Project Summary: This application from the Great Plains IDeA Clinical and Translational Research (GP IDeA-CTR) network responds to NIGMS call (NOT-GM-22-026) for Urgent Competitive Revisions to IDeA Programs for SARS-CoV-2 Surveillance Studies. Our innovative approach addresses three specific aims (SA): SA1 Unites the UNMC SPHERES (CDC SARS-CoV-2 Sequencing for Public Health Emergency Response, Epidemiology, and Surveillance) team, Nebraska Medicine (NM) clinical enterprise, and the Nebraska Public Health Laboratory (NPHL), to develop, standardize, validate, and deploy the infrastructure and communication protocols for integrating real-time viral variant data within CRANE (Clinical Research Analytics Environment). CRANE is a research data warehouse extracted from the NM EHR system, comprising over 1.4 million individuals, including 29,000 SARS-CoV-2 infected persons and 2,000 with variant data available for immediate linkage. SA2 Creates a Consortium to Organize Research in Nebraska for Covid 19 (CORN-19), spanning public health experts, clinician-scientists, and informaticists, strengthening the discovery pipeline from bench to bedside to policy. SA3 Profile the SARS-CoV-2 variant landscape in Nebraska by geographic region, risk profiles, and social determinants of health. This project deploys, leverages, integrates, and benefits from several powerful UNMC capabilities. UNMC hosts one of the most productive US infectious disease units, closely linked with state and national surveillance networks; is a site in the CDC’s SPHERES program; holds the largest US biocontainment unit; provides SARS-CoV-2 whole-genome sequencing support and variant tracking across NE with strong ties to the NPHL; and maintains the CRANE data warehouse. We deploy these powerful capabilities to test hypothetical links between COVID-19 illness (acute and chronic), post-vaccination infection, and community susceptibly with specific SARS-CoV-2 mutations and lineages, in line with five NOT-GM-21-031 priorities areas: 1) Variants in the population and how cases caused by different variants change over time? 2) Are cases of infection by different variants associated with outbreak events, geographic locations, or specific times? 3) How are different variants distributed among different racial, ethnical, gender, or age groups? 4) Are specific variants associated with different COVID-19 symptoms? 5) Are specific variants associated with more frequent vaccine breakthroughs. Real-time health surveillance data linked to EHR data expands the possibility of a learning healthcare system in which actionable evidence improves health care and saves lives across our communities, particularly in medically underserved and rural areas. As a result of this integrative silo- spanning project, we expect to provide a robust, scalable, egalitarian framework for community-engaged CTR – a national model for addressing urgent biomedical research needs that outlasts the current pandemic and promises a s...