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Bhattacharya, Parantapa; Machi, Dustin; Chen, Jiangzhuo; Hoops, Stefan; Lewis, Bryan; Mortveit, Henning; Venkatramanan, Srinivasan; Wilson, Mandy L.; Marathe, Achla; Porebski, Przemyslaw; et al(
, IEEE International Conference on Big Data)
We 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 »helps in improving overall human productivity.This work develops a pipeline that can execute US-scale models and associated workflows that typically present significant big data challenges. Our results show that, when compared to faster and accelerating vaccinations, slower vaccination rates due to vaccine hesitancy cause averted infections to drop from 6.7M to 4.5M, and averted total deaths to drop from 39.4K to 28.2K nationwide. This occurs despite the fact that the final vaccine coverage is the same in both scenarios. Improving vaccine acceptance by 10% in all states increases averted infections from 4.5M to 4.7M (a 4.4% improvement) and total deaths from 28.2K to 29.9K (a 6% increase) nationwide. The analysis also reveals interesting spatio-temporal differences in COVID-19 dynamics as a result of vaccine acceptance. To our knowledge, this is the first national-scale analysis of the effect of vaccine acceptance on the spread of COVID-19, using detailed and realistic agent-based models.« less
Free, publicly-accessible full text available December 15, 2022
Brown, Shawn T.; Buitrago, Paola; Hanna, Edward; Sanielevici, Sergiu; Scibek, Robin; Nystrom, Nicholas A.(
, Practice and Experience in Advanced Research Computing (PEARC '21))
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 »with complementary data intensive projects, campuses, and clouds. In this report, we describe Bridges-2’s hardware and configuration, user environments, and systems support and present the results of the successful Early User Program.« less
Free, publicly-accessible full text available July 17, 2022
