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Developing a time series prediction modelling for dengue in Kota Kinabalu, Sabah
N Md Taib1, A Atil1, R Dapari2
1Universiti Malaysia Sabah, Faculty of Medicine and Health Sciences, Department of Public Health Medicine, Sabah, Malaysia.
Introduction:
Dengue is a major public health issue, with 3,900,000 people living in 129 dengue-endemic countries globally facing a risk of contracting dengue fever. Dengue incidence in Sabah is among the highest in Malaysia. In 2022, Kota Kinabalu District reported 22% of the total number of dengue cases in Sabah. The objective of this study was to develop a prediction model for dengue incidence using meteorological, entomological, and environmental parameters in Kota Kinabalu, Sabah.
Materials And Methods:
An ecological study was conducted from 2016 to 2021 using the dengue database and meteorological data. The forecasting model for dengue incidence was performed with R software using the seasonal autoregressive integrated moving average (SARIMA) model. The model was fitted based on the reported weekly incidence of dengue from 2016 to 2020 and validated using data collected between January and December 2021.
Results:
SARIMA (1,1,1) (1,1,0)52 with the external regressor maximal temperature, Aedes index, and vacant lot were the models with minimal measurement errors, as indicated by the Mean Absolute Error (MAE) values of 3.04, Root Mean Squared Error (RMSE) of 4.43, and Akaike Information Criterion (AIC) of 1354.82.
Conclusions:
The predicted values in 2021 accurately forecasted the capability to serve as an early warning system for proactive dengue measures. This information is deemed valuable to healthcare administrators for enhancing the level of preparedness.
