Mathematical Model for Transmission Dynamics of HIV/ADS and HSV-II Co-infection

Authors

  • Eshetu Dadi Gurmu Department of Mathematics, Natural Science, Wollega University, Nekemte, Ethiopia http://orcid.org/0000-0003-3209-5844
  • Boka Kumsa Bole Department of Mathematics, Natural Science, Wollega University, Nekemte, Ethiopia
  • Purnachandra Rao Koya Department of Mathematics, Natural Science, Wollega University, Nekemte, Ethiopia

Keywords:

Co-infection, HIV/AIDS, HSV-II, Mathematical model, Stability analysis.

Abstract

In this paper, a mathematical model of HIV/AID and HSV-II co-infection has been formulated and analyzed. The main aim of this study was to give awareness for the community on the transmission dynamics of the disease. The well possedness of the formulated model equations was proved and the equilibrium points of the model have been identified. Qualitative analysis of the formulated model was established using basic reproduction number. The results show that the disease free equilibrium is locally asymptotically stable if the basic reproduction is less than one. The endemic equilibrium of the model equations are considered to exist when the basic reproduction number for each disease is greater than one. Finally, numerical simulations of the model equations are carried out using the software MATLAB R2015b with ODE45 solver. Numerical simulations illustrated that as we increase force of infection, the infections increases.

Author Biographies

Eshetu Dadi Gurmu, Department of Mathematics, Natural Science, Wollega University, Nekemte, Ethiopia

Mathematics

Boka Kumsa Bole, Department of Mathematics, Natural Science, Wollega University, Nekemte, Ethiopia

Mathematics

Purnachandra Rao Koya, Department of Mathematics, Natural Science, Wollega University, Nekemte, Ethiopia

Mathematics

References

D. Wodarz, Killer Cell Dynamics: Mathematical and Computational Approaches to Immunology. New York: Springer Verlag, 2007.

A. Mhlanga, A theoretical model for the transmission dynamics of HIV/HSV-2 co-infection in the presence of poor HSV-2 treatment adherence, Applied Mathematics and Nonlinear Sciences, 3(2), 2018, 603–626.

CDC, Genital Herpes Information, http://www.cdc.gov/std/Herpes/default.htm, 2001.

World Health Organization, WHO Guidelines for the Treatment of Genital Herpes Simplex Virus, 2016.

L. A. Wheeler, Silencing sexually transmitted infections: Topical siRNA-based interventions for the prevention of HIV and HSV, Infectious Diseases in Obstetrics and Gynecology, 2014, 125087, 11 pages.

World Health Organization and the Joint United Nations Programme on HIV/AIDS, Herpes simplex virus type 2: Programmatic and research priorities in developing countries, WHO/UNAIDS/LSHTM Workshop, London, UK, 2001.

A. Mhlanga, C. P. Bhunu and S. Mushayabasa, A computational study of HSV-2 with poor treatment adherence, Abstract and Applied Analysis, 2015, 850670, 15 pages.

Z. Mukandavire, K. M. Mitchell and P. Vickerman, Comparing the impact of increasing condom use or HIV pre-exposure prophylaxis (PrEP) use among female sex workers, Epidemics, 14, 2016, 62-70.

H. W. Hethcote, Qualitative analyses of communicable disease models, Mathematical Biosciences, 28(3-4), 1976, 335–356.

N. C. Grassly and C. Fraser, Mathematical models of infectious disease transmission, Nature Reviews Microbiology, 6(6), 2008, 477–487.

E. D. Gurmu and P. R. Koya, Impact of chemotherapy treatment of SITR compartmentalization and modeling of human papilloma virus (HPV), IOSR Journal of Mathematics, 15(3), 2019, 17-29.

J. Karrakchou, M. Rachik and S. Gourari, Optimal control and infectiology: Application to an HIV/AIDS model, Applied Mathematics and Computation, 177, 2006, 807–818.

G. G. Sanga, O. D. Makinde, E. S. Massawe and L. Namkinga, Modelling co-dynamics of cervical cancer and HIV disease, Global Journal of Pure and Applied Mathematics, 13(6), 2017, 2057-2078.

L. J. Abu-Raddad, J. T. Schiffer, R. Ashley, G. Mumtaz, R. A. Alsallaq, F. A. Akala, I. Semini, G. Riedner and D. Wilson, HSV-2 serology can be predictive of HIV epidemic potential and hidden sexual risk behavior in the Middle East and North Africa, Epidemics, 2(4), 2010, 173-182.

P. V. D. Driessche and J. Watmough, Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission, Mathematical Biosciences, 180(1-2), 2002, 29–48.

C. Castillo-Chavez and B. Song (2004). Dynamical models of tuberculosis and their applications, Mathematical Biosciences and Engineering, 1 (2), 2004, 361–404.

E. D. Gurmu, B. K. Bole and P. R. Koya, Mathematical modelling of HIV/AIDS transmission dynamics with drug resistance compartment, American Journal of Applied Mathematics, 8(1), 2020, 34-45.

E. D. Gurmu, B. K. Bole and P. R. Koya, Mathematical model for co-infection of HPV with cervical cancer and HIV with AIDS diseases, International Journal of Scientific Research in Mathematical and Statistical Sciences, 7(2), 2020,107-121.

M. S. Wameko, Mathematical model for transmission dynamics of hepatitis C virus with optimal control strategies, International Journal of Mathematical Modelling & Computations, 9(3), 2019, 213- 237.

Downloads

Published

20-06-2020

How to Cite

Gurmu, E. D., Bole, B. K., & Koya, P. R. (2020). Mathematical Model for Transmission Dynamics of HIV/ADS and HSV-II Co-infection. Applications of Modelling and Simulation, 4, 217–236. Retrieved from https://www.ojs.arqiipubl.com/index.php/AMS_Journal/article/view/159

Issue

Section

Articles

Most read articles by the same author(s)

Similar Articles

<< < 1 2 3 4 5 6 7 8 > >> 

You may also start an advanced similarity search for this article.