Abstract
A total of 1094 HIV patients were involved in a cohort study (from January-December 2010) with follow-up in their CD4 cell transition counts and grouped according to their immunological states into five(5) states developed by Guiseppe Di Biase et al (2007). The five states (5) considered were: State one (CD4 > 500 cells/mm3 ), State two (350 < CD4 500 cells /mm3 ) State three(200 < CD4 350 cells/mm3 ), State four(CD4 200 cells/mm3 ), State five(Death). These states de ne the seriousness of the sickness based on the epidemiological states of the patients CD4 cell counts. We use the non-stationary Markov chain model for the prediction. The estimation of the non-stationary probabilities were done using the exponential smoothing technique. The result of the prediction showed a gradual decrease of the CD4 cells as we move from Jan-Dec. Furthermore, the result showed that the patients in the study cannot survive death from the month Dec. 2011, if they are not subjected to therapy, using highly active antiretrovirals (HAART). The results also showed that the model can be used for the testing of the drug e efficacy administered to patients within a given period.
Highlights
Since the outbreak of human immunodeficiency virus (HIV)/AIDS epidemic in Nigeria in 1981 and 1983 respectively, cases of the disease have been reported in all the thirty-six (36) states of Nigeria, including Abuja, the Federal capital territory
The prediction of the CD4 cell counts with the optimal smoothing constant for the months of JanDec 2011, showed some interesting results
We observe a gradual decrease of the CD4 cell counts from the months of Jan-Dec, 2011
Summary
Since the outbreak of HIV/AIDS epidemic in Nigeria in 1981 and 1983 respectively, cases of the disease have been reported in all the thirty-six (36) states of Nigeria, including Abuja, the Federal capital territory. Many studies have been carried out on HIV/AIDS, e.g life expectancy of patients [See Osisiogu U. A. and Nwosu (2013)] Zero-prevalence rate of the disease [See. The infection has become the number one cause of death for persons between 25-44 years of age [Centre for disease control and prevention (CDC)(1995)]. The study provides an example of the use of epidemiological data such as the CD4 cell counts for estimating and projecting the impact of HIV/AIDS epidemics using the non-stationary Markov chain models. 96 Predicting Future CD4 Cell Counts of HIV/AIDS Patients by Non Stationary Markov Chain the human immunodeficiency syndrome (AIDS) and which ends to death. We shall use the non-stationary Markov chain model in this paper for the analysis in predicting future CD4 cell counts of HIV/AIDS patients
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