%0 Journal Article
%T Modelling HIV/AIDS Cases in Zambia: A Comparative Study of the Impact of Mandatory HIV Testing
%A Edwin Moyo
%A James C. Shakalima
%A Gilbert Chambashi
%A James Muchinga
%A Levy K. Matindih
%J Open Journal of Statistics
%P 409-419
%@ 2161-7198
%D 2021
%I Scientific Research Publishing
%R 10.4236/ojs.2021.113025
%X In this study, a time series modeling approach is used
to determine an<span style=\"font-family:Verdana;\"> 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 ha</span><span style=\"font-family:Verdana;\">ve</span><span style=\"font-family:Verdana;\"> a
total of 120 observations. Results indicate that ARIMA (1,</span><span style=\"font-family:&quot;\"> </span><span style=\"font-family:Verdana;\">0,</span><span style=\"font-family:&quot;\"> </span><span style=\"font-family:Verdana;\">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.</span>
%K Counterfactual Forecasting
%K Box-Jenkins Methodology
%K ARIMA Model
%K Auto-correlation Function
%K Partial Autocorrelation Function
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=110119