Time-series decomposition and modeling of dengue cases in Malaysia, 2022–2024: a nationwide observational study
10.24171/j.phrp.2025.0397
- Author:
Mohamad Afiq Amsyar HAMEDIN
1
;
Kamarul Imran MUSA
;
Mohd Rahim SULONG
Author Information
1. Department of Community Medicine, School of Medical Science, Universiti Sains Malaysia, Kubang Kerian, Malaysia
- Publication Type:Original Article
- From:
Osong Public Health and Research Perspectives
2026;17(1):50-60
- CountryRepublic of Korea
- Language:English
-
Abstract:
Objectives:This study aimed to examine the temporal dynamics of dengue cases in Malaysia from 2022 to 2024 using seasonal-trend decomposition and time-series modeling.
Methods:Weekly dengue case counts from the national registry were analyzed across all states using seasonal-trend decomposition using LOESS (STL) to separate trend, seasonal, and irregular components. Autoregressive integrated moving average (ARIMA) and seasonal ARIMA(SARIMA) models were fitted to validate temporal structures, with model selection based onthe Akaike information criterion (AIC), corrected AIC, and Bayesian information criterion.Diagnostic checks, including residual analysis and Ljung-Box testing, were performed to ensure model adequacy.
Results:Dengue incidence showed marked heterogeneity across states. STL decompositionindicated that long-term trends contributed more strongly to case dynamics than seasonality in most states, although seasonal influences were significant in the states of Kedah and Kelantan. Seasonal peak timing varied between states, highlighting differences in epidemic cycles. ARIMA and SARIMA modeling confirmed that no single temporal structure could adequately represent all states; while some series were well fitted by simple ARIMA models,others required seasonal adjustments. Residual diagnostics demonstrated that the selected models were statistically adequate.
Conclusion:Dengue dynamics in Malaysia are shaped by both trend and seasonal components, with considerable variation across states. Combining STL decomposition with ARIMA/SARIMA modeling strengthens the evidence base for state-specific forecasting and proactive vectorcontrol. Tailoring surveillance systems and interventions to local temporal patterns may improve early warning capacity and optimize resource allocation for dengue prevention.
