Evaluating Time-Series Models for Dengue Haemorrhagic Fever Prediction: Arima vs. Sarima and Exponential Smoothing
TL;DR
This study assesses the performance of three time-series forecasting models—Exponential Smoothing, Autoregressive Integrated Moving Average (ARIMA), and Seasonal ARIMA (SARIMA)—in predicting annual DHF cases in Malaysia from 2011 to 2021 and concludes that ARIMA was preferred due to its simplicity and comparable predictive power.
Abstract
Accurately forecasting the transmission of infectious diseases, such as Dengue Haemorrhagic Fever (DHF), is essential for robust public health planning. This study assesses the performance of three time-series forecasting models—Exponential Smoothing, Autoregressive Integrated Moving Average (ARIMA), and Seasonal ARIMA (SARIMA)—in predicting annual DHF cases in Malaysia from 2011 to 2021. Model accuracy was evaluated using the Mean Absolute Percentage Error (MAPE). Results indicate that ARIMA and SARIMA yielded the lowest error rates (73.34%), whereas Exponential Smoothing performed less effectively (76.79%). ARIMA was preferred due to its simplicity and comparable predictive power. Although these error rates exceed the standard 10–20% threshold for reliable forecasting, the study demonstrates that these models have significant potential when integrated with more granular data and external covariates. Furthermore, a three-segment piecewise nonlinear regression was employed to identify structural changes in DHF trends. This combined analytical approach provides deeper insights into disease progression and strengthens the evidence base for effective public health interventions.