Data-driven scalable pipeline using national agent-based models for real-time pandemic response and decision support
| Autor: | Parantapa Bhattacharya, Jiangzhuo Chen, Stefan Hoops, Dustin Machi, Bryan Lewis, Srinivasan Venkatramanan, Mandy L. Wilson, Brian Klahn, Aniruddha Adiga, Benjamin Hurt, Joseph Outten, Abhijin Adiga, Andrew Warren, Young Yun Baek, Przemyslaw Porebski, Achla Marathe, Dawen Xie, Samarth Swarup, Anil Vullikanti, Henning Mortveit, Stephen Eubank, Christopher L. Barrett, Madhav Marathe |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | The International Journal of High Performance Computing Applications. 37:4-27 |
| ISSN: | 1741-2846 1094-3420 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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