Abstract

EBHUOMA, O; GEBRESLASIE, M  and  MAGUBANE, L. A Seasonal Autoregressive Integrated Moving Average (SARIMA) forecasting model to predict monthly malaria cases in KwaZulu-Natal, South Africa. SAMJ, S. Afr. med. j. [online]. 2018, vol.108, n.7, pp.573-578. ISSN 2078-5135.  http://dx.doi.org/10.7196/samj.2018.v108i7.12885.

BACKGROUND. 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. OBJECTIVES. To develop a forecasting model to predict malaria cases in KZN using the Seasonal Autoregressive Integrated Moving Average (SARIMA) time series approach. METHODS. 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. RESULTS. 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. CONCLUSIONS. 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.

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