Resilient Shield: A Network for Outbreak Data Integration and Modeling to Support Rapid Public Health Action - This application is for Mandatory Component 1 and Optional Component 3. Purpose: Our overarching goal is to integrate the best forecasting and analytic approaches with the best data so that public health officials can make the right decisions and assure the best outcomes to prevent, predict, preempt, prepare for, and mitigate infectious disease threats. This project brings together researchers at four campuses of the University of California and the County of San Diego - Health and Human Services Agency (CoSD-HHS) as an implementation partner to adapt and integrate a proven collaborative modeling and public health action framework developed in response to the COVID-19 pandemic that was innovated with UC San Diego, the County of San Diego, California Department of Public Health, and industry, NGO and academic partners. The proposed project will create an enhanced “reference design” of existing work, called Resilient Shield, that combines an agile data and modeling center with adaptable methods and resources for implementing and assessing solutions with partners in the County of San Diego. Resilient Shield will systematically connect and test the utility of data from novel interventions like wastewater and air surveillance, molecular epidemiology, and smartphone-based exposure notification, to help model and predict future events. These interventions will be monitored, their data leveraged, and employed as “always-on” interoperable data sources for public health authorities to employ with advanced models to inform policy and response. To address shortcomings of current models built on sparse knowledge of human behavior and the dynamics of complex sociotechnical systems, we will use innovative methods to estimate latent variables and explore future and counterfactual scenarios through serious games, drills, and tabletop exercises uniquely backed by knowledge graphs, dynamical systems models, agent-based models, artificial intelligence, and other advanced modeling techniques to predict future behaviors (individual and system), parameterize and calibrate models, optimize public health interventions, and provide opportunities for near-real-life training to promote workforce development and readiness. We will monitor and evaluate project performance, processes, and outcomes using ongoing data collection for pre-defined indicators and special quantitive and qualitative studies and engage a wide range of stakeholders in the entire project life cycle. As a critical element, Resilient Shield will formally analyze data sources for predictive power and utility. We will then match these with similarly assessed models and systematically deploy them in an adaptive testing framework in real-world settings. Pilot testing with the CoSD-HHS will include the ongoing Hepatitis A outbreak in persons experiencing homelessness, HIV outbreak clusters (e.g., in people who use drugs), as well as retrospective analyses in support of the CoSD-HHS efforts to respond to respiratory pathogens like SARS-COV2, influenza, and RSV, particularly in vulnerable populations. Outcomes: We will connect, learn from, and support our integration partner and a broad national and cross-border coalition of innovators, public health authorities, and local partners to design, prototype, integrate, field test, assess utility and fitness, and disseminate a ground-breaking reference design of modular interlocking methods, technologies, and interventions for improving analytics, modeling, and forecasting of public health threats in U.S. health jurisdictions, including county, tribal, cross-border, state, and national programs. Our efforts will support decision-makers between and during outbreaks in the United States through a new paradigm of academic, public health, health care and industry partnership converging experience, technology, and know-how to assure ethical design and focus on health disparities, supporting collective intelligence, science, and national resilience.