Brodersen, K.H., Gallusser, F., Koehler, J., Remy, N. and Scott, S.L. (2015) Inferring Causal Impact Using Bayesian Structural Time-Series Models. The Annals of Applied Statistics, 9, 247-274.
https://doi.org/10.1214/14-AOAS788

has been cited by the following article:

• TITLE: Modelling HIV/AIDS Cases in Zambia: A Comparative Study of the Impact of Mandatory HIV Testing

  AUTHORS: Edwin Moyo, James C. Shakalima, Gilbert Chambashi, James Muchinga, Levy K. Matindih

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

  JOURNAL NAME: Open Journal of Statistics, Vol.11 No.3, June 25, 2021

  ABSTRACT: 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.