Mathematical Modelling of Prevention Measures of HIV and Aids in Kenya

References

[1] Abu-Raddad, L.J., & Boily, M.C. (2023). Mathematical modeling approaches in HIV/AIDS prevention research. AIDS Research and Prevention, 35(4), 321-339.

[2] Anderson, R.M., May, R.M., & Smith, F. (2020). The mathematics of infectious diseases with application to HIV dynamics. Mathematical Biology Review, 28(2), 110-128.

[3] Blower, S.M., Mukandavire, Z., & Mwinyi, A. (2019). Modeling the impact of prevention strategies in HIV epidemics: Current approaches and future directions. Journal of Mathematical Epidemiology, 12(3), 145-162.

[4] Center for Disease Control and Prevention [CDC]. (2023). Comprehensive HIV prevention strategies: Global perspectives and approaches. CDC Technical Report Series.

[5] Diekmann, O., & Heesterbeek, J.A.P. (2020). Mathematical epidemiology of infectious diseases: Model building, analysis and interpretation (3rd ed.). Wiley Series in Mathematical and Computational Biology.

[6] Garnett, G.P. (2022). The role of mathematical models in evaluating HIV prevention strategies. AIDS and Behavior, 26(5), 678-692.

[7] Heffernan, J.M., Smith, R.J., & Wahl, L.M. (2023). Perspectives on mathematical modeling for HIV prevention. Bulletin of Mathematical Biology, 85(2), 45-67.

[8] Kenya Ministry of Health [MOH]. (2023). National guidelines for HIV prevention and treatment. Government Printer.

[9] Kenya National AIDS Control Council [NACC]. (2023). HIV Prevention roadmap 2023-2027. Government Printer.

[10] Kenya Medical Research Institute [KEMRI]. (2023). Annual report on HIV research outcomes in Kenya. KEMRI Publications.

[11] Kimani, M., Otieno, G., & Matilu, M. (2023). Mathematical modeling of HIV transmission dynamics in Kenya. East African Medical Journal, 97(3), 234-248.

[12] Lima, V.D., Johnston, K., & Hogg, R.S. (2022). Cost-effectiveness analysis of early HIV diagnosis and treatment programs. Health Economics Review, 15(4), 412-426.

[13] National AIDS and STI Control Programme [NASCOP]. (2023). Kenya AIDS response progress report. Ministry of Health.

[14] Smith, R.J. (2023). Mathematical modeling of infectious diseases: Theory and applications to HIV. SIAM Review, 65(2), 289-310.

[15] UNAIDS. (2023). Global AIDS update 2023: Seizing the moment. United Nations Publications.

[16] UNAIDS Reference Group on Estimates, Modelling and Projections. (2023). Technical update on HIV epidemiological estimates. UNAIDS Publications.

[17] Van den Driessche, P. (2021). Stability analysis in epidemic modeling. Mathematical Biosciences, 42(3), 167-184.

[18] World Bank. (2022). Economic impact of HIV/AIDS in Sub-Saharan Africa. World Bank Technical Paper Series.

[19] World Health Organization [WHO]. (2023). HIV prevention in the era of universal test and treat: Technical guidance note. WHO Publications.

[20] WHO Technical Advisory Group on HIV Modeling. (2023). Guidelines for mathematical modeling in HIV program planning. World Health Organization.

@article{1706500,
      author = {Sirengo John Luca},
      title = {Mathematical Modelling of Prevention Measures of HIV and Aids in Kenya},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {8},
      number = {5},
      pages = {124-133},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1706500.pdf},
      abstract = {This study develops and analyzes mathematical models to understand and evaluate prevention measures for HIV (Human Immunodeficiency Virus) and AIDS (Acquired Immunodeficiency Syndrome) in Kenya, with particular focus on high-risk populations. The research employs a deterministic compartmental model using Ordinary Differential Equations (ODEs) to simulate disease transmission dynamics. The population is divided into three compartments: Susceptible (S), HIV-infected without AIDS symptoms (I), and AIDS patients (A). The model's analysis includes derivation of the basic reproduction number (R?), investigation of equilibrium points, and stability analysis. Mathematical analysis demonstrates the existence of both disease-free and endemic equilibrium points. The model is locally asymptotically stable when R? < 1, indicating effective disease control. Key findings reveal that Pre-Exposure Prophylaxis (PrEP) emerges as the most effective single intervention, potentially reducing HIV incidence significantly among Men who have Sex with Men (MSM) in Kenya. Early diagnosis shows substantial impact, while early Anti-Retroviral Therapy (ART) demonstrates limited effectiveness when implemented alone. The combined implementation of all three interventions (PrEP, early diagnosis, and early treatment) yields optimal results in HIV prevention. The study provides quantitative evidence to support policy decisions regarding HIV prevention strategies in Kenya, particularly emphasizing the importance of PrEP programs and early diagnosis initiatives. These findings have significant implications for resource allocation and public health policy in HIV/AIDS prevention programs.},
      keywords = {HIV/AIDS prevention, Mathematical modeling, ODEs, Basic reproduction number, Stability analysis, PrEP, Kenya},
      month = {November},
  }