Data-driven scalable pipeline using national agent-based models for real-time pandemic response and decision support
Abstract
This paper describes 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. This pipeline can run 400 replicates of national runs in less than 33 h, 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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 20, 2022
- Source ID
- 10.1177/10943420221127034
Entities
People
- Abhijin Adiga
- Achla Marathe
- Andrew Warren
- Anil Vullikanti
- Aniruddha Adiga
- Benjamin Hurt
- Brian Klahn
- Bryan Lewis
- Christopher L. Barrett
- Dawen Xie
- Dustin Machi
- Henning S Mortveit
- Jiangzhuo Chen
- Joseph Outten
- Madhav Marathe
- Mandy L. Wilson
- Parantapa Bhattacharya
- Przemyslaw J Porebski
- Samarth Swarup
- Srinivasan Venkatramanan
- Stefan Hoops
- Stephen Eubank
- Young Yun Baek
Organizations
- Centers for Disease Control and Prevention
- Defense Threat Reduction Agency
- National Institutes of Health
- National Science Foundation
- University of Virginia
- Virginia Department of Health