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Public health and clinical decisions are intertwined. Public health crises place a high burden on healthcare facilities, forcing them to make decisions such as maintaining quality verses treating more people. Meanwhile, sub-optimal clinical decisions also cause downstream effects on communities. For ex- ample, discharging patients too early may increase disease spread. Motivated by this, we bring a data-centric perspective to bridge clinical decisions within the context of infectious diseases for public health. This work addresses multiple challenges arising from effectively utilizing rich clinical datasets and issues stemming from the complexity of disease spread dynamics in healthcare facilities. We will cover methods developed to address these challenges with better designed models to optimize disease surveillance and control policies and new techniques for end-to-end learning with mechanistic models. We will conclude by discussing emerging challenges and opportunities at the intersection of machine learning, scientific modeling, and clinical decision-making for computer scientists, epidemiologists, and computational biologists.