A scenario modeling pipeline for COVID-19 emergency planning

  1. Joseph C. Lemaitre
  2. Kyra H. Grantz
  3. Joshua Kaminsky
  4. Hannah R. Meredith
  5. Shaun A. Truelove
  6. Stephen A. Lauer
  7. Lindsay T. Keegan
  8. Sam Shah
  9. Josh Wills
  10. Kathryn Kaminsky
  11. Javier Perez-Saez
  12. Justin Lessler
  13. Elizabeth C. Lee

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Abstract

Coronavirus disease 2019 (COVID-19) has caused strain on health systems worldwide due to its high mortality rate and the large portion of cases requiring critical care and mechanical ventilation. During these uncertain times, public health decision makers, from city health departments to federal agencies, sought the use of epidemiological models for decision support in allocating resources, developing non-pharmaceutical interventions, and characterizing the dynamics of COVID-19 in their jurisdictions. In response, we developed a flexible scenario modeling pipeline that could quickly tailor models for decision makers seeking to compare projections of epidemic trajectories and healthcare impacts from multiple intervention scenarios in different locations. Here, we present the components and configurable features of the COVID Scenario Pipeline, with a vignette detailing its current use. We also present model limitations and active areas of development to meet ever-changing decision maker needs.

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  1. Version published to 10.1038/s41598-021-86811-0
  2. Version published to 10.1101/2020.06.11.20127894v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.06.11.20127894: (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 master branch of this repository consists of a Python package “SEIR” and two R packages “hospitalization” and “report.generation,” which correspond to the second, third, and fourth modules of the model pipeline (https://zenodo.org/badae/latestdoi/245866576y Air importation-based seeding is implemented in the covidImportation package (https://aithub.com/HopkinsIDD/covidlmportationy while seeding according to the earliest identified cases is performed in scripts within the “HopkinsIDD/COVIDScenarioPipeline” repository.
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    The current implementation of our model has several limitations. We do not explicitly model the role of asymptomatic transmission or other factors that may lead to biases in reporting, and we assume that only one progression in health outcome severity exists (infections to hospitalization to ICU to ventilator use), although we know that many disease course progressions are possible. In addition, the delays and durations involved in our health outcomes progression are fixed values, despite high variability in the estimates of these values. Our epidemic simulations do not account for age-specific transmission, so our model cannot capture the impact of strategies such as cocooning of high-risk age groups beyond population-level reductions in disease transmission. However, the modular approach taken is meant to allow for easy substitution of models with improvement in any of these areas while still taking advantage of other pipeline components. This flexibility does come at a cost, as the modular pipeline approach requires us to write and read files at the end and beginning of each phase, respectively. This procedure requires more disk space and input/output steps than other modeling approaches that can hold all of the necessary data in memory until a single output is produced at the end. Still, these slowdowns are not critically limiting; we have been able to run 1000 county-level simulations of the United States in less than 10 minutes on a 96 core server. These limitations poi...

    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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  4. Version published to 10.1101/2020.06.11.20127894v1 on medRxiv