Citation: Tyson Loudon, Stephen Pankavich. Mathematical analysis and dynamic active subspaces for a long term model of HIV[J]. Mathematical Biosciences and Engineering, 2017, 14(3): 709-733. doi: 10.3934/mbe.2017040
[1] | Ke Guo, Wanbiao Ma . Global dynamics of an SI epidemic model with nonlinear incidence rate, feedback controls and time delays. Mathematical Biosciences and Engineering, 2021, 18(1): 643-672. doi: 10.3934/mbe.2021035 |
[2] | Yu Ji . Global stability of a multiple delayed viral infection model with general incidence rate and an application to HIV infection. Mathematical Biosciences and Engineering, 2015, 12(3): 525-536. doi: 10.3934/mbe.2015.12.525 |
[3] | Ardak Kashkynbayev, Daiana Koptleuova . Global dynamics of tick-borne diseases. Mathematical Biosciences and Engineering, 2020, 17(4): 4064-4079. doi: 10.3934/mbe.2020225 |
[4] | Xinran Zhou, Long Zhang, Tao Zheng, Hong-li Li, Zhidong Teng . Global stability for a class of HIV virus-to-cell dynamical model with Beddington-DeAngelis functional response and distributed time delay. Mathematical Biosciences and Engineering, 2020, 17(5): 4527-4543. doi: 10.3934/mbe.2020250 |
[5] | Pengyan Liu, Hong-Xu Li . Global behavior of a multi-group SEIR epidemic model with age structure and spatial diffusion. Mathematical Biosciences and Engineering, 2020, 17(6): 7248-7273. doi: 10.3934/mbe.2020372 |
[6] | A. M. Elaiw, N. H. AlShamrani . Analysis of an HTLV/HIV dual infection model with diffusion. Mathematical Biosciences and Engineering, 2021, 18(6): 9430-9473. doi: 10.3934/mbe.2021464 |
[7] | N. H. AlShamrani, A. M. Elaiw . Stability of an adaptive immunity viral infection model with multi-stages of infected cells and two routes of infection. Mathematical Biosciences and Engineering, 2020, 17(1): 575-605. doi: 10.3934/mbe.2020030 |
[8] | Ning Bai, Rui Xu . Mathematical analysis of an HIV model with latent reservoir, delayed CTL immune response and immune impairment. Mathematical Biosciences and Engineering, 2021, 18(2): 1689-1707. doi: 10.3934/mbe.2021087 |
[9] | Jinliang Wang, Jingmei Pang, Toshikazu Kuniya . A note on global stability for malaria infections model with latencies. Mathematical Biosciences and Engineering, 2014, 11(4): 995-1001. doi: 10.3934/mbe.2014.11.995 |
[10] | A. M. Elaiw, N. H. AlShamrani . Stability of HTLV/HIV dual infection model with mitosis and latency. Mathematical Biosciences and Engineering, 2021, 18(2): 1077-1120. doi: 10.3934/mbe.2021059 |
As we know, one of the most common ways to study the asymptotic stability for a system of delay differential equations (DDEs) is the Lyapunov functional method. For DDEs, the Lyapunov-LaSalle theorem (see [6,Theorem 5.3.1] or [11,Theorem 2.5.3]) is often used as a criterion for the asymptotic stability of an autonomous (possibly nonlinear) delay differential system. It can be applied to analyse the dynamics properties for lots of biomathematical models described by DDEs, for example, virus infection models (see, e.g., [2,3,10,14]), microorganism flocculation models (see, e.g., [4,5,18]), wastewater treatment models (see, e.g., [16]), etc.
In the Lyapunov-LaSalle theorem, a Lyapunov functional plays an important role. But how to construct an appropriate Lyapunov functional to investigate the asymptotic stability of DDEs, is still a very profound and challenging topic.
To state our purpose, we take the following microorganism flocculation model with time delay in [4] as example:
{˙x(t)=1−x(t)−h1x(t)y(t),˙y(t)=rx(t−τ)y(t−τ)−y(t)−h2y(t)z(t),˙z(t)=1−z(t)−h3y(t)z(t), | (1.1) |
where
G={ϕ=(ϕ1,ϕ2,ϕ3)T∈C+:=C([−τ,0],R3+) : ϕ1≤1, ϕ3≤1}. |
In model (1.1), there exists a forward bifurcation or backward bifurcation under some conditions [4]. Thus, it is difficult to use the research methods that some virus models used to study the dynamics of such model.
Clearly, (1.1) always has a microorganism-free equilibrium
L(ϕ)=ϕ2(0)+r∫0−τϕ1(θ)ϕ2(θ)dθ, ϕ∈G. | (1.2) |
The derivative of
˙L(ut)=(rx(t)−1−h2z(t))y(t)≤(r−1−h2z(t))y(t). | (1.3) |
Obviously, if
However, we can not get
lim inft→∞z(t)≥h1h1+rh3. | (1.4) |
If
˙V(ut)≤[r−1−h1h2ε(h1+rh3)]y(t)≤0, t≥T. |
Obviously, for all
In this paper, we will expand the view of constructing Lyapunov functionals, namely, we first give a new understanding of Lyapunov-LaSalle theorem (including its modified version [9,15,19]), and based on it establish some global stability criteria for an autonomous delay differential system.
Let
˙u(t)=g(ut), t≥0, | (2.1) |
where
˙L(ϕ)=˙L(ϕ)|(2.1)=lim sups→0+L(us(ϕ))−L(ϕ)s. |
Let
u(t)=u(t,ϕ):=(u1(t,ϕ),u2(t,ϕ),⋯,un(t,ϕ))T |
denote a solution of system (2.1) satisfying
U(t):=ut(⋅):X→X (which also satisfies U(t):¯X→¯X), |
and for
OT(ϕ):={ut(ϕ):t≥T}. |
Let
The following Definition 2.1 and Theorem 2.1 (see, e.g., [6,Theorem 5.3.1], [11,Theorem 2.5.3]) can be utilized in dynamics analysis of lots of biomathematical models in the form of system (2.1).
Definition 2.1. We call
(ⅰ)
(ⅱ)
Theorem 2.1 (Lyapunov-LaSalle theorem [11]). Let
In Theorem 2.1, a Lyapunov functional
X={ϕ=(ϕ1,ϕ2,⋯,ϕn)T∈C:ϕi(0)>0}, | (2.2) |
which can ensure
However, we will assume that
Corollary 2.1. Let the solution
Proof. It is clear that if
Remark 2.1. It is not difficult to find that in the modified Lyapunov-LaSalle theorem (see, e.g., [9,15,19]), if
Remark 2.2. In fact, we can see that a bounded
From Corollary 2.1, we may consider the global properties of system (2.1) on the larger space than
Let
Theorem 3.1. Suppose that the following conditions hold:
(ⅰ) Let
˙L(φ)≤−w(φ)b(φ), | (3.1) |
where
(ⅱ) There exist
k1≤φ≤k2, w(φ)≥(w01,w02,⋯,w0k)≡w0=w0(k1,k2)≫0, |
and
Then
Proof. To obtain
lim inft→∞w(ut(ϕ)):=(lim inft→∞w1(ut(ϕ)),lim inft→∞w2(ut(ϕ)),⋯,lim inft→∞wk(ut(ϕ)))=(limm→∞f1(t1m),limm→∞f2(t2m),⋯,limm→∞fk(tkm)). |
For each sequence
lim inft→∞wi(ut(ϕ))=limm→∞wi(utim(ϕ))=wi(ϕi). |
By the condition (ⅱ),
˙L(φ)≤−w(φ)b(φ)≤−w0b(φ)2≤0. |
Hence,
Next, we show that
˙L(ut(ψ))≤−w(ut(ψ))b(ut(ψ)), ∀t≥0. |
By (ⅱ),
Remark 3.1. By
Next, we will give an illustration for Theorem 3.1. Now, we reconsider the global stability for the infection-free equilibrium
{˙x(t)=s−dx(t)−cx(t)y(t)−βx(t)v(t),˙y(t)=e−μτβx(t−τ)v(t−τ)−py(t),˙v(t)=ky(t)−uv(t), | (3.2) |
where
In [1], we know
G={ϕ∈C([−τ,0],R3+):ϕ1≤x0}⊂C+:=C([−τ,0],R3+). |
Indeed, by Theorem 3.1, we can extend the result of [1] to the larger set
Corollary 3.1. If
Proof. It is not difficult to obtain
L(ϕ)=ϕ1(0)−x0−x0lnϕ1(0)x0+a1ϕ2(0)+a1e−μτ∫0−τβϕ1(θ)ϕ3(θ)dθ+a2ϕ3(0), | (3.3) |
where
a1=2(kβx0+ucx0)pu−e−μτkβx0,a2=2(pβx0+e−μτcβx20)pu−e−μτkβx0. |
Let
w(φ)≡(dφ1(0),a1p−a2k−cx0,a2u−a1e−μτβφ1(0)−βx0)≥(dx0,a1p−a2k−cx0,a2u−a1e−μτβx0−βx0)=(dx0,cx0,βx0)≡w0≫0, |
where
The derivative of
˙L1(ut)=d(x0−x(t))(1−x0x(t))+x0(cy(t)+βv(t))−x(t)(cy(t)+βv(t))+a1e−μτβx(t)v(t)−a1py(t)+a2ky(t)−a2uv(t)≤−dx(t)(x0−x(t))2−(a1p−a2k−cx0)y(t)−(a2u−a1e−μτβx(t)−βx0)v(t)=−w(ut)b(ut). |
Therefore, it follows from Theorem 3.1 that
In [3,Theorem 3.1], the infection-free equilibrium
Theorem 3.2. In the condition (ii) of Theorem 3.1, if the condition that
Proof. In the foundation of the similar argument as in the proof of Theorem 3.1, we have that
˙L(ut(ψ))≤−w0b(ut(ψ))≤0. |
Hence,
Next, by using Theorem 3.2, we will give the global stability of the equilibrium
˙L(ut)≤−w(ut)b(ut), | (3.4) |
where
w(ut)=1+h2zt(0)−r=1+h2z(t)−r,b(ut)=yt(0)=y(t). |
Let
p(t)=rh1xt(−τ)+yt(0)=rh1x(t−τ)+y(t), t≥τ. |
Then we have
lim inft→∞x(t)≥1r+1, lim inft→∞z(t)≥h1h1+rh3. | (3.5) |
Thus, for any
(1/(r+1),0,h1/(h1+rh3))T≤φ≤(1,r/h1,1)T,w(φ)=1+h2φ3(0)−r≥1+h1h2/(h1+rh3)−r≡w0>0, |
and
Thus, we only need to obtain the solutions of a system are bounded and then may establish the upper- and lower-bound estimates of
Corollary 3.2. Let
a(φ(0))≤L(φ), ˙L(φ)≤−w0b(φ), 0≪wT0∈Rk, | (3.6) |
where
Proof. Since
a(u(t,ϕ))≤L(ut(ϕ))≤L(uT(ϕ)), t∈[T,εϕ), |
and the fact that
Corollary 3.3. Assume that
a(|φ(0)−E|)≤L(φ), ˙L(φ)≤−w0b(φ), 0≪wT0∈Rk, | (3.7) |
where
Proof. It follows from Corollary 3.2 that the boundedness of
ut(ϕ)∈B(ut(E),ε)=B(E,ε), |
where
a(|u(t,ϕ)−E|)≤L(ut(ϕ))≤L(uT(ϕ))<a(ε), |
which yields
Lemma 3.1. ([13,Lemma 1.4.2]) For any infinite positive definite function
By Lemma 3.1, we have the following remark.
Remark 3.2. If there exists an infinite positive definite function
Corollary 3.4. In Corollary 3.2, if the condition
For a dissipative system (2.1), we will give the upper- and lower-bound estimates of
Lemma 3.2. Let
Proof. For any
Theorem 3.3. Suppose that there exist
k1≤lim inft→∞ut(ϕ)(θ)≤lim supt→∞ut(ϕ)(θ)≤k2, ∀ϕ∈X, ∀θ∈[−τ,0], | (3.8) |
where
lim inft→∞ut(ϕ)(θ):=(lim inft→∞u1t(ϕ)(θ),⋯,lim inft→∞unt(ϕ)(θ))T,lim supt→∞ut(ϕ)(θ):=(lim supt→∞u1t(ϕ)(θ),⋯,lim supt→∞unt(ϕ)(θ))T. |
Then
Proof. Clearly,
|˙u(t,φ)|≤M1, ∀t≥0, ∀φ∈M. |
It follows from the invariance of
In this paper, we first give a variant of Theorem 2.1, see Corollary 2.1. In fact, the modified version of Lyapunov-LaSalle theorem (see, e.g., [9,15,19]) is to expand the condition (ⅰ) of Definition 2.1, while Corollary 2.1 is mainly to expand the condition (ⅱ) of Definition 2.1. More specifically, we assume that
As a result, the criteria for the global attractivity of equilibria of system (2.1) are given in Theorem 3.1 and Theorem 3.2, respectively. As direct consequences, we also give the corresponding particular cases of Theorem 3.1 and Theorem 3.2, see Corollaries 3.2, 3.3 and 3.4, respectively. The developed theory can be utilized in many models (see, e.g., [2,3,9,10,14]). The compactness and the upper- and lower-bound estimates of
This work was supported in part by the General Program of Science and Technology Development Project of Beijing Municipal Education Commission (No. KM201910016001), the Fundamental Research Funds for Beijing Universities (Nos. X18006, X18080 and X18017), the National Natural Science Foundation of China (Nos. 11871093 and 11471034). The authors would like to thank Prof. Xiao-Qiang Zhao for his valuable suggestions.
The authors declare there is no conflict of interest in this paper.
[1] | [ D. Callaway,A. Perelson, HIV-1 infection and low steady state viral loads, Bull.Math.Biol., 64 (2002): 29-64. |
[2] | [ P. Constantine, Active Subspaces: Emerging Ideas for Dimension Reduction in Parameter Studies SIAM, 2015. |
[3] | [ P. Constantine and D. Gleich, Computing active subspaces with monte carlo, arXiv: 1408.0545 |
[4] | [ P. Constantine,B. Zaharatos,M. Campanelli, Discovering an active subspace in a single-diode solar cell model, Statistical Analysis and Data Mining: The ASA Data Science Journal, 8 (2015): 264-273. |
[5] | [ A. S. Fauci,G. Pantaleo,S. Stanley, Immunopathogenic mechanisms of HIV infection, Annals of Internal Medicine, 124 (1996): 654-663. |
[6] | [ T. C. Greenough,D. B. Brettler,F. Kirchhoff, Long-term non-progressive infection with Human Immunodeficiency Virus in a Hemophilia cohort, J Infect Dis, 180 (1999): 1790-1802. |
[7] | [ A. B. Gumel,P. N. Shivakumar,B. M. Sahai, A mathematical model for the dynamics of HIV-1 during the typical course of infection, Nonlinear Analysis, 47 (2001): 1773-1783. |
[8] | [ M. Hadjiandreou,R. Conejeros,V. S. Vassiliadis, Towards a long-term model construction for the dynamic simulation of HIV infection, Mathematical Biosciences and Engineering, 4 (2007): 489-504. |
[9] | [ E. Hernandez-Vargas,R. Middleton, Modeling the three stages in HIV infection, J TheorBiol., 320 (2013): 33-40. |
[10] | [ T. Igarashi,C. R. Brown,Y. Endo, Macrophages are the principal reservoir and sustain high virus loads in Rhesus Macaques following the depletion of CD4+ T-cells by a highly pathogenic SIV: Implications for HIV-1 infections of man, Proc Natl Acad Sci., 98 (2001): 658-663. |
[11] | [ E. Jones and P. Roemer (sponsors: S. Pankavich and M. Raghupathi), Analysis and simulation of the three-component model of HIV dynamics, SIAM Undergraduate Research Online, 7 (2014), 89–106 |
[12] | [ D. Kirschner, Using mathematics to understand HIV immunodynamics, Am. Math. Soc., 43 (1996): 191-202. |
[13] | [ D. E. Kirschner and A. S. Perelson, A model for the immune response to HIV: AZT treatment studies, Mathematical Population Dynamics: Analysis of Heterogeneity, Volume One: Theory of Epidemics Eds. O. Arino, D. Axelrod, M. Kimmel, and M. Langlais, Wuerz Publishing Ltd., Winnipeg, Canada, (1993), 295–310. |
[14] | [ D. Kirschner,G. F. Webb, Immunotherapy of HIV-1 infection, J Biological Systems, 6 (1998): 71-83. |
[15] | [ D. Kirschner,G. F. Webb,M. Cloyd, A model of HIV-1 disease progression based on virus-induced lymph node homing-induced apoptosis of CD4+ lymphocytes, J Acquir Immune Dec Syndr, 24 (2000): 352-362. |
[16] | [ J. M. Murray,G. Kaufmann,A. D. Kelleher, A model of primary HIV-1 infection, Math Biosci, 154 (1998): 57-85. |
[17] | [ M. Nowak and R. May, Virus Dynamics: Mathematical Principles of Immunology and Virology Oxford University Press, NewYork, 2000. |
[18] | [ S. Pankavich, The effects of latent infection on the dynamics of HIV, Differential Equations and Dynamical Systems, 24 (2016): 281-303. |
[19] | [ S. Pankavich,D. Shutt, An in-host model of HIV incorporating latent infection and viral mutation, Dynamical Systems, Differential Equations, and Applications, AIMS Proceedings, null (2015): 913-922. |
[20] | [ S. Pankavich, N. Neri and D. Shutt, Bistable dynamics and Hopf bifurcation in a refined model of the acute stage of HIV infection, submitted, (2015). |
[21] | [ S. Pankavich,C. Parkinson, Mathematical analysis of an in-host model of viral dynamics with spatial heterogeneity, Discrete and Continuous Dynamical Systems B, 21 (2016): 1237-1257. |
[22] | [ E. Pennisi and J. Cohen, Eradicating HIV from a patient: Not just a dream?, Science, 272 (1996), 1884. |
[23] | [ A. S. Perelson, Modeling the Interaction of the Immune System with HIV, Lecture Notes in Biomath. Berlin: Springer, 1989. |
[24] | [ A. Perelson,P. Nelson, Mathematical analysis of HIV-1 dynamics in vivo, SIAM Rev., 41 (1999): 3-44. |
[25] | [ T. M. Russi, Uncertainty Quantification with Experimental data and Complex System Models, Ph. D. thesis, UC Berkeley, 2010. |
[26] | [ W. Y. Tan,H. Wu, Stochastic modeing of the dynamics of CD4+ T-cell infection by HIV and some monte carlo studies, Math Biosci, 147 (1997): 173-205. |
[27] | [ E. Vergu,A. Mallet,J. Golmard, A modeling approach to the impact of HIV mutations on the immune system, Comput Biol Med., 35 (2005): 1-24. |
1. | Jing-An Cui, Shifang Zhao, Songbai Guo, Yuzhen Bai, Xiaojing Wang, Tianmu Chen, Global dynamics of an epidemiological model with acute and chronic HCV infections, 2020, 103, 08939659, 106203, 10.1016/j.aml.2019.106203 | |
2. | Jinlong Lv, Songbai Guo, Jing-An Cui, Jianjun Paul Tian, Asymptomatic transmission shifts epidemic dynamics, 2021, 18, 1551-0018, 92, 10.3934/mbe.2021005 | |
3. | Yunzhe Su, Yajun Yang, Xuerong Yang, Wei Ye, Attitude tracking control for observation spacecraft flying around the target spacecraft, 2021, 15, 1751-8644, 1868, 10.1049/cth2.12165 | |
4. | Yujie Sheng, Jing-An Cui, Songbai Guo, The modeling and analysis of the COVID-19 pandemic with vaccination and isolation: a case study of Italy, 2023, 20, 1551-0018, 5966, 10.3934/mbe.2023258 | |
5. | Yu-zhen Bai, Xiao-jing Wang, Song-bai Guo, Global Stability of a Mumps Transmission Model with Quarantine Measure, 2021, 37, 0168-9673, 665, 10.1007/s10255-021-1035-7 | |
6. | Song-bai Guo, Min He, Jing-an Cui, Global Stability of a Time-delayed Malaria Model with Standard Incidence Rate, 2023, 0168-9673, 10.1007/s10255-023-1042-y | |
7. | Iasson Karafyllis, Pierdomenico Pepe, Antoine Chaillet, Yuan Wang, 2022, Uniform Global Asymptotic Stability for Time-Invariant Delay Systems, 978-1-6654-6761-2, 6875, 10.1109/CDC51059.2022.9992709 | |
8. | Iasson Karafyllis, Pierdomenico Pepe, Antoine Chaillet, Yuan Wang, Is Global Asymptotic Stability Necessarily Uniform for Time-Invariant Time-Delay Systems?, 2022, 60, 0363-0129, 3237, 10.1137/22M1485887 | |
9. | Leilei Xue, Liping Sun, Songbai Guo, Dynamic effects of asymptomatic infections on malaria transmission, 2023, 214, 03784754, 172, 10.1016/j.matcom.2023.07.004 | |
10. | 勇盛 赵, Dynamical Analysis of a COVID-19 Transmission Model with Vaccination, 2024, 13, 2324-7991, 1187, 10.12677/aam.2024.134109 | |
11. | Ke Guo, Songbai Guo, Lyapunov functionals for a general time-delayed virus dynamic model with different CTL responses, 2024, 34, 1054-1500, 10.1063/5.0204169 | |
12. | Songbai Guo, Min He, Fuxiang Li, Threshold dynamics of a time-delayed dengue virus infection model incorporating vaccination failure and exposed mosquitoes, 2025, 161, 08939659, 109366, 10.1016/j.aml.2024.109366 | |
13. | Songbai Guo, Qianqian Pan, Jing‐An Cui, P. Damith Nilanga Silva, Global behavior and optimal control of a dengue transmission model with standard incidence rates and self‐protection, 2024, 0170-4214, 10.1002/mma.10351 | |
14. | Songbai Guo, Xin Yang, Zuohuan Zheng, Global dynamics of a time-delayed malaria model with asymptomatic infections and standard incidence rate, 2023, 31, 2688-1594, 3534, 10.3934/era.2023179 | |
15. | Dongfang Li, Yilong Zhang, Wei Tong, Ping Li, Rob Law, Xin Xu, Limin Zhu, Edmond Q. Wu, Anti-Disturbance Path-Following Control for Snake Robots With Spiral Motion, 2023, 19, 1551-3203, 11929, 10.1109/TII.2023.3254534 | |
16. | 欣 李, Dynamic Analysis of a Syphilis Infectious Disease Model with Early Screening, 2024, 13, 2324-7991, 3722, 10.12677/aam.2024.138355 | |
17. | Songbai Guo, Yuling Xue, Rong Yuan, Maoxing Liu, An improved method of global dynamics: Analyzing the COVID-19 model with time delays and exposed infection, 2023, 33, 1054-1500, 10.1063/5.0144553 | |
18. | Xiaojing Wang, Jiahui Li, Songbai Guo, Maoxing Liu, Dynamic analysis of an Ebola epidemic model incorporating limited medical resources and immunity loss, 2023, 69, 1598-5865, 4229, 10.1007/s12190-023-01923-2 |