In this study, a time series modeling approach is used
to determine an ARIMA model and advance
counterfactual forecasting at a point of policy intervention. We consider
monthly data of HIV/AIDS cases from the Ministry of Health (Copperbelt
province) of Zambia, for the period 2010 to 2019 and have a
total of 120 observations. Results indicate that ARIMA (1,0,0) is an adequate model which best fits the HIV/AIDS
time series data and is, therefore, suitable for forecasting cases. The model
predicts a reduction from an average of 3500 to 3177 representing 14.29% in
HIV/AIDS cases from 2017 (year of policy activation) to 2019, but the actual
recorded cases dropped from 3500 to 1514 accounting for 57.4% in the same time
frame.
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