SC21 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

Data-Driven Scalable Pipeline Using National Agent-Based Models for Real-Time Pandemic Response and Decision Support

Authors: Parantapa Bhattacharya, Jiangzhuo Chen, Stefan Hoops, Dustin Machi, Madhav Marathe, and Team University of Virginia (University of Virginia)

Abstract: We describe an integrated, data-driven operational pipeline based on national agent-based models to support federal- and state-level pandemic planning and response. The pipeline consists of (i) an automatic semantic-aware scheduling method that coordinates jobs across two separate high performance computing systems; (ii) a data pipeline to collect, integrate and organize national- and county-level disaggregated data for initialization and post-simulation analysis; (iii) a digital twin of national social contact networks made up of 288 Million individuals and 12.6 Billion time-varying interactions covering the US states and DC; (iv) an extension of a parallel agent-based simulation model to study epidemic dynamics and associated interventions. Our pipeline can run 400 replicates of national runs in less than 33 hours, and reduces the need for human intervention, resulting in faster turnaround times and higher reliability and accuracy of the results. Scientifically, the work has led to significant advances in real-time epidemic sciences.

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