+μ2(ψ+ω)φψCYY2+η1S2bIε−μ1π1(γ+λ)σ1(βγ+λ)CI−μ2π2(ψ+ω)φψσ2CY. |
Using the equilibrium conditions for Đ2:
ρ=αS2+η3S2Y2, η3S2Y2=ψ+ωφE2=δ(ψ+ω)φψY2, | (5.7) |
we obtain
dΦ2dt=(1−S2S)(αS2−αS)+η3S2Y2(1−S2S)+η2S2I−a(γ+λ)βγ+λI−η3S2Y2SYE2S2Y2E+η3S2Y2−η3S2Y2EY2E2Y+η3S2Y2+μ2(ψ+ω)φψCYY2+η1S2bIε−μ1π1(γ+λ)σ1(βγ+λ)CI−μ2π2(ψ+ω)φψσ2CY=−α(S−S2)2S+η3S2Y2(3−S2S−SYE2S2Y2E−EY2E2Y)+a(γ+λ)βγ+λ((η1b+η2ε)(βγ+λ)S2aε(γ+λ)−1)I−μ1π1(γ+λ)σ1(βγ+λ)CI+μ2(ψ+ω)φψ(Y2−π2σ2)CY=−α(S−S2)2S+η3S2Y2(3−S2S−SYE2S2Y2E−EY2E2Y)+a(γ+λ)βγ+λ(ℜ1/ℜ2−1)I−μ1π1(γ+λ)σ1(βγ+λ)CI+μ2(ψ+ω)(ασ2+η3π2)φψη3σ2(ℜ4−1)CY. |
Since ℜ1/ℜ2≤1 and ℜ4≤1, then using inequality (5.4) we get dΦ2dt≤0 for all S,L,I,E,Y,V,CI,CY>0. In addition, dΦ2dt=0 when S=S2, E=E2, Y=Y2 and I=CI=CY=0. Define Υ2={(S,L,I,E,Y,V,CI,CY):dΦ2dt=0} and let Υ′2 be the largest invariant subset of Υ2. The solutions of system (2.1) converge to Υ′2 which includes elements with S=S2, Y=Y2, I=0, then ˙S=0. The first equation of system (2.1) gives
0=˙S=ρ−αS2−η1S2V−η3S2Y2. |
From conditions (5.7) we get V(t)=0 for all t. Moreover, we have ˙I=0 and from the third equation of system (2.1) we obtain
0=˙I=λL, |
which yields L(t)=0 for all t. Therefore, Υ′2={Đ2}. By applying Lyapunov-LaSalle asymptotic stability theorem we get that Đ2 is G.A.S.
Theorem 4. Let ℜ3>1 and ℜ5≤1, then Đ3 is G.A.S.
Proof. Define a function Φ3(S,L,I,E,Y,V,CI,CY) as:
Φ3=S3ϝ(SS3)+λβγ+λL3ϝ(LL3)+γ+λβγ+λI3ϝ(II3)+1φE+ψ+ωφψY+η1S3εV3ϝ(VV3)+μ1(γ+λ)σ1(βγ+λ)CI3ϝ(CICI3)+μ2(ψ+ω)φψσ2CY. |
We calculate dΦ3dt as:
dΦ3dt=(1−S3S)[ρ−αS−η1SV−η2SI−η3SY]+λβγ+λ(1−L3L)[(1−β)(η1SV+η2SI)−(λ+γ)L]+γ+λβγ+λ(1−I3I)[β(η1SV+η2SI)+λL−aI−μ1CII]+1φ[φη3SY−(ψ+ω)E]+ψ+ωφψ[ψE−δY−μ2CYY]+η1S3ε(1−V3V)[bI−εV]+μ1(γ+λ)σ1(βγ+λ)(1−CI3CI)[σ1CII−π1CI]+μ2(ψ+ω)φψσ2[σ2CYY−π2CY]=(1−S3S)(ρ−αS)+η2S3I+η3S3Y−λ(1−β)βγ+λ(η1SV+η2SI)L3L+λ(γ+λ)βγ+λL3−a(γ+λ)βγ+λI−β(γ+λ)βγ+λ(η1SV+η2SI)I3I−λ(γ+λ)βγ+λLI3I+a(γ+λ)βγ+λI3+μ1(γ+λ)βγ+λCII3−δ(ψ+ω)φψY+η1S3εbI−η1S3εbIV3V+η1S3V3−μ1π1(γ+λ)σ1(βγ+λ)CI−μ1(γ+λ)βγ+λCI3I+μ1π1(γ+λ)σ1(βγ+λ)CI3−μ2π2(ψ+ω)φψσ2CY. |
Using the equilibrium conditions for Đ3:
ρ=αS3+η1S3V3+η2S3I3, λ(1−β)βγ+λ(η1S3V3++η2S3I3)=λ(γ+λ)βγ+λL3,η1S3V3+η2S3I3=a(γ+λ)βγ+λI3+μ1(γ+λ)βγ+λCI3I3, I3=π1σ1, V3=bεI3, |
we obtain
dΦ3dt=(1−S3S)(αS3−αS)+(η1S3V3+η2S3I3)(1−S3S)+(η3S3−δ(ψ+ω)φψ)Y−λ(1−β)βγ+λη1S3V3SVL3S3V3L−λ(1−β)βγ+λη2S3I3SIL3S3I3L+λ(1−β)βγ+λ(η1S3V3+η2S3I3)−β(γ+λ)βγ+λη1S3V3SVI3S3V3I−β(γ+λ)βγ+λη2S3I3SS3−λ(1−β)βγ+λ(η1S3V3+η2S3I3)LI3L3I+η1S3V3+η2S3I3−η1S3V3IV3I3V+η1S3V3−μ2π2(ψ+ω)φψσ2CY=−α(S−S3)2S+λ(1−β)βγ+λη1S3V3(4−S3S−SVL3S3V3L−LI3L3I−IV3I3V)+λ(1−β)βγ+λη2S3I3(3−S3S−SIL3S3I3L−LI3L3I)+β(γ+λ)βγ+λη1S3V3(3−S3S−SVI3S3V3I−IV3I3V)+β(γ+λ)βγ+λη2S3I3(2−S3S−SS3)+δ(ψ+ω)φψ(φψη3S3δ(ψ+ω)−1)Y−μ2π2(ψ+ω)φψσ2CY=−(α+βη2I3(γ+λ)βγ+λ)(S−S3)2S+λ(1−β)βγ+λη1S3V3(4−S3S−SVL3S3V3L−LI3L3I−IV3I3V)+λ(1−β)βγ+λη2S3I3(3−S3S−SIL3S3I3L−LI3L3I)+β(γ+λ)βγ+λη1S3V3(3−S3S−SVI3S3V3I−IV3I3V)+δ(ψ+ω)φψ(ℜ5−1)Y−μ2π2(ψ+ω)φψσ2CY. |
Hence, if ℜ5≤1, then using inequalities (5.1)–(5.3) we get dΦ3dt≤0 for all S,L,I,E,Y,V,CI,CY>0. Moreover, dΦ3dt=0 at S=S3, L=L3, I=I3, V=V3 and Y=CY=0. The solutions of system (2.1) converge to Υ′3 the largest invariant subset of Υ3={(S,L,I,E,Y,V,CI,CY):dΦ3dt=0}. The set Υ′3 contains elements with S=S3, L=L3, I=I3, V=V3, Y=0, and then ˙I=˙Y=0. The third and fifth equations of system (2.1) give
0=˙I=β(η1S3V3+η2S3I3)+λL3−aI3−μ1CII3,0=˙Y=ψE, |
which yield CI(t)=CI3 and E(t)=0 for all t. Therefore, Υ′3={Đ3}. By applying Lyapunov-LaSalle asymptotic stability theorem we get that Đ3 is G.A.S.
Theorem 5. If ℜ4>1 and ℜ6≤1, then Đ4 is G.A.S.
Proof. Define Φ4(S,L,I,E,Y,V,CI,CY) as:
Φ4=S4ϝ(SS4)+λβγ+λL+γ+λβγ+λI+1φE4ϝ(EE4)+ψ+ωφψY4ϝ(YY4)+η1S4εV+μ1(γ+λ)σ1(βγ+λ)CI+μ2(ψ+ω)φψσ2CY4ϝ(CYCY4). |
Calculating dΦ4dt as:
dΦ4dt=(1−S4S)[ρ−αS−η1SV−η2SI−η3SY]+λβγ+λ[(1−β)(η1SV+η2SI)−(λ+γ)L]+γ+λβγ+λ[β(η1SV+η2SI)+λL−aI−μ1CII]+1φ(1−E4E)[φη3SY−(ψ+ω)E]+ψ+ωφψ(1−Y4Y)[ψE−δY−μ2CYY]+η1S4ε[bI−εV]+μ1(γ+λ)σ1(βγ+λ)[σ1CII−π1CI]+μ2(ψ+ω)φψσ2(1−CY4CY)[σ2CYY−π2CY]=(1−S4S)(ρ−αS)+η2S4I+η3S4Y−a(γ+λ)βγ+λI−η3SYE4E+ψ+ωφE4−δ(ψ+ω)φψY−ψ+ωφEY4Y+δ(ψ+ω)φψY4+μ2(ψ+ω)φψCYY4+η1S4bIε−μ1π1(γ+λ)σ1(βγ+λ)CI−μ2π2(ψ+ω)φψσ2CY−μ2(ψ+ω)φψCY4Y+μ2π2(ψ+ω)φψσ2CY4. |
Using the equilibrium conditions for Đ4:
ρ=αS4+η3S4Y4, Y4=π2σ2,η3S4Y4=ψ+ωφE4=δ(ψ+ω)φψY4+μ2(ψ+ω)φψCY4Y4. |
We obtain
dΦ4dt=(1−S4S)(αS4−αS)+η3S4Y4(1−S4S)+η2S4I−a(γ+λ)βγ+λI−η3S4Y4SYE4S4Y4E+η3S4Y4−η3S4Y4EY4E4Y+η3S4Y4+η1S4bIε−μ1π1(γ+λ)σ1(βγ+λ)CI=−α(S−S4)2S+η3S4Y4(3−S4S−SYE4S4Y4E−EY4E4Y)+a(γ+λ)βγ+λ((η1b+η2ε)(βγ+λ)S4aε(γ+λ)−1)I−μ1π1(γ+λ)σ1(βγ+λ)CI=−α(S−S4)2S+η3S4Y4(3−S4S−SYE4S4Y4E−EY4E4Y)+a(γ+λ)βγ+λ(ℜ6−1)I−μ1π1(γ+λ)σ1(βγ+λ)CI. |
If ℜ6≤1, then using inequality (5.4) we get dΦ4dt≤0 for all S,L,I,E,Y,V,CI,CY>0, where dΦ4dt=0 at S=S4, E=E4, Y=Y4 and I=CI=0. The solutions of system (2.1) converge to Υ′4 the largest invariant subset of Υ4={(S,L,I,E,Y,V,CI,CY):dΦ4dt=0}. The set Υ′4 contains elements with S=S4, E=E4, Y=Y4, I=0, and then ˙S=˙Y=0. The first and fifth equations of system (2.1) imply
0=˙S=ρ−αS4−η1S4V−η3S4Y4,0=˙Y=ψE4−δY4−μ2CYY4, |
which yield V(t)=0 and CY(t)=CY4 for all t. Since ˙I=0, then from the third equation of system (2.1) we obtain
0=˙I=λL, |
which yields L(t)=0 for all t. Therefore, Υ′4={Đ4}. By applying Lyapunov-LaSalle asymptotic stability theorem we obtain that Đ4 is G.A.S.
Theorem 6. If ℜ5>1, ℜ8≤1 and ℜ1/ℜ2>1, then Đ5 is G.A.S.
Proof. Define Φ5(S,L,I,E,Y,V,CI,CY) as:
Φ5=S5ϝ(SS5)+λβγ+λL5ϝ(LL5)+γ+λβγ+λI5ϝ(II5)+1φE5ϝ(EE5)+ψ+ωφψY5ϝ(YY5)+η1S5εV5ϝ(VV5)+μ1(γ+λ)σ1(βγ+λ)CI5ϝ(CICI5)+μ2(ψ+ω)φψσ2CY. |
Calculating dΦ5dt as:
dΦ5dt=(1−S5S)[ρ−αS−η1SV−η2SI−η3SY]+λβγ+λ(1−L5L)[(1−β)(η1SV+η2SI)−(λ+γ)L]+γ+λβγ+λ(1−I5I)[β(η1SV+η2SI)+λL−aI−μ1CII]+1φ(1−E5E)[φη3SY−(ψ+ω)E]+ψ+ωφψ(1−Y5Y)[ψE−δY−μ2CYY]+η1S5ε(1−V5V)[bI−εV]+μ1(γ+λ)σ1(βγ+λ)(1−CI5CI)[σ1CII−π1CI]+μ2(ψ+ω)φψσ2[σ2CYY−π2CY]=(1−S5S)(ρ−αS)+η2S5I+η3S5Y−λ(1−β)βγ+λ(η1SV+η2SI)L5L+λ(γ+λ)βγ+λL5−a(γ+λ)βγ+λI−β(γ+λ)βγ+λ(η1SV+η2SI)I5I−λ(γ+λ)βγ+λLI5I+a(γ+λ)βγ+λI5+μ1(γ+λ)βγ+λCII5−η3SYE5E+ψ+ωφE5−δ(ψ+ω)φψY−ψ+ωφEY5Y+δ(ψ+ω)φψY5+μ2(ψ+ω)φψCYY5+η1S5bIε−η1S5V5bIεV+η1S5V5−μ1π1(γ+λ)σ1(βγ+λ)CI−μ1(γ+λ)βγ+λCI5I+μ1π1(γ+λ)σ1(βγ+λ)CI5−μ2π2(ψ+ω)φψσ2CY. |
Using the equilibrium conditions for Đ5:
ρ=αS5+η1S5V5+η2S5I5+η3S5Y5,λ(1−β)βγ+λ(η1S5V5+η2S5I5)=λ(γ+λ)βγ+λL5,η1S5V5+η2S5I5=a(γ+λ)βγ+λI5+μ1(γ+λ)βγ+λCI5I5,η3S5Y5=ψ+ωφE5=δ(ψ+ω)φψY5, I5=π1σ1, V5=bI5ε. |
We obtain
dΦ5dt=(1−S5S)(αS5−αS)+(η1S5V5+η2S5I5+η3S5Y5)(1−S5S)−λ(1−β)βγ+λη1S5V5SVL5S5V5L−λ(1−β)βγ+λη2S5I5SIL5S5I5L+λ(1−β)βγ+λ(η1S5V5+η2S5I5)−β(γ+λ)βγ+λη1S5V5SVI5S5V5I−β(γ+λ)βγ+λη2S5I5SS5−λ(1−β)βγ+λ(η1S5V5+η2S5I5)LI5L5I+η1S5V5+η2S5I5−η3S5Y5SYE5S5Y5E+η3S5Y5−η3S5Y5EY5E5Y+η3S5Y5−η1S5V5IV5I5V+η1S5V5+μ2(ψ+ω)φψ(Y5−π2σ2)CY=−α(S−S5)2S+λ(1−β)βγ+λη1S5V5(4−S5S−SVL5S5V5L−LI5L5I−IV5I5V)+λ(1−β)βγ+λη2S5I5(3−S5S−SIL5S5I5L−LI5L5I)+β(γ+λ)βγ+λη1S5V5(3−S5S−SVI5S5V5I−IV5I5V)+β(γ+λ)βγ+λη2S5I5(2−S5S−SS5)+η3S5Y5(3−S5S−SYE5S5Y5E−EY5E5Y)+μ2(ψ+ω)φψ(Y5−π2σ2)CY. | (5.8) |
Then, Eq (5.8) will be reduced to the form
dΦ5dt=−(α+βη2I5(γ+λ)βγ+λ)(S−S5)2S+λ(1−β)βγ+λη1S5V5(4−S5S−SVL5S5V5L−LI5L5I−IV5I5V)+λ(1−β)βγ+λη2S5I5(3−S5S−SIL5S5I5L−LI5L5I)+β(γ+λ)βγ+λη1S5V5(3−S5S−SVI5S5V5I−IV5I5V)+η3S5Y5(3−S5S−SYE5S5Y5E−EY5E5Y)+μ2(ψ+ω)[π1σ2(η1b+η2ε)+π2η3εσ1+αεσ1σ2]φψη3εσ1σ2(ℜ8−1)CY. |
If ℜ8≤1, then using inequalities (5.1)–(5.4) we get dΦ5dt≤0 for all S,L,I,E,Y,V,CI,CY>0, where dΦ5dt=0 at S=S5, L=L5, I=I5, E=E5, Y=Y5, V=V5 and CY=0. The solutions of system (2.1) converge to Υ′5 the largest invariant subset of Υ5={(S,L,I,E,Y,V,CI,CY):dΦ5dt=0}. The set Υ′5 contains elements with S=S5, L=L5, I=I5, V=V5, and then ˙I=0. Third equation of system (2.1) implies
0=˙I=β(η1S5V5+η2S5I5)+λL5−aI5−μ1CII5, |
which gives CI(t)=CI5 for all t. Therefore, Υ′5={Đ5}. Applying Lyapunov-LaSalle asymptotic stability theorem we get Đ5 is G.A.S.
Theorem 7. If ℜ6>1, ℜ7≤1 and ℜ2/ℜ1>1, then Đ6 is G.A.S.
Proof. Define Φ6(S,L,I,E,Y,V,CI,CY) as:
Φ6=S6ϝ(SS6)+λβγ+λL6ϝ(LL6)+γ+λβγ+λI6ϝ(II6)+1φE6ϝ(EE6)+ψ+ωφψY6ϝ(YY6)+η1S6εV6ϝ(VV6)+μ1(γ+λ)σ1(βγ+λ)CI+μ2(ψ+ω)φψσ2CY6ϝ(CYCY6). |
Calculating dΦ6dt as:
dΦ6dt=(1−S6S)[ρ−αS−η1SV−η2SI−η3SY]+λβγ+λ(1−L6L)[(1−β)(η1SV+η2SI)−(λ+γ)L]+γ+λβγ+λ(1−I6I)[β(η1SV+η2SI)+λL−aI−μ1CII]+1φ(1−E6E)[φη3SY−(ψ+ω)E]+ψ+ωφψ(1−Y6Y)[ψE−δY−μ2CYY]+η1S6ε(1−V6V)[bI−εV]+μ1(γ+λ)σ1(βγ+λ)[σ1CII−π1CI]+μ2(ψ+ω)φψσ2(1−CY6CY)[σ2CYY−π2CY]=(1−S6S)(ρ−αS)+η2S6I+η3S6Y−λ(1−β)βγ+λ(η1SV+η2SI)L6L+λ(γ+λ)βγ+λL6−a(γ+λ)βγ+λI−β(γ+λ)βγ+λ(η1SV+η2SI)I6I−λ(γ+λ)βγ+λLI6I+a(γ+λ)βγ+λI6+μ1(γ+λ)βγ+λCII6−η3SYE6E+ψ+ωφE6−δ(ψ+ω)φψY−ψ+ωφEY6Y+δ(ψ+ω)φψY6+μ2(ψ+ω)φψCYY6+η1S6bIε−η1S6V6bIεV+η1S6V6−μ1π1(γ+λ)σ1(βγ+λ)CI−μ2π2(ψ+ω)φψσ2CY−μ2(ψ+ω)φψCY6Y+μ2π2(ψ+ω)φψσ2CY6. |
Using the equilibrium conditions for Đ6:
ρ=αS6+η1S6V6+η2S6I6+η3S6Y6,λ(1−β)βγ+λ(η1S6V6+η2S6I6)=λ(γ+λ)βγ+λL6, Y6=π2σ2, V6=bI6ε,η1S6V6+η2S6I6=a(γ+λ)βγ+λI6,η3S6Y6=ψ+ωφE6=δ(ψ+ω)φψY6+μ2(ψ+ω)φψCY6Y6. |
We obtain
dΦ6dt=(1−S6S)(αS6−αS)+(η1S6V6+η2S6I6+η3S6Y6)(1−S6S)−λ(1−β)βγ+λη1S6V6SVL6S6V6L−λ(1−β)βγ+λη2S6I6SIL6S6I6L+λ(1−β)βγ+λ(η1S6V6+η2S6I6)−β(γ+λ)βγ+λη1S6V6SVI6S6V6I−β(γ+λ)βγ+λη2S6I6SS6−λ(1−β)βγ+λ(η1S6V6+η2S6I6)LI6L6I+η1S6V6+η2S6I6−η3S6Y6SYE6S6Y6E+η3S6Y6−η3S6Y6EY6E6Y+η3S6Y6−η1S6V6IV6I6V+η1S6V6+μ1(γ+λ)βγ+λ(I6−π1σ1)CI=−α(S−S6)2S+λ(1−β)βγ+λη1S6V6(4−S6S−SVL6S6V6L−LI6L6I−IV6I6V)+λ(1−β)βγ+λη2S6I6(3−S6S−SIL6S6I6L−LI6L6I)+β(γ+λ)βγ+λη1S6V6(3−S6S−SVI6S6V6I−IV6I6V)+β(γ+λ)βγ+λη2S6I6(2−S6S−SS6)+η3S6Y6(3−S6S−SYE6S6Y6E−EY6E6Y)+μ1(γ+λ)βγ+λ(I6−π1σ1)CI. | (5.9) |
Then, Eq (5.9) will be reduced to the form
dΦ6dt=−(α+βη2I6(γ+λ)βγ+λ)(S−S6)2S+λ(1−β)βγ+λη1S6V6(4−S6S−SVL6S6V6L−LI6L6I−IV6I6V)+λ(1−β)βγ+λη2S6I6(3−S6S−SIL6S6I6L−LI6L6I)+β(γ+λ)βγ+λη1S6V6(3−S6S−SVI6S6V6I−IV6I6V)+η3S6Y6(3−S6S−SYE6S6Y6E−EY6E6Y)+μ1(γ+λ)[π1σ2(η1b+η2ε)+π2η3εσ1+αεσ1σ2]σ1σ2(βγ+λ)(η1b+η2ε)(ℜ7−1)CI. |
Therefore, if ℜ7≤1, then using inequalities (5.1)–(5.4) we get dΦ6dt≤0 for all S,L,I,E,Y,V,CI,CY>0, where dΦ6dt=0 at S=S6, L=L6, I=I6, E=E6, Y=Y6, V=V6 and CI=0. Define Υ6={(S,L,I,E,Y,V,CI,CY):dΦ6dt=0} and let Υ′6 be the largest invariant subset of Υ6. The solutions of system (2.1) tend to Υ′6 which includes elements with E=E6, Y=Y6, and then ˙Y=0. The fifth equation of system (2.1) implies
0=˙Y=ψE6−δY6−μ2CYY6, |
which ensures that CY(t)=CY6 for all t. Therefore, Υ′6={Đ6}. Applying Lyapunov-LaSalle asymptotic stability theorem we get Đ6 is G.A.S.
Theorem 8. If ℜ7>1 and ℜ8>1, then Đ7 is G.A.S.
Proof. Define Φ7(S,L,I,E,Y,V,CI,CY) as:
Φ7=S7ϝ(SS7)+λβγ+λL7ϝ(LL7)+γ+λβγ+λI7ϝ(II7)+1φE7ϝ(EE7)+ψ+ωφψY7ϝ(YY7)+η1S7εV7ϝ(VV7)+μ1(γ+λ)σ1(βγ+λ)CI7ϝ(CICI7)+μ2(ψ+ω)φψσ2CY7ϝ(CYCY7). |
Calculating dΦ7dt and after collecting terms we get
dΦ7dt=(1−S7S)(ρ−αS)+η2S7I+η3S7Y−λ(1−β)βγ+λ(η1SV+η2SI)L7L+λ(γ+λ)βγ+λL7−a(γ+λ)βγ+λI−β(γ+λ)βγ+λ(η1SV+η2SI)I7I−λ(γ+λ)βγ+λLI7I+a(γ+λ)βγ+λI7+μ1(γ+λ)βγ+λCII7−η3SYE7E+ψ+ωφE7−δ(ψ+ω)φψY−ψ+ωφEY7Y+δ(ψ+ω)φψY7+μ2(ψ+ω)φψCYY7+η1S7bIε−η1S7V7bIεV+η1S7V7−μ1π1(γ+λ)σ1(βγ+λ)CI−μ1(γ+λ)βγ+λCI7I+μ1π1(γ+λ)σ1(βγ+λ)CI7−μ2π2(ψ+ω)φψσ2CY−μ2(ψ+ω)φψCY7Y+μ2π2(ψ+ω)φψσ2CY7. |
Using the equilibrium conditions for Đ7:
ρ=αS7+η1S7V7+η2S7I7+η3S7Y7, λ(1−β)βγ+λ(η1S7V7+η2S7I7)=λ(γ+λ)βγ+λL7,η1S7V7+η2S7I7=a(γ+λ)βγ+λI7+μ1(γ+λ)βγ+λCI7I7, I7=π1σ1, Y7=π2σ2, V7=bI7εη3S7Y7=ψ+ωφE7=δ(ψ+ω)φψY7+μ2(ψ+ω)φψCY7Y7. |
We obtain
dΦ7dt=(1−S7S)(αS7−αS)+(η1S7V7+η2S7I7+η3S7Y7)(1−S7S)−λ(1−β)βγ+λη1S7V7SVL7S7V7L−λ(1−β)βγ+λη2S7I7SIL7S7I7L+λ(1−β)βγ+λ(η1S7V7+η2S7I7)−β(γ+λ)βγ+λη1S7V7SVI7S7V7I−β(γ+λ)βγ+λη2S7I7SS7−λ(1−β)βγ+λ(η1S7V7+η2S7I7)LI7L7I+η1S7V7+η2S7I7−η3S7Y7SYE7S7Y7E+η3S7Y7−η3S7Y7EY7E7Y+η3S7Y7−η1S7V7IV7I7V+η1S7V7=−(α+βη2I7(γ+λ)βγ+λ)(S−S7)2S+λ(1−β)βγ+λη1S7V7(4−S7S−SVL7S7V7L−LI7L7I−IV7I7V)+λ(1−β)βγ+λη2S7I7(3−S7S−SIL7S7I7L−LI7L7I)+β(γ+λ)βγ+λη1S7V7(3−S7S−SVI7S7V7I−IV7I7V)+η3S7Y7(3−S7S−SYE7S7Y7E−EY7E7Y). |
Therefore, using inequalities (5.1)–(5.4) we get dΦ7dt≤0 for all S,L,I,E,Y,V,CI,CY>0. In addition we have dΦ7dt=0 at S=S7, L=L7, I=I7, E=E7, Y=Y7 and V=V7. The solutions of system (2.1) converge to Υ′7 the largest invariant subset of Υ7={(S,L,I,E,Y,V,CI,CY):dΦ7dt=0}. The set Υ′7 contains elements with S=S7, L=L7, I=I7, E=E7, Y=Y7 and V=V7. Then ˙I=˙Y=0 and from the third and fifth equations of system (2.1) we get
0=˙I=β(η1S7V7+η2S7I7)+λL7−aI7−μ1CII7,0=˙Y=ψE7−δY7−μ2CYY7, |
which ensure that CI(t)=CI7 and CY(t)=CY7 for all t. Therefore, Υ′7={Đ7}. Applying Lyapunov-LaSalle asymptotic stability theorem we get Đ7 is G.A.S.
In Table 3, we summarize the global stability results given in Theorems 1–8.
Equilibrium point | Global stability conditions |
Đ0=(S0,0,0,0,0,0,0,0) | ℜ1≤1 and ℜ2≤1 |
Đ1=(S1,L1,I1,0,0,V1,0,0) | ℜ1>1, ℜ2/ℜ1≤1 and ℜ3≤1 |
Đ2=(S2,0,0,E2,Y2,0,0,0) | ℜ2>1, ℜ1/ℜ2≤1 and ℜ4≤1 |
Đ3=(S3,L3,I3,0,0,V3,CI3,0) | ℜ3>1 and ℜ5≤1 |
Đ4=(S4,0,0,E4,Y4,0,0,CY4) | ℜ4>1 and ℜ6≤1 |
Đ5=(S5,L5,I5,E5,Y5,V5,CI5,0) | ℜ5>1, ℜ8≤1 and ℜ1/ℜ2>1 |
Đ6=(S6,L6,I6,E6,Y6,V6,0,CY6) | ℜ6>1, ℜ7≤1 and ℜ2/ℜ1>1 |
Đ7=(S7,L7,I7,E7,Y7,V7,CI7,CY7) | ℜ7>1 and ℜ8>1 |
In this section, we illustrate the results of Theorems 1–8 by performing numerical simulations. Moreover, we study the effect of HTLV-I infection on the HIV mono-infected individuals by making a comparison between the dynamics of HIV mono-infection and HIV/HTLV-I co-infection. Otherwise, we investigate the influence of HIV infection on the HTLV-I mono-infected individuals by conducting a comparison between the dynamics of HTLV-I mono-infection and HIV/HTLV-I co-infection.
To solve system (2.1) numerically we fix the values of some parameters (see Table 4) and the others will be varied.
Parameter | Value | Parameter | Value | Parameter | Value |
ρ | 10 | δ | 0.2 | β | 0.7 |
α | 0.01 | b | 5 | γ | 0.02 |
η1 | Varied | π1 | 0.1 | σ1 | Varied |
η2 | Varied | π2 | 0.1 | σ2 | Varied |
η3 | Varied | μ1 | 0.2 | λ | 0.2 |
a | 0.5 | μ2 | 0.2 | ω | 0.01 |
φ | 0.2 | ε | 2 | ψ | 0.003 |
In this subsection, we choose the following three different initial conditions for system (2.1):
Initial-1 :(S(0),L(0),I(0),E(0),Y(0),V(0),CI(0),CY(0))=(600,1.5,1.5,30,0.3,5,1,3),
Initial-2:(S(0),L(0),I(0),E(0),Y(0),V(0),CI(0),CY(0))=(500,1,1,20,0.2,2,2,2),
Initial-3:(S(0),L(0),I(0),E(0),Y(0),V(0),CI(0),CY(0))=(300,0.5,0.5,10,0.1,1.5,3,1).
Choosing selected values of η1, η2, η3, σ1 and σ2 under the above initial conditions leads to the following scenarios:
Scenario 1 (Stability of Đ0): η1=η2=0.0001, η3=0.001 and σ1=σ2=0.2. For this set of parameters, we have ℜ1=0.68<1 and ℜ2=0.23<1. Figure 2 displays that the trajectories initiating with Initial-1, Initial-2 and Initial-3 reach the equilibrium Đ0=(1000,0,0,0,0,0,0,0). This shows that Đ0 is G.A.S according to Theorem 1. In this situation both HIV and HTLV will be died out.
Scenario 2 (Stability of Đ1): η1=0.0005, η2=0.0003, η3=0.0005, σ1=0.003 and σ2=0.2. With such choice we get ℜ2=0.12<1<3.02=ℜ1, ℜ3=0.49<1 and hence ℜ2/ℜ1=0.04<1. Therefore, the conditions in Table 2 is verified. In fact, the equilibrium point Đ1 exists with Đ1=(331.63,9.11,13,0,0,32.51,0,0). Figure 3 displays that the trajectories initiating with Initial-1, Initial-2 and Initial-3 tend to Đ1. Therefore, the numerical results support Theorem 2. This case corresponds to a chronic HIV mono-infection but with unstimulated CTL-mediated immune response.
Scenario 3 (Stability of Đ2): η1=0.0001, η2=0.0002, η3=0.01, σ1=0.001 and σ2=0.05. Then, we calculate ℜ1=0.88<1<2.31=ℜ2, ℜ4=0.77<1 and then ℜ1/ℜ2=0.38<1. Hence, the conditions in Table 2 is satisfied. The numerical results show that Đ2=(433.33,0,0,87.18,1.31,0,0,0) exists. Figure 4 illustrates that the trajectories initiating with Initial-1, Initial-2 and Initial-3 tend to Đ2. Thus, the numerical results consistent with Theorem 3. This situation leads to a persistent HTLV mono-infection with unstimulated CTL-mediated immune response.
Scenario 4 (Stability of Đ3): η1=0.001, η2=0.0001, η3=0.005 and σ1=σ2=0.01. Then, we calculate ℜ3=1.41>1 and ℜ5=0.32<1. Table 2 and Figure 5 show that the trajectories initiating with Initial-1, Initial-2 and Initial-3 tend to Đ3=(277.78,9.85,10,0,0,25,1.01,0). Therefore, Đ3 is G.A.S and this agrees with Theorem 4. Hence, a chronic HIV mono-infection with HIV-specific CTL-mediated immune response is attained.
Scenario 5 (Stability of Đ4): η1=0.0007, η2=0.0001, η3=0.1, σ1=0.05 and σ2=0.3. Then, we calculate ℜ4=5.33>1 and ℜ6=0.83<1. According to Table 2, Đ4 exists with Đ4=(230.77,0,0,118.34,0.33,0,0,4.33). In Figure 6, we show that the trajectories initiating with Initial-1, Initial-2 and Initial-3 tend to Đ4 and then it is G.A.S which agrees with Theorem 5. Hence, a chronic HTLV mono-infection with HTLV-specific CTL-mediated immune response is attained.
Scenario 6 (Stability of Đ5): η1=0.001, η2=0.0001, η3=0.01, σ1=0.05 and σ2=0.08. Then, we calculate ℜ5=1.52>1, ℜ8=0.83<1 and ℜ1/ℜ2=2.19>1. Table 2 and the numerical results demonstrated in Figure 7 show that Đ5=(433.33,3.07,2,52.51,0.79,5,2.98,0) exists and it is G.A.S and this agrees with Theorem 6. As a result, a chronic co-infection with HIV and HTLV is attained where the HIV-specific CTL-mediated immune response is active and the HTLV-specific CTL-mediated immune response is unstimulated.
Scenario 7 (Stability of Đ6): η1=0.0006, η2=0.0001, η3=0.04, σ1=0.01 and σ2=0.5. We compute ℜ6=1.73>1, ℜ7=0.92<1 and ℜ2/ℜ1=2.97>1. Based on the conditions in Table 2, the equilibrium Đ6=(321.26,5.75,8.2,39.54,0.2,20.51,0,1.97) exists. Moreover, the numerical results plotted in Figure 8 show that Đ6 is G.A.S and this illustrates Theorem 7. As a result, a chronic co-infection with HIV and HTLV is attained where the HTLV-specific CTL-mediated immune response is active and the HIV-specific CTL-mediated immune response is unstimulated.
Scenario 8 (Stability of Đ7): η1=0.0006, η2=0.0002, η3=0.04, σ1=0.05 and σ2=0.5. These data give ℜ7=1.55>1 and ℜ8=4.31>1. According to Table 2, the equilibrium Đ7 exists. Figure 9 illustrates that the trajectories initiating with Initial-1, Initial-2 and Initial-3 tend to Đ7=(467.29,2.17,2,57.51,0.2,5,1.36,3.31). The numerical results displayed in Figure 9 show that Đ7 is G.A.S based on Theorem 8. In this case, a chronic co-infection with HIV and HTLV is attained where both HIV-specific CTL-mediated and HTLV-specific CTL-mediated immune responses are working.
To further confirmation, we calculate the Jacobian matrix J=J(S,L,I,E,Y,V,CI,CY) of system (2.1) as in the following form:
J=(−(α+η1V+η2I+η3Y)0−η2S0−η3S−η1S00(1−β)(η1V+η2I)−(γ+λ)(1−β)η2S00(1−β)η1S00β(η1V+η2I)λβη2S−(a+μ1CI)00βη1S−μ1I0φη3Y00−(ψ+ω)φη3S000000ψ−(δ+μ2CY)00−μ2Y00b00−ε0000σ1CI000σ1I−π100000σ2CY00σ2Y−π2). |
Then, we calculate the eigenvalues λi, i=1,2,...,8 of the matrix J at each equilibrium. The examined steady will be locally stable if all its eigenvalues satisfy the following condition:
Re(λi)<0, i=1,2,...,8. |
We use the parameters η1, η2, η3, σ1 and σ2 the same as given above to compute all positive equilibria and the corresponding eigenvalues. From the scenarios 1–8, we present in Table 5 the positive equilibria, the real parts of the eigenvalues and whether the equilibrium is locally stable or unstable.
Scenario | The equilibria | (Re(λi), i=1,2,...,6) | Stability |
1 | Đ0=(1000,0,0,0,0,0,0,0) | (−2.19,−0.37,−0.2,−0.1,−0.1,−0.09,−0.01,−0.01) | stable |
2 | Đ0=(1000,0,0,0,0,0,0,0)Đ1=(331.63,9.11,13,0,0,32.51,0,0) | (−2.7,0.51,−0.32,−0.2,−0.1,−0.1,−0.01,−0.01)(−2.3,−0.35,−0.2,−0.1,−0.02,−0.02,−0.06,−0.01) | unstablestable |
3 | Đ0=(1000,0,0,0,0,0,0,0)Đ2=(433.33,0,0,87.18,1.31,0,0,0) | (−2.18,−0.36,−0.23,−0.1,−0.1,−0.03,0.01,−0.01)(−2.09,−0.41,−0.21,−0.16,−0.1,−0.03,−0.01,−0.01) | unstablestable |
4 | Đ0=(1000,0,0,0,0,0,0,0)Đ1=(197.7,10.94,15.61,0,0,39.02,0,0)Đ2=(866.67,0,0,20.51,0.31,0,0,0)Đ3=(277.78,9.85,10,0,0,25,1.01,0) | (−3.22,0.88,−0.31,−0.21,−0.1,−0.1,−0.01,0.002)(−2.36,−0.35,−0.2,−0.03,−0.03,−0.1,0.06,−0.01)(−3.1,0.76,−0.32,−0.21,−0.1,−0.1,−0.01,−0.002)(−2.51,−0.37,−0.2,−0.02,−0.02,−0.1,−0.02,−0.01) | unstableunstableunstablestable |
5 | Đ0=(1000,0,0,0,0,0,0,0)Đ1=(277.85,9.85,14.05,0,0,35.12,0,0)Đ2=(43.33,0,0,147.18,2.21,0,0,0)Đ3=(729.93,3.68,2,0,0,5,4.07,0)Đ4=(230.77,0,0,118.34,0.33,0,0,4.33) | (−2.94,0.61,−0.37,−0.32,0.16,−0.1,−0.1,−0.01)(−2.35,0.6,−0.35,−0.27,−0.1,−0.02,−0.02,0.05)(−2.06,0.56,−0.45,−0.27,−0.2,−0.16,−0.1,−0.01)(−2.99,−0.43,−0.34,−0.03,−0.03,0.12,−0.1,−0.01)(−2.3,−0.99,−0.36,−0.1,−0.06,−0.06,−0.05,−0.01) | unstableunstableunstableunstablestable |
6 | Đ0=(1000,0,0,0,0,0,0,0)Đ1=(197.7,10.94,15.61,0,0,39.02,0,0)Đ2=(433.33,0,0,87.18,1.31,0,0,0)Đ3=(657.89,4.67,2,0,0,5,5.82,0)Đ4=(444.44,0,0,85.47,1.25,0,0,0.03)Đ5=(433.33,3.07,2,52.51,0.79,5,2.98,0) | (−3.22,0.88,−0.31,−0.23,−0.1,−0.1,0.01,−0.01)(−2.36,0.68,−0.35,−0.21,−0.03,−0.03,−0.1,−0.01)(−2.67,−0.33,0.3,−0.21,−0.1,−0.01,−0.01,0.005)(−3.3,−0.45,−0.22,−0.05,−0.05,−0.1,−0.01,0.01)(−2.68,−0.32,0.32,−0.22,−0.1,−0.01,−0.01,−0.005)(−2.83,−0.41,−0.21,−0.03,−0.03,−0.04,−0.01,−0.01) | unstableunstableunstableunstableunstablestable |
7 | Đ0=(1000,0,0,0,0,0,0,0)Đ1=(321.26,9.26,13.2,0,0,33.01,0,0)Đ2=(108.33,0,0,137.18,2.06,0,0,0)Đ3=(384.62,8.39,10,0,0,25,0.49,0)Đ4=(555.56,0,0,68.38,0.2,0,0,4.13)Đ6=(321.26,5.75,8.2,39.54,0.2,20.51,0,1.97) | (−2.84,0.51,−0.32,−0.29,−0.1,−0.1,0.07,−0.01)(−2.35,−0.35,−0.23,−0.1,−0.02,−0.02,0.03,0.02)(−2.13,0.93,−0.42,−0.22,−0.16,−0.1,−0.07,−0.01)(−2.42,−0.36,−0.24,−0.1,−0.01,−0.01,0.03,−0.01)(−2.54,−0.95,−0.33,0.19,−0.1,−0.07,−0.03,−0.01)(−2.35,−0.53,−0.35,−0.02,−0.02,−0.05,−0.02,−0.02) | unstableunstableunstableunstableunstablestable |
8 | Đ0=(1000,0,0,0,0,0,0,0)Đ1=(302.36,9.51,13.57,0,0,33.93,0,0)Đ2=(108.33,0,0,137.18,2.06,0,0,0)Đ3=(746.27,3.46,2,0,0,5,3.67,0)Đ4=(555.56,0,0,68.38,0.2,0,0,4.13)Đ6=(302.36,6.21,8.87,37.21,0.2,22.17,0,1.79)Đ7=(467.29,2.17,2,57.51,0.2,5,1.36,3.31) | (−2.83,0.56,−0.32,−0.29,−0.1,−0.1,0.07,−0.01)(−2.33,0.58,−0.35,−0.23,−0.1,−0.02,−0.02,0.02)(−2.13,0.93,−0.41,−0.22,−0.16,−0.1,−0.07,−0.01)(−2.86,−0.42,−0.27,−0.03,−0.03,−0.1,0.06,−0.01)(−2.53,−0.95,−0.33,0.22,−0.1,−0.07,−0.03,−0.01)(−2.33,−0.5,−0.35,0.34,−0.02,−0.02,−0.05,−0.02)(−2.52,−0.79,−0.38,−0.01,−0.01,−0.06,−0.03,−0.01) | unstableunstableunstableunstableunstableunstablestable |
In this subsection, we study the influence of HTLV-I infection on HIV mono-infection dynamics, and how affect the HIV infection on the dynamics of HTLV-I mono-infection as well.
To investigate the effect of HTLV-I infection on HIV mono-infection dynamics, we make a comparison between model (2.1) and the following HIV mono-infection model:
{˙S=ρ−αS−η1SV−η2SI,˙L=(1−β)(η1SV+η2SI)−(λ+γ)L,˙I=βη1(η1SV+η2SI)+λL−aI−μ1CII,˙V=bI−εV,˙CI=σ1CII−π1CI. | (6.1) |
We fix parameters η1=0.0006, η2=0.0001, σ1=0.05, and σ2=0.5 and consider the following initial condition:
Initial-4: (S(0),L(0),I(0),E(0),Y(0),V(0),CI(0),CY(0))=(600,2.4,1.8,60,0.2,4.5,1.8,3.5).
We choose two values of the parameter η3 as η3=0.04 (HIV/HTLV-I co-infection), and η3=0.0 (HIV mono-infection). It can be seen from Figure 10 that when the HIV mono-infected individual is co-infected with HTLV-I then the concentrations of susceptible CD4+T cells, latently HIV-infected cells and HIV-specific CTLs are decreased. Although, the concentration of free HIV particles tend to the same value in both HIV mono-infection and HIV/HTLV-I co-infection. Indeed, such observation are compatible with the study that has been performed by Vandormael et al. in 2017 [54]. The researchers have not found any worthy differences in the concentration of HIV virus particles in comparison between HIV mono-infected and HIV/HTLV-I co-infected patients.
To investigate the effect of HIV infection on HTLV-I mono-infection dynamics, we make a comparison between model (2.1) and the following HTLV-I mono-infection model:
{˙S=ρ−αS−η2SY,˙E=φη2SY−(ψ+ω)E,˙Y=ψE−δY−μ2CYY,˙CY=σ2CYY−π2CY. | (6.2) |
We fix parameters η3=0.01; σ1=0.05, and σ2=0.5 and consider the following initial condition:
Initial-5: (S(0),L(0),I(0),E(0),Y(0),V(0),CI(0),CY(0))=(700,4,2,21,0.198,5,4.5,0.6).
We choose two values of the parameters η1, η2 as η1= 0.001, η2=0.0002 (HIV/HTLV-I co-infection), and η1=η2=0.0 (HTLV-I mono-infection). It can be seen from Figure 11 that when the HTLV-I mono-infected individual is co-infected with HIV then the concentrations of susceptible CD4+T cells, latently HTLV-infected cells and HTLV-specific CTLs are decreased. Although, the concentration of Tax-expressing HTLV-infected cells tend to the same value in both HTLV-I mono-infection and HIV/HTLV-I co-infection.
As we discussed in Section 1 that CTLs have significant important in controlling HIV and HTLV-I mono-infections by killing infected cells. Model (2.1) in the absence of CTL immune response leads to a model with competition between HIV and HTLV-I on CD4+T cells:
{˙S=ρ−αS−η1SV−η2SI−η3SY,˙L=(1−β)(η1SV+η2SI)−(λ+γ)L,˙I=β(η1SV+η2SI)+λL−aI,˙E=φη3SY−(ψ+ω)E,˙Y=ψE−δY,˙V=bI−εV. | (6.3) |
The system has only three equilibria, infection-free equilibrium, ¯Đ0=(S0,0,0,0,0,0), chronic HIV mono-infection equilibrium, ¯Đ1=(S1,L1,I1,0,0,V1) and chronic HTLV mono-infection equilibrium, ¯Đ2=(S2,0,0,E2,Y2,0), where S0, S1, L1, I1, V1, S2, E2 and Y2 are given in Section 4. The existence of the these three equilibria is determined by two threshold parameters ℜ1 and ℜ2 which are defined in Section 4.
Corollary 1. For system (6.3), the following statements hold true.
(ⅰ) If ℜ1≤1 and ℜ2≤1, then ¯Đ0 is G.A.S.
(ⅱ) If ℜ1>1 and ℜ2/ℜ1≤1, then ¯Đ1 is G.A.S.
(ⅲ) If ℜ2>1 and ℜ1/ℜ2≤1, then ¯Đ2 is G.A.S.
Therefore, the system will tend to one of the three equilibria ¯Đ0, ¯Đ1 and ¯Đ2. The above result says that in the absence of immune response, the competition between HIV and HTLV-I consuming common resources, only one type of viruses with maximum basic reproductive ratio can survive. However, in our proposed model (2.1) involving HIV- and HTLV-specific CTLs, HIV and HTLV-I coexist at equilibrium. We can consider this situation as follows. Since CTL immune responses suppress viral progression, the competition between HIV and HTLV-I is also suppressed and the coexistence of HIV and HTLV-I is occurred [55].
This research work formulates a mathematical model which describes the within host dynamics of HIV/HTLV-I co-infection. The model incorporated the effect of HIV-specific CTLs and HTLV-specific CTLs. HIV has two predominant infection modes: the classical VTC infection and CTC spread. The HTLV-I has two ways of transmission, (ⅰ) horizontal transmission via direct CTC contact, and (ⅱ) vertical transmission through mitotic division of Tax-expressing HTLV-infected cells. We first proved that the model is well-posed by showing that the solutions are nonnegative and bounded. We derived eight threshold parameters that governed the existence and stability of the eight equilibria of the model. We constructed appropriate Lyapunov functions and applied Lyapunov-LaSalle asymptotic stability theorem to prove the global asymptotic stability of all equilibria. We conducted numerical simulations to support and clarify our theoretical results. We studied the effect of HIV infection on HTLV-I mono-infection dynamics and vice versa. The model analysis suggested that co-infected individuals with both viruses will have smaller number of healthy CD4+T cells in comparison with HIV or HTLV-I mono-infected individuals. We discussed the influence of CTL immune response on the co-infection dynamics.
Our model can be extended in many directions:
● In our model (2.1), we assumed that susceptible CD4+T cells are produced at a constant rate ρ and have a linear death rate αS. It would be more reasonable to consider the density dependent production rate. One possibility is to assume a logistic growth for the susceptible CD4+T cells in the absence of infection. Moreover, the model assumed bilinear incidence rate of infections, η1SV, η2SI and η3SY. However, such bilinear form may not describe the virus dynamics during the full course of infection. Therefore, it is reasonable to consider other forms of the incidence rate such as: saturated incidence, Beddington-DeAngelis incidence and general incidence [56,57,58].
● Model (2.1) assumed that once susceptible CD4+T cell is contacted by an HIV or HIV-infected or HTLV-infected cell it becomes latently or actively infected instantaneously. However, such process needs time. Intracellular time delay has a significant effect on the virus dynamics. Delayed viral infection models have been constructed and analyzed in several works (see, e.g., [59,60,61,62,63,64]).
● Model (2.1) assumes that cells and viruses are equally distributed in the domain with no spatial variations. Taking into account spatial variations in the case of HIV/HTLV-I co-infection will be significant [65,66].
We leave these extensions as a future project.
This project was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, Saudi Arabia under grant no. (KEP-PhD-20-130-41). The authors, therefore, acknowledge with thanks DSR technical and financial support.
On behalf of all authors, the corresponding author states that there is no conflict of interest.
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Parameter | Description |
ρ | Recruitment rate for the susceptible CD4+T cells |
α | Natural mortality rate constant for the susceptible CD4+T cells |
η1 | Virus-cell incidence rate constant between free HIV particles and susceptible CD4+T cells |
η2 | Cell-cell incidence rate constant between HIV-infected cells and susceptible CD4+T cells |
η3 | Cell-cell incidence rate constant between Tax-expressing HTLV-infected cells andsusceptible CD4+T cells |
β∈(0,1) | Fraction coefficient accounts for the probability of new HIV-infected cells could be active, and the remaining part 1−β will be latent |
γ | Death rate constant of latently HIV-infected cells |
a | Death rate constant of actively HIV-infected cells |
μ1 | Killing rate constant of actively HIV-infected cells due to HIV-specific CTLs |
μ2 | Killing rate constant of Tax-expressing HTLV-infected cells due to HTLV-specific CTLs |
φ∈(0,1) | Probability of new HTLV infections could be enter a latent period |
λ | Transmission rate constant of latently HIV-infected cells that become actively HIV-infected cells |
ψ | Transmission rate constant of latently HTLV-infected cells that become Tax-expressing HTLV-infected cells |
ω | Death rate constant of latently HTLV-infected cells |
δ | Death rate constant of Tax-expressing HTLV-infected cells |
b | Generation rate constant of new HIV particles |
ε | Death rate constant of free HIV particles |
σ1 | Proliferation rate constant of HIV-specific CTLs |
σ2 | Proliferation rate constant of HTLV-specific CTLs |
π1 | Decay rate constant of HIV-specific CTLs |
π2 | Decay rate constant of HTLV-specific CTLs |
Equilibrium point | Definition | Existence conditions |
Đ0=(S0,0,0,0,0,0,0,0) | Infection-free equilibrium | None |
Đ1=(S1,L1,I1,0,0,V1,0,0) | Chronic HIV mono-infection equilibriumwith inactive immune response | ℜ1>1 |
Đ2=(S2,0,0,E2,Y2,0,0,0) | Chronic HTLV mono-infection equilibriumwith inactive immune response | ℜ2>1 |
Đ3=(S3,L3,I3,0,0,V3,CI3,0) | Chronic HIV mono-infection equilibriumwith only active HIV-specific CTL | ℜ3>1 |
Đ4=(S4,0,0,E4,Y4,0,0,CY4) | Chronic HTLV mono-infection equilibriumwith only active HTLV-specific CTL | ℜ4>1 |
Đ5=(S5,L5,I5,E5,Y5,V5,CI5,0) | \begin{array} [c]{l} \text{Chronic HIV/HTLV co-infection equilibrium} \; \text{with only active HIV-specific CTL} \end{array} | \Re_{5} > 1 and \Re_{1}/\Re_{2} > 1 |
Đ _{6}=(S_{6}, L_{6}, I_{6}, E_{6}, Y_{6}, V_{6}, 0, C_{6}^{Y}) | \begin{array} [c]{l} \text{Chronic HIV/HTLV co-infection equilibrium} \; \text{with only active HTLV-specific CTL} \end{array} | \Re_{6} > 1 and \Re_{2}/\Re_{1} > 1 |
Đ _{7}=(S_{7}, L_{7}, I_{7}, E_{7}, Y_{7}, V_{7}, C_{7}^{I}, C_{7}^{Y}) | \begin{array} [c]{l} \text{Chronic HIV/HTLV co-infection} \; \text{equilibrium with active HIV-specific} \\ \text{CTL and HTLV-specific CTL} \end{array} | \Re_{7} > 1 and \Re_{8} > 1 |
Equilibrium point | Global stability \text{ conditions} |
Đ _{0}=(S_{0}, 0, 0, 0, 0, 0, 0, 0) | \Re_{1}\leq1 and \Re_{2}\leq1 |
Đ _{1}=(S_{1}, L_{1}, I_{1}, 0, 0, V_{1}, 0, 0) | \Re_{1} > 1 , \Re_{2}/\Re _{1}\leq1 and \Re_{3}\leq1 |
Đ _{2}=(S_{2}, 0, 0, E_{2}, Y_{2}, 0, 0, 0) | \Re_{2} > 1 , \Re_{1}/\Re_{2}\leq1 and \Re_{4}\leq1 |
Đ _{3}=(S_{3}, L_{3}, I_{3}, 0, 0, V_{3}, C_{3}^{I}, 0) | \Re_{3} > 1 and \Re_{5}\leq1 |
Đ _{4}=(S_{4}, 0, 0, E_{4}, Y_{4}, 0, 0, C_{4}^{Y}) | \Re_{4} > 1 and \Re _{6}\leq1 |
Đ _{5}=(S_{5}, L_{5}, I_{5}, E_{5}, Y_{5}, V_{5}, C_{5}^{I}, 0) | \Re_{5} > 1 , \Re_{8}\leq1 and \Re_{1}/\Re_{2} > 1 |
Đ _{6}=(S_{6}, L_{6}, I_{6}, E_{6}, Y_{6}, V_{6}, 0, C_{6}^{Y}) | \Re_{6} > 1 , \Re_{7}\leq1 and \Re_{2}/\Re_{1} > 1 |
Đ _{7}=(S_{7}, L_{7}, I_{7}, E_{7}, Y_{7}, V_{7}, C_{7}^{I}, C_{7}^{Y}) | \Re_{7} > 1 and \Re_{8} > 1 |
Parameter | Value | Parameter | Value | Parameter | Value |
\rho | 10 | \delta | 0.2 | \beta | 0.7 |
\alpha | 0.01 | b | 5 | \gamma | 0.02 |
\eta_{1} | Varied | \pi_{1} | 0.1 | \sigma_{1} | Varied |
\eta_{2} | Varied | \pi_{2} | 0.1 | \sigma_{2} | Varied |
\eta_{3} | Varied | \mu_{1} | 0.2 | \lambda | 0.2 |
a | 0.5 | \mu_{2} | 0.2 | \omega | 0.01 |
\varphi | 0.2 | \varepsilon | 2 | \psi | 0.003 |
Scenario | The equilibria | (\operatorname{Re}(\lambda_{i}), i=1, 2, ..., 6) | Stability |
1 | \begin{array} [c]{c} Đ _{0}=(1000, 0, 0, 0, 0, 0, 0, 0) \end{array} | \begin{array} [c]{c} (-2.19, -0.37, -0.2, -0.1, -0.1, -0.09, -0.01, -0.01) \end{array} | \begin{array} [c]{l} \text{stable} \end{array} |
2 | \begin{array} [c]{l} Đ _{0}=(1000, 0, 0, 0, 0, 0, 0, 0) \\ Đ _{1}=(331.63, 9.11, 13, 0, 0, 32.51, 0, 0) \end{array} | \begin{array} [c]{l} (-2.7, 0.51, -0.32, -0.2, -0.1, -0.1, -0.01, -0.01) \\ (-2.3, -0.35, -0.2, -0.1, -0.02, -0.02, -0.06, -0.01) \end{array} | \begin{array} [c]{l} \text{unstable} \\ \text{stable} \end{array} |
3 | \begin{array} [c]{l} Đ _{0}=(1000, 0, 0, 0, 0, 0, 0, 0) \\ Đ _{2}=(433.33, 0, 0, 87.18, 1.31, 0, 0, 0) \end{array} | \begin{array} [c]{l} (-2.18, -0.36, -0.23, -0.1, -0.1, -0.03, 0.01, -0.01) \\ (-2.09, -0.41, -0.21, -0.16, -0.1, -0.03, -0.01, -0.01) \end{array} | \begin{array} [c]{c} \text{unstable} \\ \text{stable} \end{array} |
4 | \begin{array} [c]{l} Đ _{0}=(1000, 0, 0, 0, 0, 0, 0, 0) \\ Đ _{1}=(197.7, 10.94, 15.61, 0, 0, 39.02, 0, 0) \\ Đ _{2}=(866.67, 0, 0, 20.51, 0.31, 0, 0, 0) \\ Đ _{3}=(277.78, 9.85, 10, 0, 0, 25, 1.01, 0) \end{array} | \begin{array} [c]{l} (-3.22, 0.88, -0.31, -0.21, -0.1, -0.1, -0.01, 0.002) \\ (-2.36, -0.35, -0.2, -0.03, -0.03, -0.1, 0.06, -0.01) \\ (-3.1, 0.76, -0.32, -0.21, -0.1, -0.1, -0.01, -0.002) \\ (-2.51, -0.37, -0.2, -0.02, -0.02, -0.1, -0.02, -0.01) \end{array} | \begin{array} [c]{c} \text{unstable} \\ \text{unstable} \\ \text{unstable} \\\text{stable} \end{array} |
5 | \begin{array} [c]{l} Đ _{0}=(1000, 0, 0, 0, 0, 0, 0, 0) \\ Đ _{1}=(277.85, 9.85, 14.05, 0, 0, 35.12, 0, 0) \\ Đ _{2}=(43.33, 0, 0,147.18, 2.21, 0, 0, 0) \\ Đ _{3}=(729.93, 3.68, 2, 0, 0, 5, 4.07, 0) \\ Đ _{4}=(230.77, 0, 0,118.34, 0.33, 0, 0, 4.33) \end{array} | \begin{array} [c]{l} (-2.94, 0.61, -0.37, -0.32, 0.16, -0.1, -0.1, -0.01) \\ (-2.35, 0.6, -0.35, -0.27, -0.1, -0.02, -0.02, 0.05) \\ (-2.06, 0.56, -0.45, -0.27, -0.2, -0.16, -0.1, -0.01) \\ (-2.99, -0.43, -0.34, -0.03, -0.03, 0.12, -0.1, -0.01) \\ (-2.3, -0.99, -0.36, -0.1, -0.06, -0.06, -0.05, -0.01) \end{array} | \begin{array} [c]{c} \text{unstable} \\ \text{unstable} \\ \text{unstable} \\ \text{unstable} \\ \text{stable} \end{array} |
6 | \begin{array} [c]{l} Đ _{0}=(1000, 0, 0, 0, 0, 0, 0, 0) \\ Đ _{1}=(197.7, 10.94, 15.61, 0, 0, 39.02, 0, 0) \\ Đ _{2}=(433.33, 0, 0, 87.18, 1.31, 0, 0, 0) \\ Đ _{3}=(657.89, 4.67, 2, 0, 0, 5, 5.82, 0) \\ Đ _{4}=(444.44, 0, 0, 85.47, 1.25, 0, 0, 0.03) \\ Đ _{5}=(433.33, 3.07, 2, 52.51, 0.79, 5, 2.98, 0) \end{array} | \begin{array} [c]{l} (-3.22, 0.88, -0.31, -0.23, -0.1, -0.1, 0.01, -0.01) \\ (-2.36, 0.68, -0.35, -0.21, -0.03, -0.03, -0.1, -0.01) \\ (-2.67, -0.33, 0.3, -0.21, -0.1, -0.01, -0.01, 0.005) \\ (-3.3, -0.45, -0.22, -0.05, -0.05, -0.1, -0.01, 0.01) \\ (-2.68, -0.32, 0.32, -0.22, -0.1, -0.01, -0.01, -0.005) \\ (-2.83, -0.41, -0.21, -0.03, -0.03, -0.04, -0.01, -0.01) \end{array} | \begin{array} [c]{c} \text{unstable} \\ \text{unstable} \\ \text{unstable} \\ \text{unstable} \\ \text{unstable} \\ \text{stable} \end{array} |
7 | \begin{array} [c]{l} Đ _{0}=(1000, 0, 0, 0, 0, 0, 0, 0) \\ Đ _{1}=(321.26, 9.26, 13.2, 0, 0, 33.01, 0, 0) \\ Đ _{2}=(108.33, 0, 0,137.18, 2.06, 0, 0, 0) \\ Đ _{3}=(384.62, 8.39, 10, 0, 0, 25, 0.49, 0) \\ Đ _{4}=(555.56, 0, 0, 68.38, 0.2, 0, 0, 4.13) \\ Đ _{6}=(321.26, 5.75, 8.2, 39.54, 0.2, 20.51, 0, 1.97) \end{array} | \begin{array} [c]{l} (-2.84, 0.51, -0.32, -0.29, -0.1, -0.1, 0.07, -0.01) \\ (-2.35, -0.35, -0.23, -0.1, -0.02, -0.02, 0.03, 0.02) \\ (-2.13, 0.93, -0.42, -0.22, -0.16, -0.1, -0.07, -0.01) \\ (-2.42, -0.36, -0.24, -0.1, -0.01, -0.01, 0.03, -0.01) \\ (-2.54, -0.95, -0.33, 0.19, -0.1, -0.07, -0.03, -0.01) \\ (-2.35, -0.53, -0.35, -0.02, -0.02, -0.05, -0.02, -0.02) \end{array} | \begin{array} [c]{c} \text{unstable} \\ \text{unstable} \\ \text{unstable} \\ \text{unstable} \\ \text{unstable} \\ \text{stable} \end{array} |
8 | \begin{array} [c]{l} Đ _{0}=(1000, 0, 0, 0, 0, 0, 0, 0) \\ Đ _{1}=(302.36, 9.51, 13.57, 0, 0, 33.93, 0, 0) \\ Đ _{2}=(108.33, 0, 0,137.18, 2.06, 0, 0, 0) \\ Đ _{3}=(746.27, 3.46, 2, 0, 0, 5, 3.67, 0) \\ Đ _{4}=(555.56, 0, 0, 68.38, 0.2, 0, 0, 4.13) \\ Đ _{6}=(302.36, 6.21, 8.87, 37.21, 0.2, 22.17, 0, 1.79) \\ Đ _{7}=(467.29, 2.17, 2, 57.51, 0.2, 5, 1.36, 3.31) \end{array} | \begin{array} [c]{l} (-2.83, 0.56, -0.32, -0.29, -0.1, -0.1, 0.07, -0.01) \\ (-2.33, 0.58, -0.35, -0.23, -0.1, -0.02, -0.02, 0.02) \\ (-2.13, 0.93, -0.41, -0.22, -0.16, -0.1, -0.07, -0.01) \\ (-2.86, -0.42, -0.27, -0.03, -0.03, -0.1, 0.06, -0.01) \\ (-2.53, -0.95, -0.33, 0.22, -0.1, -0.07, -0.03, -0.01) \\ (-2.33, -0.5, -0.35, 0.34, -0.02, -0.02, -0.05, -0.02) \\ (-2.52, -0.79, -0.38, -0.01, -0.01, -0.06, -0.03, -0.01) \end{array} | \begin{array} [c]{c} \text{unstable} \\ \text{unstable} \\ \text{unstable} \\ \text{unstable} \\ \text{unstable} \\ \text{unstable} \\ \text{stable} \end{array} |
Parameter | Description |
\rho | \begin{array} [c]{l} \text{Recruitment rate for the susceptible CD}4^{+}\text{T cells} \end{array} |
\alpha | \begin{array} [c]{l} \text{Natural mortality rate constant for the susceptible CD}4^{+}\text{T cells} \end{array} |
\eta_{1} | \begin{array} [c]{l} \text{Virus-cell incidence rate constant between free HIV particles and susceptible CD}4^{+}\text{T cells} \end{array} |
\eta_{2} | \begin{array} [c]{l} \text{Cell-cell incidence rate constant between HIV-infected cells and susceptible CD}4^{+}\text{T cells} \end{array} |
\eta_{3} | \begin{array} [c]{l} \text{Cell-cell incidence rate constant between Tax-expressing HTLV-infected cells and} \; \text{susceptible CD}4^{+}\text{T cells} \end{array} |
\beta \in \left(0, 1\right) | \begin{array} [c]{l} \text{Fraction coefficient accounts for the probability of new HIV-infected cells could be active, } \; \text{and the remaining part }1-\beta \text{ will be latent} \end{array} |
\gamma | \begin{array} [c]{l} \text{Death rate constant of latently HIV-infected cells} \end{array} |
a | \begin{array} [c]{l} \text{Death rate constant of actively HIV-infected cells} \end{array} |
\mu_{1} | \begin{array} [c]{l} \text{Killing rate constant of actively HIV-infected cells due to HIV-specific CTLs} \end{array} |
\mu_{2} | \begin{array} [c]{l} \text{Killing rate constant of Tax-expressing HTLV-infected cells due to HTLV-specific CTLs} \end{array} |
\varphi \in \left(0, 1\right) | \begin{array} [c]{l} \text{Probability of new HTLV infections could be enter a latent period} \end{array} |
\lambda | \begin{array} [c]{l} \text{Transmission rate constant of latently HIV-infected cells that become actively } \; \text{HIV-infected cells} \end{array} |
\psi | \begin{array} [c]{l} \text{Transmission rate constant of latently HTLV-infected cells that become Tax-expressing } \; \text{HTLV-infected cells} \end{array} |
\omega | \begin{array} [c]{l} \text{Death rate constant of latently HTLV-infected cells} \end{array} |
\delta | \begin{array} [c]{l} \text{Death rate constant of Tax-expressing HTLV-infected cells} \end{array} |
b | \begin{array} [c]{l} \text{Generation rate constant of new HIV particles} \end{array} |
\varepsilon | \begin{array} [c]{l} \text{Death rate constant of free HIV particles} \end{array} |
\sigma_{1} | \begin{array} [c]{l} \text{Proliferation rate constant of HIV-specific CTLs} \end{array} |
\sigma_{2} | \begin{array} [c]{l} \text{Proliferation rate constant of HTLV-specific CTLs} \end{array} |
\pi_{1} | \begin{array} [c]{l} \text{Decay rate constant of HIV-specific CTLs} \end{array} |
\pi_{2} | \begin{array} [c]{l} \text{Decay rate constant of HTLV-specific CTLs} \end{array} |
Equilibrium point | \begin{array} [c]{l} \text{Definition} \end{array} | \text{Existence conditions} |
Đ _{0}=(S_{0}, 0, 0, 0, 0, 0, 0, 0) | \begin{array} [c]{l} \text{Infection-free equilibrium} \end{array} | None |
Đ _{1}=(S_{1}, L_{1}, I_{1}, 0, 0, V_{1}, 0, 0) | \begin{array} [c]{l} \text{Chronic HIV mono-infection equilibrium} \; \text{with inactive immune response} \end{array} | \Re_{1} > 1 |
Đ _{2}=(S_{2}, 0, 0, E_{2}, Y_{2}, 0, 0, 0) | \begin{array} [c]{l} \text{Chronic HTLV mono-infection equilibrium} \; \text{with inactive immune response} \end{array} | \Re_{2} > 1 |
Đ _{3}=(S_{3}, L_{3}, I_{3}, 0, 0, V_{3}, C_{3}^{I}, 0) | \begin{array} [c]{l} \text{Chronic HIV mono-infection equilibrium} \; \text{with only active HIV-specific CTL} \end{array} | \Re_{3} > 1 |
Đ _{4}=(S_{4}, 0, 0, E_{4}, Y_{4}, 0, 0, C_{4}^{Y}) | \begin{array} [c]{l} \text{Chronic HTLV mono-infection equilibrium} \; \text{with only active HTLV-specific CTL} \end{array} | \Re_{4} > 1 |
Đ _{5}=(S_{5}, L_{5}, I_{5}, E_{5}, Y_{5}, V_{5}, C_{5}^{I}, 0) | \begin{array} [c]{l} \text{Chronic HIV/HTLV co-infection equilibrium} \; \text{with only active HIV-specific CTL} \end{array} | \Re_{5} > 1 and \Re_{1}/\Re_{2} > 1 |
Đ _{6}=(S_{6}, L_{6}, I_{6}, E_{6}, Y_{6}, V_{6}, 0, C_{6}^{Y}) | \begin{array} [c]{l} \text{Chronic HIV/HTLV co-infection equilibrium} \; \text{with only active HTLV-specific CTL} \end{array} | \Re_{6} > 1 and \Re_{2}/\Re_{1} > 1 |
Đ _{7}=(S_{7}, L_{7}, I_{7}, E_{7}, Y_{7}, V_{7}, C_{7}^{I}, C_{7}^{Y}) | \begin{array} [c]{l} \text{Chronic HIV/HTLV co-infection} \; \text{equilibrium with active HIV-specific} \\ \text{CTL and HTLV-specific CTL} \end{array} | \Re_{7} > 1 and \Re_{8} > 1 |
Equilibrium point | Global stability \text{ conditions} |
Đ _{0}=(S_{0}, 0, 0, 0, 0, 0, 0, 0) | \Re_{1}\leq1 and \Re_{2}\leq1 |
Đ _{1}=(S_{1}, L_{1}, I_{1}, 0, 0, V_{1}, 0, 0) | \Re_{1} > 1 , \Re_{2}/\Re _{1}\leq1 and \Re_{3}\leq1 |
Đ _{2}=(S_{2}, 0, 0, E_{2}, Y_{2}, 0, 0, 0) | \Re_{2} > 1 , \Re_{1}/\Re_{2}\leq1 and \Re_{4}\leq1 |
Đ _{3}=(S_{3}, L_{3}, I_{3}, 0, 0, V_{3}, C_{3}^{I}, 0) | \Re_{3} > 1 and \Re_{5}\leq1 |
Đ _{4}=(S_{4}, 0, 0, E_{4}, Y_{4}, 0, 0, C_{4}^{Y}) | \Re_{4} > 1 and \Re _{6}\leq1 |
Đ _{5}=(S_{5}, L_{5}, I_{5}, E_{5}, Y_{5}, V_{5}, C_{5}^{I}, 0) | \Re_{5} > 1 , \Re_{8}\leq1 and \Re_{1}/\Re_{2} > 1 |
Đ _{6}=(S_{6}, L_{6}, I_{6}, E_{6}, Y_{6}, V_{6}, 0, C_{6}^{Y}) | \Re_{6} > 1 , \Re_{7}\leq1 and \Re_{2}/\Re_{1} > 1 |
Đ _{7}=(S_{7}, L_{7}, I_{7}, E_{7}, Y_{7}, V_{7}, C_{7}^{I}, C_{7}^{Y}) | \Re_{7} > 1 and \Re_{8} > 1 |
Parameter | Value | Parameter | Value | Parameter | Value |
\rho | 10 | \delta | 0.2 | \beta | 0.7 |
\alpha | 0.01 | b | 5 | \gamma | 0.02 |
\eta_{1} | Varied | \pi_{1} | 0.1 | \sigma_{1} | Varied |
\eta_{2} | Varied | \pi_{2} | 0.1 | \sigma_{2} | Varied |
\eta_{3} | Varied | \mu_{1} | 0.2 | \lambda | 0.2 |
a | 0.5 | \mu_{2} | 0.2 | \omega | 0.01 |
\varphi | 0.2 | \varepsilon | 2 | \psi | 0.003 |
Scenario | The equilibria | (\operatorname{Re}(\lambda_{i}), i=1, 2, ..., 6) | Stability |
1 | \begin{array} [c]{c} Đ _{0}=(1000, 0, 0, 0, 0, 0, 0, 0) \end{array} | \begin{array} [c]{c} (-2.19, -0.37, -0.2, -0.1, -0.1, -0.09, -0.01, -0.01) \end{array} | \begin{array} [c]{l} \text{stable} \end{array} |
2 | \begin{array} [c]{l} Đ _{0}=(1000, 0, 0, 0, 0, 0, 0, 0) \\ Đ _{1}=(331.63, 9.11, 13, 0, 0, 32.51, 0, 0) \end{array} | \begin{array} [c]{l} (-2.7, 0.51, -0.32, -0.2, -0.1, -0.1, -0.01, -0.01) \\ (-2.3, -0.35, -0.2, -0.1, -0.02, -0.02, -0.06, -0.01) \end{array} | \begin{array} [c]{l} \text{unstable} \\ \text{stable} \end{array} |
3 | \begin{array} [c]{l} Đ _{0}=(1000, 0, 0, 0, 0, 0, 0, 0) \\ Đ _{2}=(433.33, 0, 0, 87.18, 1.31, 0, 0, 0) \end{array} | \begin{array} [c]{l} (-2.18, -0.36, -0.23, -0.1, -0.1, -0.03, 0.01, -0.01) \\ (-2.09, -0.41, -0.21, -0.16, -0.1, -0.03, -0.01, -0.01) \end{array} | \begin{array} [c]{c} \text{unstable} \\ \text{stable} \end{array} |
4 | \begin{array} [c]{l} Đ _{0}=(1000, 0, 0, 0, 0, 0, 0, 0) \\ Đ _{1}=(197.7, 10.94, 15.61, 0, 0, 39.02, 0, 0) \\ Đ _{2}=(866.67, 0, 0, 20.51, 0.31, 0, 0, 0) \\ Đ _{3}=(277.78, 9.85, 10, 0, 0, 25, 1.01, 0) \end{array} | \begin{array} [c]{l} (-3.22, 0.88, -0.31, -0.21, -0.1, -0.1, -0.01, 0.002) \\ (-2.36, -0.35, -0.2, -0.03, -0.03, -0.1, 0.06, -0.01) \\ (-3.1, 0.76, -0.32, -0.21, -0.1, -0.1, -0.01, -0.002) \\ (-2.51, -0.37, -0.2, -0.02, -0.02, -0.1, -0.02, -0.01) \end{array} | \begin{array} [c]{c} \text{unstable} \\ \text{unstable} \\ \text{unstable} \\\text{stable} \end{array} |
5 | \begin{array} [c]{l} Đ _{0}=(1000, 0, 0, 0, 0, 0, 0, 0) \\ Đ _{1}=(277.85, 9.85, 14.05, 0, 0, 35.12, 0, 0) \\ Đ _{2}=(43.33, 0, 0,147.18, 2.21, 0, 0, 0) \\ Đ _{3}=(729.93, 3.68, 2, 0, 0, 5, 4.07, 0) \\ Đ _{4}=(230.77, 0, 0,118.34, 0.33, 0, 0, 4.33) \end{array} | \begin{array} [c]{l} (-2.94, 0.61, -0.37, -0.32, 0.16, -0.1, -0.1, -0.01) \\ (-2.35, 0.6, -0.35, -0.27, -0.1, -0.02, -0.02, 0.05) \\ (-2.06, 0.56, -0.45, -0.27, -0.2, -0.16, -0.1, -0.01) \\ (-2.99, -0.43, -0.34, -0.03, -0.03, 0.12, -0.1, -0.01) \\ (-2.3, -0.99, -0.36, -0.1, -0.06, -0.06, -0.05, -0.01) \end{array} | \begin{array} [c]{c} \text{unstable} \\ \text{unstable} \\ \text{unstable} \\ \text{unstable} \\ \text{stable} \end{array} |
6 | \begin{array} [c]{l} Đ _{0}=(1000, 0, 0, 0, 0, 0, 0, 0) \\ Đ _{1}=(197.7, 10.94, 15.61, 0, 0, 39.02, 0, 0) \\ Đ _{2}=(433.33, 0, 0, 87.18, 1.31, 0, 0, 0) \\ Đ _{3}=(657.89, 4.67, 2, 0, 0, 5, 5.82, 0) \\ Đ _{4}=(444.44, 0, 0, 85.47, 1.25, 0, 0, 0.03) \\ Đ _{5}=(433.33, 3.07, 2, 52.51, 0.79, 5, 2.98, 0) \end{array} | \begin{array} [c]{l} (-3.22, 0.88, -0.31, -0.23, -0.1, -0.1, 0.01, -0.01) \\ (-2.36, 0.68, -0.35, -0.21, -0.03, -0.03, -0.1, -0.01) \\ (-2.67, -0.33, 0.3, -0.21, -0.1, -0.01, -0.01, 0.005) \\ (-3.3, -0.45, -0.22, -0.05, -0.05, -0.1, -0.01, 0.01) \\ (-2.68, -0.32, 0.32, -0.22, -0.1, -0.01, -0.01, -0.005) \\ (-2.83, -0.41, -0.21, -0.03, -0.03, -0.04, -0.01, -0.01) \end{array} | \begin{array} [c]{c} \text{unstable} \\ \text{unstable} \\ \text{unstable} \\ \text{unstable} \\ \text{unstable} \\ \text{stable} \end{array} |
7 | \begin{array} [c]{l} Đ _{0}=(1000, 0, 0, 0, 0, 0, 0, 0) \\ Đ _{1}=(321.26, 9.26, 13.2, 0, 0, 33.01, 0, 0) \\ Đ _{2}=(108.33, 0, 0,137.18, 2.06, 0, 0, 0) \\ Đ _{3}=(384.62, 8.39, 10, 0, 0, 25, 0.49, 0) \\ Đ _{4}=(555.56, 0, 0, 68.38, 0.2, 0, 0, 4.13) \\ Đ _{6}=(321.26, 5.75, 8.2, 39.54, 0.2, 20.51, 0, 1.97) \end{array} | \begin{array} [c]{l} (-2.84, 0.51, -0.32, -0.29, -0.1, -0.1, 0.07, -0.01) \\ (-2.35, -0.35, -0.23, -0.1, -0.02, -0.02, 0.03, 0.02) \\ (-2.13, 0.93, -0.42, -0.22, -0.16, -0.1, -0.07, -0.01) \\ (-2.42, -0.36, -0.24, -0.1, -0.01, -0.01, 0.03, -0.01) \\ (-2.54, -0.95, -0.33, 0.19, -0.1, -0.07, -0.03, -0.01) \\ (-2.35, -0.53, -0.35, -0.02, -0.02, -0.05, -0.02, -0.02) \end{array} | \begin{array} [c]{c} \text{unstable} \\ \text{unstable} \\ \text{unstable} \\ \text{unstable} \\ \text{unstable} \\ \text{stable} \end{array} |
8 | \begin{array} [c]{l} Đ _{0}=(1000, 0, 0, 0, 0, 0, 0, 0) \\ Đ _{1}=(302.36, 9.51, 13.57, 0, 0, 33.93, 0, 0) \\ Đ _{2}=(108.33, 0, 0,137.18, 2.06, 0, 0, 0) \\ Đ _{3}=(746.27, 3.46, 2, 0, 0, 5, 3.67, 0) \\ Đ _{4}=(555.56, 0, 0, 68.38, 0.2, 0, 0, 4.13) \\ Đ _{6}=(302.36, 6.21, 8.87, 37.21, 0.2, 22.17, 0, 1.79) \\ Đ _{7}=(467.29, 2.17, 2, 57.51, 0.2, 5, 1.36, 3.31) \end{array} | \begin{array} [c]{l} (-2.83, 0.56, -0.32, -0.29, -0.1, -0.1, 0.07, -0.01) \\ (-2.33, 0.58, -0.35, -0.23, -0.1, -0.02, -0.02, 0.02) \\ (-2.13, 0.93, -0.41, -0.22, -0.16, -0.1, -0.07, -0.01) \\ (-2.86, -0.42, -0.27, -0.03, -0.03, -0.1, 0.06, -0.01) \\ (-2.53, -0.95, -0.33, 0.22, -0.1, -0.07, -0.03, -0.01) \\ (-2.33, -0.5, -0.35, 0.34, -0.02, -0.02, -0.05, -0.02) \\ (-2.52, -0.79, -0.38, -0.01, -0.01, -0.06, -0.03, -0.01) \end{array} | \begin{array} [c]{c} \text{unstable} \\ \text{unstable} \\ \text{unstable} \\ \text{unstable} \\ \text{unstable} \\ \text{unstable} \\ \text{stable} \end{array} |