Trend analysis and short-term forecast of incident HIV infection in Ghana

Abstract

The study uses time-series modelling to determine and predict trends in incident HIV infection in Ghana among specific age groups. The HIV data for Ghana were grouped according to northern and southern spatial sectors as they exhibited slightly different data collection  formats. The trend of the epidemic is modelled using moving-average smoothing techniques, and the Box-Jenkins ARIMA model is used to forecast cases of newly acquired (incident) HIV infection. Trend analysis of past growth patterns reveals an increase in new cases of HIV infection in the northern sector, with the greatest increase occurring among persons aged 30 years and over. The epidemic in the southern sector appears to have levelled off. However, incident HIV infection in the 20–39-year-old age group of females in the sector is estimated to increase in the next three years. Moreover, the estimates suggest a higher increase in incident cases than that predicted by the National AIDS Control Programme. Nevertheless, incident HIV infection among persons aged 19 and below is found to be relatively stable. Thus, if efforts are made to reduce or prevent an increase in the  number of new infections in the northern sector, and for the 20–39 years age group in the southern sector, Ghana will have a brighter future with regard to its response to the HIV epidemic. These findings  can assist with developing strategic-intervention policy planning for  Ghana and other countries in sub-Saharan Africa.

Keywords: autoregressive model; Box-Jenkins models; epidemiological models; HIV/AIDS; model selection; moving-average model; time-series analysis

African Journal of AIDS Research 2010, 9(2): 165–173