![]() ![]() HIV/AIDS and other STD have an obliterating effect on women’s health, especially the well-being of younger ladies. Since South Africa is at the epicenter of the HIV/AIDS epidemic, South African concerns are worldwide concerns, and lessons learned in South Africa are lessons for the universal community. The HIV crisis in South Africa is critical. Sub-Saharan Africa and Southern Africa, in specific, is right now the region most influenced by HIV/AIDS in the world 2. Despite recent progressions in HIV prevention, care, and treatment, which has modestly decreased the total number of new infections and deaths every year, AIDS and AIDS-related illnesses are still among the driving causes of loss of life globally. Approximately 0.8% of grownup persons in the age range fifteen to forty-nine years worldwide are living with HIV, even though the problem of the epidemic continues to vary sizably between nations and regions 1. Worldwide, 37.9 million individuals were HIV positive at the end of 2018. More than 75 million individuals have been infected with HIV, more than 32 million individuals have perished due to AIDS-related causes since the pandemic started, and 7000 new infections are reported daily. Similar content being viewed by othersĪfter it is identified by scientists as the human immunodeficiency virus (HIV) and the cause of acquired immunodeficiency syndrome (AIDS) in 1983, HIV has spread persistently, triggering one of the most severe pandemics ever documented in human history. Comparison, discussion, and conclusion of the results of the fitted models complete the study. In addition, the results imply that the effect of baseline BMI, HAART initiation, baseline viral load, and the number of sexual partners were significantly associated with the patient’s CD4 count in both fitted models. Multiple imputation techniques are also used to handle missing values in the dataset to get valid inferences for parameter estimates. The results display that the NBMM has appropriate properties and outperforms the PMM in terms of handling over-dispersion of the data. We evaluate and compare the proposed models and their application to the number of CD4 cells of HIV-Infected patients recruited in the CAPRISA 002 Acute Infection Study. The later model effectively manages the over-dispersion of the longitudinal data. Therefore, the PMM is replaced by the negative binomial mixed-effects model (NBMM). However, this model is not realistic because of the restriction that the mean and variance are equal. The Poisson mixed-effects models (PMM) can be an appropriate choice for repeated count data. Whereas, in the geometric and negative binomial distributions, the number of "successes" is fixed, and we count the number of trials needed to obtain the desired number of "successes".It is of great interest for a biomedical analyst or an investigator to correctly model the CD4 cell count or disease biomarkers of a patient in the presence of covariates or factors determining the disease progression over time. In the binomial distribution, the number of trials is fixed, and we count the number of "successes". We again note the distinction between the binomial distribution and the geometric and negative binomial distributions. This is in contrast to the Bernoulli, binomial, and hypergeometric distributions, where the number of possible values is finite. In other words, the possible values are countable. These are still discrete distributions though, since we can "list" the values. ![]() Note that for both the geometric and negative binomial distributions the number of possible values the random variable can take is infinite. In general, note that a geometric distribution can be thought of a negative binomial distribution with parameter \(r=1\). \), but now we also have the parameter \(r = 100\), the number of desired "successes".
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