O Ebhuoma1, M Gebreslasie, L Magubane. 1. School of Agricultural, Earth and Environmental Sciences, College of Agriculture, Engineering and Science, University of KwaZulu-Natal, Durban, South Africa. osadolorebhuoma@gmail.com.
Abstract
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.
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.
Authors: Wei Kit Phang; Mohd Hafizi Abdul Hamid; Jenarun Jelip; Rose Nani Mudin; Ting-Wu Chuang; Yee Ling Lau; Mun Yik Fong Journal: Int J Environ Res Public Health Date: 2020-12-11 Impact factor: 3.390
Authors: Theodore Gondwe; Yongi Yang; Simeon Yosefe; Maisa Kasanga; Griffin Mulula; Mphatso Prince Luwemba; Annie Jere; Victor Daka; Tobela Mudenda Journal: Int J Environ Res Public Health Date: 2021-12-03 Impact factor: 3.390