A Working Model to Inform Risk-Based Back to Work Strategies

  1. Kristen Meier
  2. Kirsten J. Curnow
  3. Darcy Vavrek
  4. John Moon
  5. Kyle Farh
  6. Martin Chian
  7. Robert Ragusa
  8. Eileen de Feo
  9. Phillip G. Febbo

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Background

The coronavirus disease 2019 (COVID-19) pandemic has forced many businesses to close or move to remote work to reduce the potential spread of disease. Employers desiring a return to onsite work want to understand their risk for having an infected employee on site and how best to mitigate this risk. Here, we modelled a range of key metrics to help inform return to work policies and procedures, including evaluating the benefit and optimal design of a SARS-CoV-2 employee screening program.

Methods

We modeled a range of input variables including prevalence of COVID-19, time infected, number of employees, test sensitivity and specificity, test turnaround time, number of times tested within the infectious period, and sample pooling. We modeled the impact of these input variables on several output variables: number of healthy employees; number of infected employees; number of test positive and test negative employees; number of true positive, false positive, true negative, and false negative employees; positive and negative predictive values; and time an infected, potentially contagious employee is on site.

Results

We show that an employee screening program can reduce the risk for onsite transmission across different prevalence values and group sizes. For example, at a pre-test asymptomatic community prevalence of 0.5% (5 in 1000) with an employee group size of 500, the risk for at least one infected employee on site is 91.8%, with 3 asymptomatic infected employees predicted within those 500 employees. Implementing a SARS-CoV-2 baseline screen with an 80% sensitivity and 99.5% specificity would reduce the risk of at least one infected employee on site to 39.4% and the predicted number of infected employees onsite (false negatives) to 1. Repetitive testing is required for ongoing vigilance of onsite employees. The expected number of days an infected employee is on site depends on test sensitivity, testing interval, and turnaround time. If the test interval is longer than the infectious period (∼14 days for COVID-19), testing will not detect the infected employee. Sample pooling reduces the number of tests performed, thereby reducing testing costs. However, the pooling methodology (eg, 1-stage vs 2-stage pooling, pool size) will impact the number of employees that screen positive, thereby affected the number of employees eligible to return to onsite work.

Conclusions

The modeling presented here can be used to help employers understand their risk for having an infected employee on site. Further, it details how an employee screening program can reduce this risk and shows how screening performance and frequency impact the effectiveness of a screening program. The primary factors determining the effectiveness of a screening program are test sensitivity and frequency of testing.

Disclaimer

This publication is offered to businesses/employers as a model of potential risk arising from COVID19 in the workplace. While believed to be based on reliable data, the model described herein has not been prospectively validated and should not be relied upon for any purpose other than as an aid to understand the potential impacts of a number of variables on the risk of having COVID19 positive employees on a worksite. Decisions related to workplace safety; COVID19 related workplace testing; programs and procedures should be based upon your actual data and applicable laws and public health orders.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.12.18.20248512: (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

    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: We detected the following sentences addressing limitations in the study:
    Counties on the watch list have limitations on which indoor businesses can operate. The CDC has released interim guidance for businesses and employers responding to COVID-1939: Conducting daily health checks; conducting a hazard assessment of the workplace; encouraging employees to wear cloth face coverings in the workplace, if appropriate; implementing policies and practices for social distancing in the workplace; and improving the building ventilation system. The ability to implement social distancing and other precautionary measures may influence a given employer’s return to work policy, as well as the necessity for employees to be on site for business resumption. Some employers may be able to keep a proportion of their employees working remotely to reduce the required group size and the risk of workplace infection40. Finally, the desired degree of risk reduction or risk tolerance threshold will vary between employers. Employers for whom a single onsite infected employee may trigger a site shutdown and loss of business may desire to minimize risk to whatever degree is possible. The strength of this study is in the variety of variables modeled and the range of values modeled. The findings here are consistent with a recent paper that modeled COVID-19 screening strategies that would allow a safe re-opening of US college campuses15. Multiple studies have reported test frequency has more impact on reducing COVID-19 infections than test sensitivity, and that rapid and frequent s...

    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.

  2. Version published to 10.1101/2020.12.18.20248512v1 on medRxiv