PDFM, a geospatial embedding model, enhances health prediction models by integrating privacy-preserving data signals for improved disease surveillance and risk assessment. Proven effective in diverse settings, PDFM notably advances cholera warnings, postpartum depression predictions, and dengue forecasts, with commercial availability and free access for select research uses.