Scalable Epidemiological Workflows to Support COVID-19 Planning and Response

  1. Dustin Machi
  2. Parantapa Bhattacharya
  3. Stefan Hoops
  4. Jiangzhuo Chen
  5. Henning Mortveit
  6. Srinivasan Venkatramanan
  7. Bryan Lewis
  8. Mandy Wilson
  9. Arindam Fadikar
  10. Tom Maiden
  11. Christopher L. Barrett
  12. Madhav V. Marathe

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Abstract

The COVID-19 global outbreak represents the most significant epidemic event since the 1918 influenza pandemic. Simulations have played a crucial role in supporting COVID-19 planning and response efforts. Developing scalable workflows to provide policymakers quick responses to important questions pertaining to logistics, resource allocation, epidemic forecasts and intervention analysis remains a challenging computational problem. In this work, we present scalable high performance computing-enabled workflows for COVID-19 pandemic planning and response. The scalability of our methodology allows us to run fine-grained simulations daily, and to generate county-level forecasts and other counter-factual analysis for each of the 50 states (and DC), 3140 counties across the USA. Our workflows use a hybrid cloud/cluster system utilizing a combination of local and remote cluster computing facilities, and using over 20,000 CPU cores running for 6–9 hours every day to meet this objective. Our state (Virginia), state hospital network, our university, the DOD and the CDC use our models to guide their COVID-19 planning and response efforts. We began executing these pipelines March 25, 2020, and have delivered and briefed weekly updates to these stakeholders for over 30 weeks without interruption.

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    SciScore for 10.1101/2021.02.23.21252325: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The calibration task is carried out using the GPMSA framework [23] in Matlab.
    Matlab
    suggested: (MATLAB, RRID:SCR_001622)
    COMPUTING CLUSTER (B RIDGES HPC FACILITY AT P ITTSBURGH S
    CLUSTER
    suggested: (Cluster, RRID:SCR_013505)
    Journal of the American Statistical Association, 103(482):570– 583, 2008. [ 30] S. C. Kamerlin and P. M. Kasson.
    American Statistical Association
    suggested: None

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

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  2. Version published to 10.1101/2021.02.23.21252325v1 on medRxiv