Cross-national prediction of imported dengue risk in Northeast Asia using climate, hydrological, and viral epidemiological indicators

BACKGROUND: Imported dengue remains a public health concern in non-endemic countries. Local transmission may occur when imported infections coincide with competent vectors and suitable environmental conditions. We developed a cross-national framework for predicting monthly imported dengue risk in Japan and the Republic of Korea. METHODS: We analyzed monthly data from January 2005 to June 2025. The period from 2020 to 2022 was excluded because of coronavirus disease 2019-related structural changes. The models were fitted to the log-transformed travel-volume-adjusted imported dengue case risk, and predictions were back-transformed to monthly case counts using outbound travel volume. Candidate predictors included Pacific Ocean sea surface temperature indices, the Dipole Mode Index, monsoon indices, source-region Standardized Precipitation-Evapotranspiration Index values, and dengue serotype dominance persistence. Candidate lags were selected from prespecified predictor-specific lag windows using the Akaike Information Criterion. Elastic Net regression, Random Forest, Support Vector Machine, and eXtreme Gradient Boosting were evaluated across 72 predictor sets for each country. A trend-and-seasonality-only reference model was used to evaluate the additional predictive information provided by the candidate predictors. Selected base-model predictions were combined in stacked ensemble models using Elastic Net regression and Random Forest as meta-learners. RESULTS: The selected Elastic Net-based stacked ensemble outperformed the reference model in both countries. In Japan, root mean squared error decreased from 23.875 to 7.220, while mean directional accuracy increased from 48.3% to 65.5%. Similar improvements were observed in the Republic of Korea, with root mean squared error decreasing from 28.812 to 6.313 and mean directional accuracy increasing from 44.8% to 65.5%. Within the fitted base models, the 6-month-lagged Oceanic Niño Index in Japan and the 5-month-lagged Niño 3 Index in the Republic of Korea showed relatively high predictive relevance. Thailand Standardized Precipitation-Evapotranspiration Index also ranked highly within several selected base models. An interactive R Shiny application was developed for scenario-based prediction. CONCLUSIONS: A framework combining climate, hydrological, and viral epidemiological indicators performed better for predicting imported dengue than a model based on temporal trends and seasonality alone. These findings support a multidimensional approach to preparedness for imported dengue in non-endemic Northeast Asia.