Enhancing public health surveillance: integrating genomic and epidemiologic data to inform public health action and One Health progress

Pathogen genomic data can provide highly useful information for public health practice, particularly when combined and analyzed with epidemiologic data in real time. Likewise, a One Health approach pushes our current health surveillance systems beyond their siloed views to consider balancing and optimizing health outcomes across human, animal, and environmental domains. Implementation of genomic epidemiology in public health practice alongside a One Health approach holds promise for early and more specific outbreak detection, improved understanding of health risks, increased hypothesis generation for research, and proactive public health action to prevent health threats.This dissertation focuses on genomic data integration and use within public health practice, highlighting the systems changes required for successful implementation, demonstrating population-level genomic-epidemiologic analyses for the purpose of public health action, and discussing expansion of these concepts to encompass a One Health approach. In the chapters that follow, I first describe implementation of a comprehensive system for large-scale genomic data capture and linkage to epidemiological data and an evaluation of this system. This study identifies key areas of success for this system as well as areas for improvement to enable real-time genomic-epidemiologic analyses. Next, I apply genomic-epidemiologic methods, demonstrating the utility of genomic data produced at the population-level to add information for public health action over the course of the SARS-CoV-2 pandemic. Given the available data, computing infrastructure, workforce, and tools, I outline which genomic-epidemiologic methods are most applicable for ongoing or routine data analysis given the system’s current state, as well as recommendations for improved data capture to support additional methods. Finally, I outline requirements for operationalizing One Health data integration through the development of a framework and possible approaches to One Health genomic data storage and co-analysis. This framework is developed to support data integration across One Health domains, expanding our joint ability to prevent and control disease. Together, this work envisions a more holistic approach to infectious disease surveillance, considering data generated from pathogens, hosts of all species, and the environment to better prepare our public health system to face emerging and endemic health threats.