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
Objectives Scrub typhus, caused by Orientia tsutsugamushi, is a climate-sensitive vector-borne disease with high incidence in the Republic of Korea. This study examined long-term epidemiological trends and changing meteorological influences in the context of climate change.
Methods A retrospective time-series study was conducted using national surveillance data on 149,289 scrub typhus cases (2001–2024) across 4 surveillance phases. Temporal trends in age-standardized incidence rates were evaluated using Joinpoint regression. Associations between monthly meteorological variables and incidence were assessed with Spearman correlation analysis and time-series regression analysis using distributed lag non-linear models.
Results The national incidence increased until 2017 and has decreased since 2018, whereas the AAPC rebound to 4.32% during phase IV (2019–2024). The proportion of female cases decreased, while that of adults ≥70 years increased significantly. In phase IV, the average annual percent change increased in central and urban regions. The lag effect of meteorological factors lengthened from 4 to 6 months, with mean temperature (Tmean) and relative humidity (RH) representing the primary predictors. Phase III (2013–2018) displayed the highest cumulative relative risk (RR) for Tmean at 25.2 °C (RR, 5.86; 95% confidence interval [CI], 2.56–13.42), whereas in phase IV, only moderate RH (58%) remained significantly associated with incidence (RR, 1.68; 95% CI, 1.29–2.20).
Conclusion Over the past 2 decades, the influence of meteorological factors on scrub typhus has shifted, with recent years marked by greater uncertainty under increasing climate variability and instability. For timely risk prediction and targeted prevention, adaptive surveillance systems that integrate dynamic climate indicators—capturing the intensity, frequency, and variability of extreme weather events—are needed.
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Objectives
From the introduction of HIV into the Republic of Korea in 1985 through 2012, 9,410 HIV-infected Koreans have been identified. Since 2000, there has been a sharp increase in newly diagnosed HIV-infected Koreans. It is necessary to estimate the changes in HIV infection to plan budgets and to modify HIV/AIDS prevention policy. We constructed autoregressive integrated moving average (ARIMA) models to forecast the number of HIV infections from 2013 to 2017. Methods
HIV infection data from 1985 to 2012 were used to fit ARIMA models. Akaike Information Criterion and Schwartz Bayesian Criterion statistics were used to evaluate the constructed models. Estimation was via the maximum likelihood method. To assess the validity of the proposed models, the mean absolute percentage error (MAPE) between the number of observed and fitted HIV infections from 1985 to 2012 was calculated. Finally, the fitted ARIMA models were used to forecast the number of HIV infections from 2013 to 2017. Results
The fitted number of HIV infections was calculated by optimum ARIMA (2,2,1) model from 1985–2012. The fitted number was similar to the observed number of HIV infections, with a MAPE of 13.7%. The forecasted number of new HIV infections in 2013 was 962 (95% confidence interval (CI): 889–1,036) and in 2017 was 1,111 (95% CI: 805–1,418). The forecasted cumulative number of HIV infections in 2013 was 10,372 (95% CI: 10,308–10,437) and in 2017 was14,724 (95% CI: 13,893–15,555) by ARIMA (1,2,3). Conclusion
Based on the forecast of the number of newly diagnosed HIV infections and the current cumulative number of HIV infections, the cumulative number of HIV-infected Koreans in 2017 would reach about 15,000.
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