International journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN:2313-626X

Frequency: 12

line decor
  
line decor

 Volume 6, Issue 5 (May 2019), Pages: 44-49

----------------------------------------------

 Original Research Paper

 Title: Analysis of optimal control problem of HIV-1 model of engineered virus

 Author(s): Nigar Ali 1, *, Muhammad Ikhlaq Chohan 2, Sajjad Ali 1, Gul Zaman 1, Ibrahim Ibrahim 1

 Affiliation(s):

 1Department of Mathematics, University of Malakand, Chakadara Dir (L), Khyber Pakhtunkhwa, Pakistan
 2Department of Business Administration and Accounting, Buraimi University College, Al-Buraimi, Oman

  Full Text - PDF          XML

 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0002-6920-3194

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2019.05.008

 Abstract:

In this article, an optimal control problem of HIV-1 infection model consists of pathogen virus and engineered virus is taken into account. The purpose of this work is to investigate an optimal control model of drug treatment of HIV infection of genetically modified virus and CD4+T-cells. The optimal control problem is to design an effective drug plan in order to reduce the number of infected cells and free virions for patients infected by HIV. Two kinds of treatments are used, and existence and uniqueness results for the optimal control pair are established. Pontryagins maximum principle is used to characterize the optimal levels of the controls. The results of optimality are solved numerically using MATLAB software. In the last few decades, the researchers have focused on controlling problems on similar models of HIV infection in different types of models using treatment with a single drug and similar objective functional. Many researchers have studied the HIV models consisting of the only class of pathogen virus and class of single infected cells. Here we consider the HIV-1 optimal control problem consisting of a genetically modified virus and double infected cells. 

 © 2019 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: HIV-1 model, Optimal control problem, Recombinant virus, Pontryagins maximum principle, Objective functional

 Article History: Received 26 November 2018, Received in revised form 9 March 2019, Accepted 13 March 2019

 Acknowledgement:

No Acknowledgement. 

 Compliance with ethical standards

 Conflict of interest:  The authors declare that they have no conflict of interest.

 Citation:

 Ali N, Chohan MI, and Ali S et al. (2019). Analysis of optimal control problem of HIV-1 model of engineered virus. International Journal of Advanced and Applied Sciences, 6(5): 44-49

 Permanent Link to this page

 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6

 Tables

 No Table

----------------------------------------------

 References (16)

  1. Adams BM, Banks HT, Davidian M, Kwon HD, Tran HT, Wynne SN, and Rosenberg ES (2005). HIV dynamics: Modeling, data analysis, and optimal treatment protocols. Journal of Computational and Applied Mathematics, 184(1): 10-49. https://doi.org/10.1016/j.cam.2005.02.004   [Google Scholar]
  2. Ali N and Zaman G (2016). Asymptotic behavior of HIV-1 epidemic model with infinite distributed intracellular delays. SpringerPlus, 5(1): 324-337.  https://doi.org/10.1186/s40064-016-1951-9   [Google Scholar] PMid:27066352 PMCid:PMC4789014
  3. Ali N, Zaman G, and Algahtani O (2016). Stability analysis of HIV-1 model with multiple delays. Advances in Difference Equations, 2016: 88. https://doi.org/10.1186/s13662-016-0808-4   [Google Scholar]
  4. Ali N, Zaman G, and Alshomrani AS (2017). Optimal control strategy of HIV-1 epidemic model for recombinant virus. Cogent Mathematics, 4(1): 1293468. https://doi.org/10.1080/23311835.2017.1293468   [Google Scholar]
  5. Boltyanskii VGE, Gamkrelidze RVY, and Pontryagin LS (1960). The theory of optimal processes. I. The maximum principle. Izvestiya Rossiiskoi Akademii Nauk. Seriya Matematicheskaya, 24(1): 3-42.   [Google Scholar]
  6. Cohen MS, Chen YQ, McCauley M, Gamble T, Hosseinipour MC, Kumarasamy N, and Godbole SV (2011). Prevention of HIV-1 infection with early antiretroviral therapy. New England Journal of Medicine, 365(6): 493-505. https://doi.org/10.1056/NEJMoa1105243   [Google Scholar] PMid:21767103 PMCid:PMC3200068
  7. Fister KR, Lenhart S, and McNally JS (1998). Optimizing chemotherapy in an HIV model. Electronic Journal of Differential Equations, 1998(32): 1-12.   [Google Scholar]
  8. Fleming WH and Rishel RW (1975). Deterministic and stochastic optimal control. Springer Verlag, New York, USA. https://doi.org/10.1007/978-1-4612-6380-7   [Google Scholar]
  9. Garira W, Musekwa SD, and Shiri T (2005). Optimal control of combined therapy in a single strain HIV-1 model. Electronic Journal of Differential Equations, 2005(52): 1-22.   [Google Scholar]
  10. Jordan MR, Bennett DE, Wainberg MA, Havlir D, Hammer S, Yang C, and Nachega JB (2012). Update on world health organization HIV drug resistance prevention and assessment strategy: 2004–2011. Clinical Infectious Diseases, 54(suppl_4): S245-S249. https://doi.org/10.1093/cid/cis206   [Google Scholar]
  11. Joshi HR (2002). Optimal control of an HIV immunology model. Optimal Control Applications and Methods, 23(4): 199-213. https://doi.org/10.1002/oca.710   [Google Scholar]
  12. Kirschner D, Lenhart S, and Serbin S (1997). Optimal control of the chemotherapy of HIV. Journal of Mathematical Biology, 35(7): 775-792. https://doi.org/10.1007/s002850050076   [Google Scholar] PMid:9269736
  13. Pontryagin LS, Boltyanskii VG, Gamkredligze RW, and Mishchenko EF (1962). The mathematical theory of optimal processes. Wiley, New York, USA.   [Google Scholar]
  14. Revilla T and Garcı́a-Ramos G (2003). Fighting a virus with a virus: A dynamic model for HIV-1 therapy. Mathematical Biosciences, 185(2): 191-203. https://doi.org/10.1016/S0025-5564(03)00091-9   [Google Scholar]
  15. Richter A, Brandeau ML, and Owens DK (1999). An analysis of optimal resource allocation for prevention of infection with human immunodeficiency virus (HIV) in injection drug users and non-users. Medical Decision Making, 19(2): 167-179. https://doi.org/10.1177/0272989X9901900207   [Google Scholar] PMid:10231079
  16. Zhou X, Song X, and Shi X (2008). A differential equation model of HIV infection of CD4+ T-cells with cure rate. Journal of Mathematical Analysis and Applications, 342(2): 1342-1355. https://doi.org/10.1016/j.jmaa.2008.01.008   [Google Scholar]