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A Delayed Model for HIV Infection Incorporating Intracellular Delay

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Abstract

This article introduces a delayed HIV model arising out of incorporating the intracellular delay. It has been assumed that intracellular delay \(\tau \) is constant and some of the infected T cell actually dies out due to infection and only a portion of infected T cell which remain alive a time lag of \(\tau \) after infection will produce newly HIV particles. The mathematical analysis on the present model suggests that infection free equilibrium is still always possible. The endemic equilibrium point exists if number of virus particle produced is greater than \(e^{\delta _2 \tau }\) and \(\delta _3 < {\overline{\delta _3}}\), where \(\delta _2\) and \(\delta _3\) are the mortality rate of infected T cell and virus respectively. The local stability analysis and Hopf Bifurcation analysis have been carried out on the proposed model and same supported by numerical simulation. The proposed model exhibit some interesting dynamical behaviour of the HIV infection.

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References

  1. Anderson, R.M.: Mathematical and statistical studies of the epidemiology of HIV. AIDS 3(6), 333–346 (1989)

    Article  Google Scholar 

  2. Bailey, J.J., Fletcher, J.E., Chuck, E.T., Shrager, R.I.: A kinetic model of CD4\(^+\) lymphocytes with the human immunodeficiency virus (HIV). BioSystems 26(3), 177–183 (1992)

    Article  Google Scholar 

  3. Beretta, E., Kuang, Y.: Modeling and analysis of a marine bacteriophage infection with latency period. Nonlinear Anal. Real World Appl. 2(1), 35–74 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  4. Beretta, E., Kuang, Y.: Geometric stability switch criteria in delay differential systems with delay-dependent parameters. SIAM J. Math. Anal. 33(31), 144–1165 (2002)

    MathSciNet  MATH  Google Scholar 

  5. Brauer, F., Castillo-Chavez, C., Castillo-Chavez, C.: Mathematical Models in Population Biology and Epidemiology, vol. 40. Springer, New York (2001)

  6. Culshaw, R.V., Ruan, S.: A delay-differential equation model of HIV infection of CD4\(^+\) T-cells. Math. Biosci. 165(1), 27–39 (2000)

    Article  MATH  Google Scholar 

  7. Culshaw, R.V., Ruan, S., Webb, G.: A mathematical model of cell-to-cell spread of HIV-1 that includes a time delay. J. Math. Biol. 46(5), 425–444 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  8. Hale, J.K., Lunel, S.M.V.: Introduction to Functional Differential Equations. Springer, New York (1993)

    Book  MATH  Google Scholar 

  9. Hraba, T., Doležal, J., čelikovský, S.: Model-based analysis of CD4\(^+\) lymphocyte dynamics in HIV infected individuals. Immunobiology 181(1), 108–118 (1990)

    Article  Google Scholar 

  10. Hristov, J.: Transient heat diffusion with a non-singular fading memory: from the Cattaneo constitutive equation with Jeffrey’s kernel to the Caputo-Fabrizio time-fractional derivative. Therm. Sci. 20, 19 (2016)

    Article  Google Scholar 

  11. Kirschner, D.E., Webb, G.F.: A mathematical model of combined drug therapy of HIV infection. Comput. Math. Methods Med. 1(1), 25–34 (1997)

    MATH  Google Scholar 

  12. Kuang, Y.: Delay Differential Equations: With Applications in Population Dynamics. Academic Press, Boston (1993)

    MATH  Google Scholar 

  13. Li, D., Ma, W.: Asymptotic properties of a HIV-1 infection model with time delay. J. Math. Anal. Appl. 335(1), 683–691 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  14. Li, M.Y., Shu, H.: Impact of intracellular delays and target-cell dynamics on in vivo viral infections. SIAM J. Appl. Math. 70(7), 2434–2448 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  15. Mittler, J.E., Sulzer, B., Neumann, A.U., Perelson, A.S.: Influence of delayed viral production on viral dynamics in HIV-1 infected patients. Math. Biosci. 152(2), 143–163 (1998)

    Article  MATH  Google Scholar 

  16. Nelson, P.W., Murray, J.D., Perelson, A.S.: A model of HIV-1 pathogenesis that includes an intracellular delay. Math. Biosci. 163(2), 201–215 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  17. Nelson, P.W., Perelson, A.S.: Mathematical analysis of delay differential equation models of HIV-1 infection. Math. Biosci. 179(1), 73–94 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  18. Nowak, M., May, R.M.: Virus Dynamics: Mathematical Principles of Immunology and Virology: Oxford University Press, New York (2000)

  19. Nowak, M.A., Bangham, C.R.: Population dynamics of immune responses to persistent viruses. Science 272(5258), 74–79 (1996)

    Article  Google Scholar 

  20. Perelson, A.S.: Modelling viral and immune system dynamics. Nat. Rev. Immunol. 2(1), 28–36 (2002)

    Article  Google Scholar 

  21. Perelson, A.S., Kirschner, D.E., De Boer, R.: Dynamics of HIV infection of CD4\(^+\) T cells. Math. Biosci. 114(1), 81–125 (1993)

    Article  MATH  Google Scholar 

  22. Perelson, A.S., Nelson, P.W.: Mathematical analysis of HIV-1 dynamics in vivo. SIAM Rev. 41(1), 3–44 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  23. Sahani, S.K.: Effects of intracellular delay and immune response delay in HIV model. Neural Parallel Sci. Comput. 23, 357–366 (2015)

    MathSciNet  Google Scholar 

  24. Smith, H.L., De Leenheer, P.: Virus dynamics: a global analysis. SIAM J. Appl. Math. 63(4), 1313–1327 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  25. Song, X., Cheng, S.: A delay-differential equation model of hiv infection of CD4\(^+\) T-cells. J. Korean Math. Soc. 42(5), 1071–1086 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  26. Stafford, M., Cao, Y., Ho, D.D., Corey, L., Perelson, A.S.: Modeling plasma virus concentration and CD4\(^+\) T cell kinetics during primary HIV infection. No. 99-05-036 (1999)

  27. Sun, Z., Xu, W., Yang, X., Fang, T.: Effects of time delays on bifurcation and chaos in a non-autonomous system with multiple time delays. Chaos Solitons Fractals 31(1), 39–53 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  28. Wang, L., Li, M.Y.: Mathematical analysis of the global dynamics of a model for HIV infection of CD4\(^+\) T cells. Math. Biosci. 200(1), 44–57 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  29. Wein, L.M., Zenios, S.A., Nowak, M.A.: Dynamic multidrug therapies for HIV: a control theoretic approach. J. Theor. Biol. 185(1), 15–29 (1997)

    Article  Google Scholar 

  30. Wodarz, D., Nowak, M.A.: Mathematical models of HIV pathogenesis and treatment. BioEssays 24(12), 1178–1187 (2002)

    Article  Google Scholar 

Download references

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Correspondence to Saroj Kumar Sahani.

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Sahani, S.K. A Delayed Model for HIV Infection Incorporating Intracellular Delay. Int. J. Appl. Comput. Math 3, 2303–2322 (2017). https://doi.org/10.1007/s40819-016-0190-7

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