Objectives
Nontyphoidal Salmonella (NTS) is a major cause of foodborne illness in Malaysia. Time-series modeling can characterize temporal patterns, although structural disruptions, nonlinear dynamics, and regional variability may affect model performance. This study aimed to characterize NTS seasonality in Malaysia, assess temporal stability, and explore model-based approaches for surveillance.
Methods
Monthly laboratory-confirmed human NTS cases from national laboratory surveillance databases for 2018–2024 were analyzed using time-series models, including autoregressive integrated moving-average (ARIMA) models with Fourier terms and an exploratory hybrid ARIMA–long short-term memory (LSTM) model. Interrupted time-series analysis was used to assess structural stability. Anomalies were identified using 95% prediction intervals, and stratified modeling assessed regional heterogeneity.
Results
NTS cases showed recurring annual seasonal patterns of moderate strength, with temporal variation over the study period. A significant decline occurred in March 2020 (β=−62.24 cases, p=0.001), followed by gradual recovery (β=2.71 cases/month, p<0.001), suggesting a marked coronavirus disease 2019 pandemic-related disruption and a subsequent positive post-intervention trend. The ARIMA(0,1,1)–Fourier (K=1) model outperformed conventional ARIMA, benchmark, and exponential smoothing models (root mean squared error, 42.9 vs. 51.37–108.79). The hybrid ARIMA–LSTM model provided modest additional improvement, particularly during high-case periods. Model-based anomaly detection identified deviations from expected patterns that coincided with documented outbreak-associated months. Regional heterogeneity in temporal dynamics suggested the need for surveillance strategies tailored to local epidemiological patterns.
Conclusion
Parsimonious models captured most temporal patterns in NTS cases, with only modest gains from more complex approaches. Context-specific modeling strategies, model-based anomaly detection, and tiered surveillance frameworks may support more proactive NTS surveillance in Malaysia.

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 on the 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 decomposition indicated 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 vector control. Tailoring surveillance systems and interventions to local temporal patterns