Rainfall, Mosquito Indices, and Dengue Outbreaks in Southern Taiwan: Reassessing Predictive Modeling with Machine Learning Approaches

Dengue remains a major public health challenge in southern Taiwan, where recurrent outbreaks are shaped by complex environmental and entomological drivers. Using nine years of surveillance data (2015–2024) comprising more than 70,000 dengue cases, daily rainfall, and six mosquito indices from Tainan, Kaohsiung, and Pingtung, we evaluated the predictive value of these indicators through lagged correlation and machine learning models. Rainfall showed little explanatory power, while entomological indices demonstrated only moderate, location-specific associations, with the Container Index in Kaohsiung peaking at r ≈ 0.38 at 4–6 week lags. Regression models failed to predict outbreak magnitude, with R ² near zero on test data, but classification frameworks distinguishing outbreak from non-outbreak weeks achieved high accuracy (AUC up to 0.98), driven primarily by mosquito indices. These findings highlight the limited utility of regression forecasts for dengue, while supporting classification-based approaches as more practical tools for early outbreak detection and vector control planning.