Skip to main content

Advertisement

Log in

Dynamics of spatio-temporal HIV–AIDS model with the treatments of HAART and immunotherapy

  • Published:
International Journal of Dynamics and Control Aims and scope Submit manuscript

Abstract

In this paper, we focus on the study of HIV–AIDS model in space and time that is adapted from the previous study in Ammi et al. (Sci Rep 12:5751, 2022) without the fractional-order derivative. The fixed controls of highly antiretroviral and immunotherapy are considered for the interaction between susceptible and infected CD4\(^+\)T cell. The equilibrium points of disease-free and endemic, positivity, boundedness and basic reproduction number of dynamical system are provided in the standard ways. For the local stability, the Fourier series is firstly employed to obtain the Jacobian matrix which is then used for further analysis of stability. Moreover, the classical numerical scheme of standard finite difference is applied to approximate our model. The stability, positivity, and consistency of numerical scheme are very important in the numerical analysis. At the last section of numerical analysis, we provide the experiments of our model numerically by varying values for the parameters of treatment HAART and Immunotherapy. We can conclude that the combinations of HAART and Immunotherapy at once are the most efficient in decreasing the infected CD4\(^+\)T cells and the treatment of immunotherapy is more effective than the treatment of HAART. Our dynamical system is eligible to predict the spread of HIV–AIDS based on the validation results with the actual data by using least square technique. Moreover, we apply the ARIMA(1,1,0) model in this paper to predict infected profile and the result has the similar trend (decreasing trend) with the HIV–AIDS model (obtained from the least square technique) and the actual data. Moreover, we employ the neural network for dynamical system, due to the significant results of best validation performance, error histogram, and regression.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Campbell EM, Jia H, Shankar A, Hanson D, Luo W, Masciotra S, Blosser SJ (2017) Detailed transmission network analysis of a large opiate-driven outbreak of HIV infection in the United States. J Infect Dis 216(9):1053–62

    Google Scholar 

  2. Qiao YC, Xu Y, Jiang DX, Wang X, Wang F, Yang J, Wei YS (2019) Epidemiological analyses of regional and age differences of HIV/AIDS prevalence in China, 2004–2016. Int J Infect Dis 81:215–20

    Google Scholar 

  3. Paraskevis D, Nikolopoulos G, Tsiara C, Paraskeva D, Antoniadou A, Lazanas M, Hatzakis A (2011) HIV-1 outbreak among injecting drug users in Greece, 2011: a preliminary report. Eurosurveillance 16(36):19962

    Google Scholar 

  4. Doitsh G, Greene WC (2016) Dissecting how CD4 T cells are lost during HIV infection. Cell Host Microbe 19(3):280–91

    Google Scholar 

  5. Cummins NW, Badley AD (2014) Making sense of how HIV kills infected CD4 T cells: implications for HIV cure. Mol Cell Ther 2(1):20

    Google Scholar 

  6. Poorolajal J, Hooshmand E, Mahjub H, Esmailnasab N, Jenabi E (2016) Survival rate of AIDS disease and mortality in HIV-infected patients: a meta-analysis. Public Health 139:3–12

    Google Scholar 

  7. Alemu A, Yesuf A, Zerihun B, Getu M, Worku T, Bitew ZW (2020) Incidence and determinants of tuberculosis among HIV positives in Addis Ababa, Ethiopia: a Retrospective Cohort Study. Int J Infect Dis 95:59

    Google Scholar 

  8. WHO (2018) Global Tuberculosis Report Geneva

  9. Ramírez BC, Vega YC, Shepherd BE, Le C, Turner M, Frola C, McGowan CC (2017) Outcomes of HIV-positive patients with cryptococcal meningitis in the Americas. Int J Infect Dis 63:57–63

    Google Scholar 

  10. Rajasingham R, Smith RM, Park BJ, Jarvis JN, Govender NP, Chiller TM, Boulware DR (2017) Global burden of disease of HIV-associated cryptococcal meningitis: an updated analysis. Lancet Infect Dis 17(8):873–81

    Google Scholar 

  11. Mekonnen Y, Hadush T, Tafere A, Tilahun A (2016) A review article on cryptosporidiosis

  12. Sullivan MC, Rosen AO, Allen A, Benbella D, Camacho G, Cortopassi AC, Kalichman SC (2020) Falling Short of the First 90: HIV Stigma and HIV Testing Research in the 90-90-90 Era

  13. Li D, Ma W (2007) Asymptotic properties of a HIV-1 infection model with time delay. J Math Anal Appl 335:683–91

    MathSciNet  Google Scholar 

  14. Abdullahi YM, Nweze O (2011) A simulation of an sir mathematical model of HIV transmission dynamics using the classical Euler’s method. Shiraz Med J 12:196–202

    Google Scholar 

  15. Jiang X, Chen X, Huang T, Yan H (2021) Bifurcation and control for a predator-prey system with two delays. IEEE Trans Circuits Syst II Express Briefs 68(1):376–80

    Google Scholar 

  16. Jiang X, Chen X, Chi M, Chen J (2020) On Hopf bifurcation and control for a delay systems. Appl Math Comput 370(1):124906

    MathSciNet  Google Scholar 

  17. Asif M, Ullah Jan S, Haider N, Mdallal QA, Jawad TA (2020) Numerical modeling of NPZ and SIR models with and without diffusion. Result Phys 19:103512

    Google Scholar 

  18. Asif M, Khan ZA, Haider N, Mdallal QA (2020) Numerical simulation for solution of SEIR models by meshless and finite difference methods. Chaos Soliton Fractals 141:110340

    MathSciNet  Google Scholar 

  19. Hajji MA, Mdallal QA (2018) Numerical simulations of a delay model for immune system tumor interaction. Sultan Qaboos Univ J Sci 23(1):19–31

    Google Scholar 

  20. Rihan FA, Al-Mdallal QM, AlSakaji HJ, Hashish A (2019) A fractional-order epidemic model with time-delay and nonlinear incidence rate. Chaos Solitons Fractals 126:97–105

    MathSciNet  Google Scholar 

  21. Ullah I, Ahmad S, Mdallal QA, Khan ZA, Khan H, Khan A (2020) Stability analysis of a dynamical model of tuberculosis with incomplete treatment. Adv Differ Equ. https://doi.org/10.1186/s13662-020-02950-0

    Article  MathSciNet  Google Scholar 

  22. Kumar S, Chauhan RP, Abdel-Aty AH, Alharthi MR (2021) A study on transmission dynamics of HIV/AIDS model through fractional operators. Results Phys 22:103855

    Google Scholar 

  23. Yu P, Zhang W (2019) Complex dynamics in a unified SIR and HIV disease model: a bifurcation theory approach. J Nonlinear Sci 29(5):2447

    MathSciNet  Google Scholar 

  24. Dhar M, Bhattacharya P (2019) Analysis of SIR epidemic model with different basic reproduction numbers and validation with HIV and TSWV data, Iran. J Sci Technol Trans A Sci 43(5):2385

    MathSciNet  Google Scholar 

  25. Naik PA, Zu J, Owolabi KM (2020) Global dynamics of a fractional order model for the transmission of HIV epidemic with optimal control. Chaos, Solitons Fractals 138:109826

    MathSciNet  Google Scholar 

  26. Wang X, Wang W, Li Y (2021) Global stability of switched HIV/AIDS models with drug treatment involving caputo-fractional derivatives. Discret Dyn Nat Soc 138:109826

    MathSciNet  Google Scholar 

  27. Babaei A, Jafari H, Liya A (2020) Mathematical models of HIV/AIDS and drug addiction in prisons. Eur Phys J Plus 135(5):1

    Google Scholar 

  28. Chang H (2021) A mathematical study on the drug resistant virus emergence with HIV/AIDS treatment cases. Heliyon 7(1):e05883

    Google Scholar 

  29. Bassey BE, Henry AO (2021) Global stability analysis of the role of antiretroviral therapy (ART) abuse in HIV/AIDS treatment dynamics. Pure Appl Math J 10(1):9

    Google Scholar 

  30. Wanduku (2020) A nonlinear multi-population behavioral model to assess the roles of education campaigns, random supply of aids, and delayed ART treatment in HIV/AIDS epidemics. Math Biosci Eng 17(6):6791

    MathSciNet  Google Scholar 

  31. Teklu SW, Mekonnen TT (2021) HIV/AIDS-pneumonia coinfection model with treatment at each infection stage: mathematical analysis and numerical simulation. J Appl Math 2021:1–21

    MathSciNet  Google Scholar 

  32. Kumar S, Chauhan RP, Momani S, Hadid S (2020) Numerical investigations on COVID-19 model through singular and non-singular fractional operators. Numer Methods Partial Differ Equ. https://doi.org/10.1002/num.22707

    Article  Google Scholar 

  33. Khan MA, Ullah S, Kumar S (2021) A robust study on 2019-nCOV outbreaks through non-singular derivative. Eur Phys J Plus. https://doi.org/10.1140/epjp/s13360-021-01159-8

    Article  Google Scholar 

  34. Kumar S, Kumar A, Samet B, Dutta H (2020) A study on fractional host-parasitoid population dynamical model to describe insect species. Numer Methods Partial Differ Equ 37:1673–1692

    MathSciNet  Google Scholar 

  35. Mohammadi H, Kumar S, Rezapour S, Etemad S (2021) A theoretical study of the Caputo-Fabrizio fractional modeling for hearing loss due to Mumps virus with optimal control. Chaos, Solitons Fractals 144:110668

    MathSciNet  Google Scholar 

  36. Kumar S, Kumar R, Cattani C, Samet B (2020) Chaotic behaviour of fractional predator-prey dynamical system. Chaos, Solitons Fractals 135:109811

    MathSciNet  Google Scholar 

  37. Kumar S, Kumar R, Osman MS, Samet B (2021) A wavelet based numerical scheme for fractional order SEIR epidemic of measles by using Genocchi polynomials. Numer Methods Partial Differ Equ 37:1250–1268

    MathSciNet  Google Scholar 

  38. Ammi MRS, Tahiri M, Tilioua M, Zeb A, Khan I, Andualem M (2022) Global analysis of a time fractional order spatio-temporal SIR model. Sci Rep 12:5751

    Google Scholar 

  39. Fraser C, Donnelly CA, Cauchemez S et al (2009) Pandemic potential of a strain of influenza A (H1N1): early findings. Science 324:1557–1561

    Google Scholar 

  40. Chakrabrty A, Singh M, Lucy B, Ridland P (2007) Predator-prey model with pry-taxis and diffusion. Math Comput Model 46:482–98

    Google Scholar 

  41. Ahmed N, Tahira SS, Rafiq M, Rehman MA, Ali M, Ahmad MO (2019) Positivity preserving operator splitting nonstandard finite difference methods for SEIR reaction diffusion model. Open Math 17:313–30

    MathSciNet  Google Scholar 

  42. Lin Z, Pederson M (2004) Stability in a di usive food-chain model with Michaelis-Menten functional response. Nonlinear Anal 57:421–433

    MathSciNet  Google Scholar 

  43. Ahmed N, Rafiq M, Rehman MA, Iqbal MS, Ali M (2019) Numerical modeling of three dimensional Brusselator system. AIP Adv 09(01):01–19

    Google Scholar 

  44. Charpentier BMC, Kojouharov HV (2013) Unconditionally positive preserving scheme for advection-diffusion-reaction equations. Math Comput Model 57(10):2177–1285

    Google Scholar 

  45. Fujimoto T, Ranade R (2004) Two characterizations of inverse positive matrices: the Hawkins-Simon condition and the le chatelier-braun principle. Electron J Linear Algebra ELA 11(1):01–18

    MathSciNet  Google Scholar 

  46. Smith GD (1985) Numerical solution of partial differential equations: finite difference methods, 3rd edn. Clarendon Press, Oxford

    Google Scholar 

  47. Mitchell AR, Griffiths DF (1980) Finite difference methods in partial differential equations. T. Wiley, Hoboken

    Google Scholar 

  48. Chinviriyasit S, Chinviriyasit W (2010) Numerical modelling of an SIR epidemic model with diffusion. Appl Math Comput 216:395–409

    MathSciNet  Google Scholar 

  49. Kustiawan C (2018) A Numerical scheme for a reaction diffusion equation with time delay and impulses. Far East J Math Sci 106(2):451–62

    Google Scholar 

  50. Ahmed N, Elsonbaty A, Adel W, Baleanu D, Rafiq M (2020) Stability analysis and numerical simulations of spatiotemporal HIV CD4+ T cell model with drug therapy. Chaos 30:083122

  51. Samsuzzoha M, Singh M, Lucy D (2013) Parameter estimation of influenza epidemic model. Appl Math Comput 220:616

    MathSciNet  Google Scholar 

  52. Kristensen MR (2014) Parameter estimation in nonlinear dynamical systems Master’s Thesis. Technical University of Denmark, Kongens

Download references

Funding

Mohammad Ghani received financial support for the research under Contract Number 121/UN3.1.17/PT/2022 by the Faculty of Advanced Technology and Multidiscipline, Universitas Airlangga, Indonesia.

Author information

Authors and Affiliations

Authors

Contributions

MG contributed to formal analysis, investigation, methodology, software, writing—original draft, and writing—review and editing.

Corresponding author

Correspondence to Mohammad Ghani.

Ethics declarations

Conflict of interest

None of the authors of this paper has a financial or personal relationship with other people or organizations that could inappropriately influence or bias the content of the paper. It is to specifically state that “No Competing interests are at stake and there is No Conflict of Interest” with other people or organizations that could inappropriately influence or bias the content of the paper.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ghani, M. Dynamics of spatio-temporal HIV–AIDS model with the treatments of HAART and immunotherapy. Int. J. Dynam. Control 12, 1366–1391 (2024). https://doi.org/10.1007/s40435-023-01284-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40435-023-01284-5

Keywords

Mathematics Subject Classification

Navigation