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AI-Driven Agent-Based Models to Study the Role of Vaccine Acceptance in Controlling COVID-19 Spread in the USWe study the role of vaccine acceptance in controlling the spread of COVID-19 in the US using AI-driven agent-based models. Our study uses a 288 million node social contact network spanning all 50 US states plus Washington DC, comprised of 3300 counties, with 12.59 billion daily interactions. The highly-resolved agent-based models use realistic information about disease progression, vaccine uptake, production schedules, acceptance trends, prevalence, and social distancing guidelines. Developing a national model at this resolution that is driven by realistic data requires a complex scalable workflow, model calibration, simulation, and analytics components. Our workflow optimizes the total execution time andmore »Free, publicly-accessible full text available December 15, 2022
Today’s landscape of computational science is evolving rapidly, with a need for new, flexible, and responsive supercomputing platforms for addressing the growing areas of artificial intelligence (AI), data analytics (DA) and convergent collaborative research. To support this community, we designed and deployed the Bridges-2 platform. Building on our highly successful Bridges supercomputer, which was a high-performance computing resource supporting new communities and complex workflows, Bridges-2 supports traditional and nontraditional research communities and applications; integrates new technologies for converged, scalable high-performance computing (HPC), AI, and data analytics; prioritizes researcher productivity and ease of use; and provides an extensible architecture for interoperationmore »Free, publicly-accessible full text available July 17, 2022