Characterizing and Forecasting Emergency Department Visits Related to COVID-19 Using Chief Complaints and Discharge Diagnoses

  1. Phillip Koshute
  2. Rekha Holtry
  3. Richard Wojcik
  4. Wayne Loschen
  5. Sheri Lewis

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Abstract

In response to the unprecedented public health challenge posed by the SARS CoV-2 virus (COVID-19) in the United States, we and our colleagues at the Johns Hopkins University Applied Physics Laboratory (JHU/APL) have developed a model of COVID-19 progression using emergency department (ED) visit data from the National Capital Region (NCR). We obtained ED visits counts through targeted queries of the NCR Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE). To focus on ED visits by COVID-19 patients, we adjusted the query results for typical ED visit volumes and for reductions in ED volumes due to COVID-19 precautions. With these ED visit data, we fitted a logistic growth model to characterize and forecast the increase in cumulative COVID-19 ED visits. Our model achieves the best fit when we assume that the first NCR visit occurred in early January. We estimate that approximately 15,000 COVID-19 ED visits occurred prior to May 2020 and that approximately 17,000 more visits will occur in subsequent months. We plan to deploy an operational pilot of this model in the NCR ESSENCE environment, assisting local public health authorities as they brace for a second wave of COVID-19. Additionally, we will iteratively assess potential model refinements, aiming to maximize our model’s relevance for local public health authorities’ situational awareness and decision-making.

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  1. ScreenIT

    SciScore for 10.1101/2020.06.01.20116772: (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 10.1101/2020.06.01.20116772v1 on medRxiv