Ebhuoma O, Gebreslasie M, Magubane L
School of Agricultural, Earth and Environmental Sciences, College of Agriculture, Engineering and Science, University of KwaZulu-Natal, Durban, South Africa.
S Afr Med J. 2018 Jun 26;108(7):573-578. doi: 10.7196/SAMJ.2018.v108i7.12885.
South Africa (SA) in general, and KwaZulu-Natal (KZN) Province in particular, have stepped up efforts to eliminate malaria. To strengthen malaria control in KZN, a relevant malaria forecasting model is important.
To develop a forecasting model to predict malaria cases in KZN using the Seasonal Autoregressive Integrated Moving Average (SARIMA) time series approach.
The study was carried out retrospectively using a clinically confirmed monthly malaria case dataset that was split into two. The first dataset (January 2005 - December 2013) was used to construct a SARIMA model by adopting the Box-Jenkins approach, while the second dataset (January - December 2014) was used to validate the forecast generated from the best-fit model.
Three plausible models were identified, and the SARIMA (0,1,1)(0,1,1)12 model was selected as the best-fit model. This model was used to forecast malaria cases during 2014, and it was observed to fit closely with malaria cases reported in 2014.
The SARIMA (0,1,1)(0,1,1)12 model could serve as a useful tool for modelling and forecasting monthly malaria cases in KZN. It could therefore play a key role in shaping malaria control and elimination efforts in the province.
总体而言，南非，尤其是夸祖鲁 - 纳塔尔省，已加大了消除疟疾的力度。为加强夸祖鲁 - 纳塔尔省的疟疾防控，一个相关的疟疾预测模型至关重要。
采用季节性自回归积分滑动平均（SARIMA）时间序列方法，开发一个预测夸祖鲁 - 纳塔尔省疟疾病例的模型。
该研究采用回顾性研究方法，使用经临床确诊的每月疟疾病例数据集，该数据集被分为两部分。第一个数据集（2005年1月 - 2013年12月）采用博克斯 - 詹金斯方法构建SARIMA模型，而第二个数据集（2014年1月 - 12月）用于验证由最佳拟合模型生成的预测。
确定了三个合理的模型，SARIMA（0,1,1）（0,1,1）12模型被选为最佳拟合模型。该模型用于预测2014年的疟疾病例，发现其与2014年报告的疟疾病例密切吻合。
SARIMA（0,1,1）（0,1,1）12模型可作为夸祖鲁 - 纳塔尔省每月疟疾病例建模和预测的有用工具。因此，它在该省疟疾防控和消除工作中可发挥关键作用。
