CHAPTER TWO
LITERATURE REVIEW
2.0 Introduction
This chapter presents a review of related literature based on the views and findings of various scholars and researchers.
2.1 Distribution of HIV/AIDS Among Children Below 15 Years
HIV/AIDS is a global epidemic caused by the Human Immunodeficiency Virus (HIV), which weakens the human immune system. The epidemic was first recognized in the early 1980s. Since then, approximately 20 million people have died, and about 38 million people are currently living with HIV worldwide (MOH, 2005). Despite global efforts, infection rates continue to rise in many parts of the world, and the disease remains unevenly distributed.
HIV/AIDS poses a serious development challenge, affecting livelihoods and undermining social and economic progress globally. Although there have been increased investments, political commitment, and expanded access to treatment, the epidemic continues to evolve. It spreads rapidly and adapts to new transmission opportunities, leading to a steady increase in the number of people living with HIV/AIDS.
Since its emergence, researchers have made significant efforts to develop a cure or vaccine. However, due to the complex nature of the virus, no complete cure or fully effective vaccine has been discovered. Existing antiretroviral therapies (ART) can only slow down the progression of the virus rather than eliminate it. Additionally, the high cost of these drugs remains a major challenge, particularly in developing countries (UNAIDS, 2004).
HIV/AIDS affects all regions of the world, although its impact varies significantly. Sub-Saharan Africa (SSA) remains the most affected region, bearing the greatest burden of the epidemic. Approximately 68% (22.5 million) of all people living with HIV are found in this region (UNAIDS, 2010). Furthermore, about 90% of countries classified as having generalized epidemics are located in SSA.
Although SSA accounts for only about 10% of the global population, it represents nearly 25.8 million people living with HIV/AIDS. In 2005 alone, about 3.2 million people were newly infected, while 2.4 million died from AIDS-related illnesses. Among young people aged 15–24 years, infection rates were higher among females (4.6%) compared to males (1.7%) (UNAIDS, 2005). In 2010, approximately 2.7 million new infections were recorded globally, with a large proportion occurring in this region.
Knowledge and awareness play a critical role in combating HIV/AIDS. Adequate understanding of the disease helps individuals make informed decisions, while misconceptions can increase vulnerability. Evidence from UNAIDS (2005) indicates that countries that invested heavily in HIV education and awareness programs experienced significant reductions in new infections. Additionally, studies show that young people exposed to comprehensive sexual education are more likely to delay sexual activity or practice safe sex, contrary to the belief that such education promotes risky behavior (UNAIDS, 2003; UNFPA, 2003).
2.2 Forecast of HIV/AIDS Prevalence Among Children Below 15 Years in Uganda
Trends in HIV Incidence (2010–2013) Using Mathematical Modelling
| Population Category | 2010 | 2011 | 2012 | 2013 |
|---|---|---|---|---|
| Adults (≥15 years) | 129,133 | 134,634 | 139,178 | 131,279 |
| Children (<15 years) | 27,139 | 27,660 | 15,411 | 15,411 |
| Total | 156,272 | 162,294 | 154,589 | 140,908 |
Source: MOH Spectrum Estimates (2013)
The data above illustrates trends in HIV incidence between 2010 and 2013. It shows fluctuations in infection rates among both adults and children, with a noticeable decline among children under 15 years after 2011.
(Graphical representation to be included)
CHAPTER THREE
METHODOLOGY
3.0 Introduction
This chapter describes the methods that will be used to conduct the study and collect the required data. It covers the research design, study area, data sources, data processing methods, data analysis techniques, and anticipated limitations.
3.1 Data Processing and Analysis Techniques
Data processing will involve editing to identify and correct errors or omissions, as well as coding to organize responses into meaningful categories. Appropriate software tools will be used for data entry, tabulation, analysis, and report preparation.
The study will employ both time series analysis and descriptive statistics. Descriptive statistics will be used to summarize and present data using tables and graphs. Data analysis will also involve applying statistical techniques to interpret findings in relation to the research questions and objectives.
3.2 Descriptive Analysis
3.2.1 Time Series Analysis
Given that the data is time-based, the analysis will focus on identifying trends and seasonal variations in HIV prevalence. A multiplicative time series model will be applied as follows:
[
Y_t = T_t \times S_t
]
Where:
- (Y_t) represents the observed series (HIV prevalence)
- (T_t) represents the trend component
- (S_t) represents the seasonal component
The study will utilize the Autoregressive Integrated Moving Average (ARIMA) model, also known as the Box-Jenkins methodology, for forecasting HIV prevalence among children below 15 years.
ARIMA Modelling Procedure
The ARIMA model assumes that the time series data is stationary. If the data is not stationary, differencing will be applied until stationarity is achieved. The ARIMA model is expressed as ARIMA (p, d, q), where:
- p = number of autoregressive terms
- d = number of differences required to achieve stationarity
- q = number of moving average terms
a. Identification
This step involves determining the appropriate values of p, d, and q using correlograms (ACF and PACF plots). If both autocorrelation and partial autocorrelation functions cut off at certain lags, the appropriate model structure can be identified.
b. Estimation
This involves estimating the parameters of the AR and MA components using nonlinear estimation techniques.
c. Diagnostic Checking
The selected model will be tested to ensure it adequately fits the data. Residuals will be examined to confirm whether they behave like white noise. If they do not, the model will be revised.
d. Forecasting
Forecasts will be generated using the fitted ARIMA model. Exponential smoothing methods may also be applied to improve prediction accuracy. These approaches account for patterns and correlations within the time series data.
Finally, HIV prevalence for the year 2016 will be forecasted by regressing HIV prevalence against time.
