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Data-driven scalable pipeline using national agent-based models for real-time pandemic response and decision support.
Bhattacharya, Parantapa; Chen, Jiangzhuo; Hoops, Stefan; Machi, Dustin; Lewis, Bryan; Venkatramanan, Srinivasan; Wilson, Mandy L; Klahn, Brian; Adiga, Aniruddha; Hurt, Benjamin; Outten, Joseph; Adiga, Abhijin; Warren, Andrew; Baek, Young Yun; Porebski, Przemyslaw; Marathe, Achla; Xie, Dawen; Swarup, Samarth; Vullikanti, Anil; Mortveit, Henning; Eubank, Stephen; Barrett, Christopher L; Marathe, Madhav.
Afiliación
  • Bhattacharya P; Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA.
  • Chen J; Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA.
  • Hoops S; Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA.
  • Machi D; Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA.
  • Lewis B; Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA.
  • Venkatramanan S; Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA.
  • Wilson ML; Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA.
  • Klahn B; Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA.
  • Adiga A; Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA.
  • Hurt B; Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA.
  • Outten J; Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA.
  • Adiga A; Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA.
  • Warren A; Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA.
  • Baek YY; Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA.
  • Porebski P; Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA.
  • Marathe A; Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA.
  • Xie D; Dept. of Public Health Sciences, University of Virginia, Charlottesville, VA, USA.
  • Swarup S; Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA.
  • Vullikanti A; Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA.
  • Mortveit H; Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA.
  • Eubank S; Dept. of Computer Science, University of Virginia, Charlottesville, VA, USA.
  • Barrett CL; Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA.
  • Marathe M; Dept. of Eng. Systems and Environment, University of Virginia, Charlottesville, VA, USA.
Int J High Perform Comput Appl ; 37(1): 4-27, 2023 Jan.
Article en En | MEDLINE | ID: mdl-38603425
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Int J High Perform Comput Appl Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Int J High Perform Comput Appl Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos