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Modelling HIV/AIDS Cases in Zambia: A Comparative Study of the Impact of Mandatory HIV Testing
Department of Science and Mathematics, Mulungushi University, Kabwe, Zambia
Department of Science and Mathematics, Mulungushi University, Kabwe, Zambia
School of Business Studies, Unicaf University, Lusaka, Zambia
Department of Natural Sciences, Levy Mwanawasa University, Lusaka, Zambia
Department of Science and Mathematics, Mulungushi University, Kabwe, Zambia
- 1 Department of Science and Mathematics, Mulungushi University, Kabwe, Zambia
- 2 Department of Science and Mathematics, Mulungushi University, Kabwe, Zambia
- 3 School of Business Studies, Unicaf University, Lusaka, Zambia
- 4 Department of Natural Sciences, Levy Mwanawasa University, Lusaka, Zambia
- 5 Department of Science and Mathematics, Mulungushi University, Kabwe, Zambia
Open Journal of Statistics·Volume 11 (2021)·Pages 409–419·Published 8 May 2021·DOI10.4236/ojs.2021.113025
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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 ha ve 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.
KeywordsCounterfactual ForecastingBox-Jenkins MethodologyARIMA ModelAuto-correlation FunctionPartial Autocorrelation Function
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