Generative embedding of sparse data with a tabular foundation model for dengue anticipatory action: a machine learning approach.

Early outbreak detection has largely relied on complex, data-intensive models with limited applicability to low-resource surveillance. Even state-of-the-art tabular foundation models require dense datasets for fine-tuning to capture disease transmission dynamics. We address this by building a domain-mechanistic generative embedding from cases and rainfall to detect early epidemic onset. We build a generative, domain-mechanistic embedding from sparse case and rainfall data into 132 features, converting limited inputs into a structured representation of transmission for outbreak-onset detection. A tabular foundation model was evaluated by leave-one-year-out validation with cluster-bootstrap intervals across 17 Philippine regions and eight dengue-endemic countries, benchmarked against raw data columns and catch22. Raw columns used as input to the tabular foundation model were weakly predictive of dengue outbreak onset (AUROC 0·56-0·70). The generative embedding improved detection to 0·77 across countries and 0·89 across regions (+0·205 and +0·183; paired cluster-bootstrap p≤0·006). Calibration error was lower at the regional scale than at the country scale (expected calibration error 0·067 and 0·149). Strongly seasonal regions and countries were the most predictable (Philippine Type I region mean 0·87; Mexico 0·94, Brazil 0·93, the Philippines 0·91), whereas countries with year-round or coastally opposing rainfall were weaker or below chance (Singapore 0·69, Sri Lanka 0·42), and countries left with only one or two seasons after applying the onset rule gave unreliable estimates. Under sparse surveillance conditions, predictive capacity depended strongly on the representation supplied to the tabular foundation model. The generative embedding translates climate and epidemiological variables into actionable early-warning signals by capturing underlying transmission mechanisms, whose accuracy scales with local seasonal dynamics. This approach provides a viable pathway for extending prospective outbreak surveillance in data-limited settings, and indicates that mechanism-grounded embeddings could calibrate transmission-acceleration models at aggregated scales to improve their predictions. National Institute of Environmental Health Sciences, National Institutes of Health (award P20ES036118).