ISSN: 2756-6684
Model: Open Access/Peer Reviewed
DOI: 10.31248/AJPS
Start Year: 2018
Email: ajps@integrityresjournals.org
https://doi.org/10.31248/AJPS2021.065 | Article Number: 9AB50FFF1 | Vol.4 (1) - February 2022
Received Date: 23 December 2021 | Accepted Date: 26 February 2022 | Published Date: 28 February 2022
Authors: Gbodoti Abdulmumini Isah* and Mohammed Abdullahi
Keywords: discrete time, HIV-naives, holding time, interval transition probability, semi-Markov process, waiting time.
Semi-Markov model in discrete state and time to study the trajection among the various defined HIV/AIDs stages has been presented in this paper. The model was used to determine the staging of HIV- infected clients reported at heart-to-heart clinic of General Hospital Minna, Niger State, Nigeria for a period of 10 years. The result shows that there is no transition from asymptomatic stage of HIV to late/advance AIDs such that φ14 (n) = 0; which led to gradual increment in the graphs of interval transition probabilities for all n ∈ N. The study shows that there are some increments in the transition probabilities from stages 2, 3 and 4 to stage 1 from about 0.05745, 0.01055 and 0.00379 in the first month (n = 1) to about 0.37163, 0.17821 and 0.05862 in the forty four months (n = 44) and in the forty five months (n = 45) respectively. From the virtual transition probabilities, the graph is gradually decreasing after forty-five (45) months. The result show that φ11 (n), φ22 (n), φ33 (n) and φ44 (n)attained the values of about 0.56516, 0.50353, 0.53343 and 0.65623 respectively for the first few months or years. The percentage decrease is about 37.1%, 17.8% for stages 2, 3 in forty-four months and 5.8% for stage 4 in forty-five month duration. However, client’s transitions dropped slowly and attain stability of stage at infinity. In compliance to the medical view, all HIV-naïve patients transit to AIDs stage especially when therapeutic intervention is lacking. The model established in this study could assist the Medical Personnels’ (MP), Health Care Providers (HCP), Epidemiologists, Medical Statisticians and other funding organizations to plan for the treatment, tracking and intervention for the ever increasing scourge of HIV/AIDs.
Basta, T. B., Reece, M., & Wilson, M. G. (2008). Predictors of exercise stage of change among individuals living with HIV/AIDS. Medicine and Science in Sports and Exercise, 40(9), 1700-1706. Crossref |
||||
Bellman, R. (1957). Dynamic programming. Princeton University Press. | ||||
Centers for Disease Control (CDC). (1997). Revised guidelines for performing CD4+ T-cell determinations in persons with human immunodeficiency virus. Morbidity and Mortality Weekly Report, 46(2), 1-29. | ||||
Cox, D. R. (1972). Regression models and lifeātables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187-202. Crossref |
||||
Gillaizeau, F., Giral, M., Dantan, E., Dragun, D., Soulillou, J. P., & Foucher, Y. (2014). Multi-state analysis of kidney transplant recipients outcome: a semi-Markov model for studying the role of pre-transplant sensitization against Angiotensin II Type 1 receptor. Journal de la Société Française de Statistique, 155(1), 117-133. | ||||
Howard R. A (1971). Dynamic probabilistic systems (Vol 1 and 2). John Wiley, New York. | ||||
Kay, R. (1986). A Markov model for analysing cancer markers and disease states in survival studies. Biometrics, 42, 855-865. Crossref |
||||
Laird, A. E. (2013). Modeling a progressive disease process under panel observation. Doctoral dissertation, University of Washington. | ||||
Welte, T. M., Vatn, J., & Heggset, J. (2006, June). Markov state model for optimization of maintenance and renewal of hydro power components. In 2006 International Conference on Probabilistic Methods Applied to Power Systems (pp. 1-7). IEEE. Crossref |