Bridging Public Health with Clinical Decisions from a Data Centric Perspective

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 vs. treating more people. Meanwhile, sub-optimal clinical decisions also cause downstream effects on communities, like discharging patients too early might increase disease spread. My work brings a data-centric perspective to bridge clinical decisions within the context of infectious diseases for public health. My work addresses multiple challenges arising from effectively utilizing rich clinical datasets, and issues stemming from the complexity of disease spread dynamics in healthcare facilities. This talk will cover methods I have developed to address these challenges with better-designed models to optimize disease surveillance and control policy, and new techniques for end-to-end learning with mechanistic epidemiological models. I will conclude by discussing new challenges and opportunities in infectious diseases and pandemic response for computer scientists, epidemiologists, and computational biologists.