Abstract
Using annual time series data on the number of adults (ages 15 and above) newly infected with HIV in Burundi from 1990 – 2018, the study predicts the annual number of adults who will be newly infected with HIV over the period 2019 – 2025. The study applied the Box-Jenkins ARIMA methodology. The diagnostic ADF tests as well as correlogram analysis show that the G series under consideration is an I (2) variable. Based on the AIC, the study presents the ARIMA (0, 2, 1) model as the optimal model. The residual correlogram and the inverse roots of the applied model further reveal that the presented model is stable and suitable for forecasting new HIV infections in adults in Burundi. The results of the study indicate that the number of new HIV infections in adults in Burundi will most likely decline, over the period 2019 – 2023, from approximately 698 to almost 90 new HIV infections. By 2025, Burundi could experience her first zero new HIV infections in adults! This implies that, despite the fact that Vision Burundi 2025 is a highly ambitious blue-print; Vision Burundi 2025 will largely be achieved as far as HIV/AIDS prevention and control is concerned.
Highlights
Burundi, an East African country, is one of the world’s poorest countries
This study will go a long way in assessing the possibility of ending the HIV pandemic in the country. 2.0 LITERATURE REVIEW In a Zimbabwean study, Mahomva et al (2006) investigated HIV prevalence in Zimbabwe using data reported from 4 Antenatal Clinic (ANC) surveys conducted between 2000 and 2004, 2 small local studies in Zimbabwe conducted from 1997 through 2003, 4 general population surveys from 1999 through 2003 and service statistics covering 1999 through 2004
Given the fact that HIV in Burundi is more prevalent in adults than children, this study will shade light on the possibility of reasonably controlling the spread of HIV/AIDS amongst adults in the country. 3.0 METHODODOLOGY 3.1 The Box – Jenkins (1970) Methodology The first step towards model selection is to difference the series in order to achieve stationarity
Summary
Only the AIC is used to select the optimal model. The ARIMA (0, 2, 1) model is selected. This implies that the “no autocorrelation” assumption is not violated in this work. Since all the roots lie inside the unit circle, it implies that the estimated ARIMA process is (covariance) stationary; confirming that the ARIMA (0, 2, 1) model is stable and suitable for forecasting annual number of adults newly infected with HIV in Burundi
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