Graduate

Data-driven modeling frameworks have become essential tools for guiding surveillance strategies and informing public health policies across diverse population health challenges. Accurate, fine-scale disease estimates often lacking from direct surveys are critical for policy planning, given that spatial heterogeneity and nonlinear dynamics among determinants of health challenge classical models, limiting their utility for targeted public health interventions.