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

作     者：Edwin Moyo James C. Shakalima Gilbert Chambashi James Muchinga Levy K. Matindih Edwin Moyo;James C. Shakalima;Gilbert Chambashi;James Muchinga;Levy K. Matindih

作者机构：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 

出 版 物：《Open Journal of Statistics》 (统计学期刊（英文）)

年 卷 期：2021年第11卷第3期

页      面：409-419页

学科分类：1004[医学-公共卫生与预防医学(可授医学、理学学位)] 100401[医学-流行病与卫生统计学] 10[医学] 

主　　题：Counterfactual Forecasting Box-Jenkins Methodology ARIMA Model Auto-correlation Function Partial Autocorrelation Function 

摘      要：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.