A seasonal autoregressive integrated moving average model to predict incidence of dengue cases in Kuantan, Malaysia - Abdul Hadi, Zulkifli; Che Dom, Nazri; Ismail, Rodziah (Science, Engineering and Health Studies, 2021)
A time series analysis model. Could provide useful information to facilitate the planning of public health interventions to minimize the frequency of dengue fever (DF) outbreaks. The objectives of this study were to analyze the trend of monthly DF cases from Kuantan, Malaysia during 2011-2019 to develop a seasonal autoregressive integrated moving average (SARIMA) model, test the accuracy of model parameters by forecasting monthly cases of DF in 2018 and comparing it with actual monthly cases of DF in 2018, and construct a SARIMA model, by adopting the Box-Jenkins method, to forecast the monthly DF cases in 2019. Monthly-confirmed DF cases from 2011 to 2018 fit the model while the prediction was validated using epidemiological data from January 2018 to December 2018. The study concluded that the SARIMA (0,1,0) (3,0,2)12 model was the best-fit and could be used to extrapolate case numbers up to 12 months in advance. Our predicted number of monthly DF cases in 2019 was relatively close to the actual number of monthly DF cases and fell within the confidence interval. Therefore, the SARIMA model developed by this study is capable of accurately forecasting and predicting future DF cases. This can help improve existing intervention programs, which are an integral component of minimizing the burden of the disease in Kuantan.