Author(s)

Edwin Moyo^1*, James C. Shakalima¹, Gilbert Chambashi², James Muchinga³, Levy K. Matindih¹

¹Department of Science and Mathematics, Mulungushi University, Kabwe, Zambia.
²School of Business Studies, Unicaf University, Lusaka, Zambia.
³Department of Natural Sciences, Levy Mwanawasa University, Lusaka, Zambia.

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.

Counterfactual Forecasting, Box-Jenkins Methodology, ARIMA Model, Auto-correlation Function, Partial Autocorrelation Function

Moyo, E. , Shakalima, J. , Chambashi, G. , Muchinga, J. and Matindih, L. (2021) Modelling HIV/AIDS Cases in Zambia: A Comparative Study of the Impact of Mandatory HIV Testing. Open Journal of Statistics, 11, 409-419. doi: 10.4236/ojs.2021.113025.