A model to forecast regional demand for COVID-19 related hospital beds

  1. Johannes O. Ferstad
  2. Angela Gu
  3. Raymond Y. Lee
  4. Isha Thapa
  5. Andrew Y. Shin
  6. Joshua A. Salomon
  7. Peter Glynn
  8. Nigam H. Shah
  9. Arnold Milstein
  10. Kevin Schulman
  11. David Scheinker

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Abstract

COVID-19 threatens to overwhelm hospital facilities throughout the United States. We created an interactive, quantitative model that forecasts demand for COVID-19 related hospitalization based on county-level population characteristics, data from the literature on COVID-19, and data from online repositories. Using this information as well as user inputs, the model estimates a time series of demand for intensive care beds and acute care beds as well as the availability of those beds. The online model is designed to be intuitive and interactive so that local leaders with limited technical or epidemiological expertise may make decisions based on a variety of scenarios. This complements high-level models designed for public consumption and technically sophisticated models designed for use by epidemiologists. The model is actively being used by several academic medical centers and policy makers, and we believe that broader access will continue to aid community and hospital leaders in their response to COVID-19.

LINK TO ONLINE MODEL

https://surf.stanford.edu/covid-19-tools/covid-19/

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  1. ScreenIT

    SciScore for 10.1101/2020.03.26.20044842: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    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.

    About SciScore

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  2. Version published to 10.1101/2020.03.26.20044842v3 on medRxiv
  3. Version published to 10.1101/2020.03.26.20044842v2 on medRxiv
  4. Version published to 10.1101/2020.03.26.20044842v1 on medRxiv