An updated ensemble forecasting model for district-level dengue prediction in the Mekong Delta Region of Vietnam: model refinement and evaluation
TL;DR
A revised dengue forecasting ensemble for Vietnam’s Mekong Delta Region improves outbreak detection sensitivity while maintaining high specificity and reduces forecast lag, supporting more timely and targeted dengue prevention and control interventions in the MDR.
Abstract
Background The original district-level dengue forecasting ensemble for Vietnam’s Mekong Delta Region (MDR) showed strong overall accuracy but had two key limitations: forecasted outbreak peaks lagged behind observed timing, and outbreaks were under-detected during periods of rapidly increasing incidence. Methods We evaluated 57 candidate models varying in spatial structure, seasonal representation, and covariates, including flexible harmonic seasonality and climate-driven covariates (mosquito thermal suitability, cumulative precipitation), assessed using the Continuous Ranked Probability Score and Brier score. The six best-performing models were combined into a revised ensemble using district- and horizon-specific Brier score weighting, trained on 2010–2015 data, cross-validated over 2016–2020, and evaluated over 2021–2025 for the on-season period (April–November), using the 95th percentile of historical district-month cases as the epidemic threshold. Results At the 3-month forecast horizon, the revised ensemble achieved 66.3% sensitivity and 89.1% specificity, compared with 43.4% sensitivity and 91.7% specificity for the original model, and overall accuracy improved from 84% to 87% (see Extended Data). The revised model also reduced the temporal lag in outbreak predictions observed previously. Conclusions The revised ensemble improves outbreak detection sensitivity while maintaining high specificity and reduces forecast lag, supporting more timely and targeted dengue prevention and control interventions in the MDR.