Reopening businesses and risk of COVID-19 transmission

  1. Ashley O’Donoghue
  2. Tenzin Dechen
  3. Whitney Pavlova
  4. Michael Boals
  5. Garba Moussa
  6. Manvi Madan
  7. Aalok Thakkar
  8. Frank J. DeFalco
  9. Jennifer P. Stevens

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Abstract

The true risk of a COVID-19 resurgence as states reopen businesses is unknown. In this paper, we used anonymized cell-phone data to quantify the potential risk of COVID-19 transmission in business establishments by building a Business Risk Index that measures transmission risk over time. The index was built using two metrics, visits per square foot and the average duration of visits, to account for both density of visits and length of time visitors linger in the business. We analyzed trends in traffic patterns to 1,272,260 businesses across eight states from January 2020 to June 2020. We found that potentially risky traffic behaviors at businesses decreased by 30% by April. Since the end of April, the risk index has been increasing as states reopen. There are some notable differences in trends across states and industries. Finally, we showed that the time series of the average Business Risk Index is useful for forecasting future COVID-19 cases at the county-level ( P  < 0.001). We found that an increase in a county’s average Business Risk Index is associated with an increase in positive COVID-19 cases in 1 week (IRR: 1.16, 95% CI: (1.1–1.26)). Our risk index provides a way for policymakers and hospital decision-makers to monitor the potential risk of COVID-19 transmission from businesses based on the frequency and density of visits to businesses. This can serve as an important metric as states monitor and evaluate their reopening strategies.

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  1. Version published to 10.1038/s41746-021-00420-9
  2. Version published to 10.1101/2020.05.24.20112110v4 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.05.24.20112110: (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

    Software and Algorithms
    SentencesResources
    STATISTICAL ANALYSIS: Statistical analysis was performed using Stata SE version 14.2 (StataCorp) and SAS (v. 9.4, SAS Institute Inc.
    StataCorp
    suggested: (Stata, RRID:SCR_012763)
    SAS Institute
    suggested: (Statistical Analysis System, RRID:SCR_008567)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    LIMITATIONS: There are several limitations to this study. Because Safegraph’s location data uses GPS location data, there is room for error. Businesses that occupy one floor of a multi-level building may have artificially high visits per square feet because GPS cannot differentiate between floors. Locations that are very small and closely located next to other businesses may erroneously have spillovers of their visits to nearby stores or visits from other nearby stores attributed to them, if the GPS does not pinpoint them precisely. SafeGraph’s data accounts for individuals who have a cell phone with location services. Finally, while traffic moves towards pre-pandemic levels as states reopen, states may experience different effects of reopening based upon other practices such as mask adherence and fiberglass barriers at businesses that did not exist pre-pandemic. FUTURE WORK: We are building an online decision-support tool that will allow policymakers and hospital decision makers to visualize potential high risk businesses in their area and monitor weekly risk to these businesses. We are currently testing a prototype of our tool at a health system in New York to monitor business traffic (e-figures 1-4) and our index is being integrated into a forecasting model for a large health system in Massachusetts to help predict a potential second wave. Our tool is helping policymakers and hospital decision makers monitor the risks in their community as they reopen. Finally, as states r...

    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

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  4. Version published to 10.1101/2020.05.24.20112110v3 on medRxiv
  5. Version published to 10.1101/2020.05.24.20112110v2 on medRxiv
  6. Version published to 10.1101/2020.05.24.20112110v1 on medRxiv