Foundation models capture human and environmental context to improve dengue forecasting

Accurate and timely probabilistic dengue forecasts can support public health preparedness, but producing reliable forecasts across locations and time horizons remains challenging. We evaluated five statistical, machine-learning, deep-learning, and foundation-model approaches for forecasting monthly reported dengue cases across 5,570 Brazilian municipalities at 1-, 3-, 6- and 12-month horizons, with forecasts backtested from January 2020 to August 2025. The time-series foundation model TimesFM maintained comparatively stable probabilistic forecast performance at longer horizons, while most othe

This content is for informational purposes only and is not medical advice. It is not intended to diagnose, treat, cure, or prevent any disease. Consult a healthcare professional before starting any supplement, treatment, or program: especially if you are pregnant, nursing, taking medication, or managing a health condition.