Background: Dengue fever remains a critical public health challenge in Thailand, with transmission dynamics driven by complex interactions between environmental and socioeconomic factors. Understanding these drivers is essential for developing robust prediction systems. Methods: We developed a machine learning framework to classify spatiotemporal dengue risk and identify key drivers of transmission across Thailand. We analyzed 20 years of monthly dengue hemorrhagic fever surveillance data (2003-2022) from 77 provinces, integrating 54 environmental, climatic, and socioeconomic variables. Using eXtreme Gradient Boosting (XGBoost) with SHapley Additive exPlanations (SHAP), we classified provinces as high-risk or low-risk based on the national median incidence. The dataset was stratified into training (2003-2016) and testing periods, with the latter subdivided into pre-COVID-19 (2017-2019), COVID-19 (2020-2021), and post-COVID-19 (2022) phases. Results: The model achieved robust performance with an area under the curve (AUC) of 0.94 during training and 0.80 in pre-pandemic testing. Temperature emerged as the dominant predictor, with temperature-related variables comprising seven of the ten most influential features. Critical transmission thresholds were identified at approximately 21 °C for a 1-month lagged minimum temperature and approximately 32 °C for a 3-month lagged maximum temperature. Interestingly, precipitation contributed minimally to model predictions, while a higher Gross Provincial Product was associated with an increased risk of dengue, reflecting urban transmission patterns. Model performance deteriorated significantly during the COVID-19 pandemic (AUC = 0.62 in 2021), with systematic overprediction suggesting that behavioral factors outweighed environmental drivers during the pandemic disruption. Conclusions: Temperature, particularly with lags of 1-3 months, is the primary predictor of dengue risk in Thailand. The pandemic-induced disruption of model accuracy underscores the crucial role of human behavioral factors in influencing dengue transmission dynamics. Our results challenge traditional precipitation-focused models and highlight the importance of temperature-driven approaches for dengue prediction in Thailand.

The authors have declared no competing interest.

This research has received funding support from The Rockefeller Foundation under the Strengthening the Early Warning and Outbreak Detection Systems through Nation-wide Event-based and Syndromic Surveillance (STONES) project (Grant No. 2021PPI005). Pikkanet Suttirat acknowledges support from the Development and Promotion of Science and Technology Talents Project (DPST) of Thailand.

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This study received an exemption from ethical review by the Ethics Committee of the Faculty of Tropical Medicine, Mahidol University, Thailand (Documentary Proof of Exemption: UTMEXMPT 2024-005, dated 8 July 2024).

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