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

Abstract

BackgroundThe 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.MethodsWe 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.ResultsWe 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.ConclusionsThe 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.DisclaimerThis 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.

Optimal Workplace Occupancy Strategies during the COVID-19 Pandemic

“…For instance, some studies have explored how enhanced levels of testing, contact-tracing, adoption of facial masks, and home quarantine can facilitate relaxation of social distancing interventions [30,31]. Similarly, [32] studied how designing an optimal employee screening strategy can reduce workplace infections, as well as how such a strategy affects the feasibility of return to work policies.…”

COVID-19 Test Frequency Versus Test Sensitivity in Disrupting SARS-CoV-2 Transmission: A Review of Statistical Modeling Simulations

“…They conclude test frequency and sensitivity are the primary factors impacting the effectiveness of a screening program, with repeat testing able to compensate for lower sensitivity. For example, a test with 80% sensitivity would have a 96% probability to detect an infected person after 2 test cycles 33 .…”

On the optimal presence strategies for workplace during pandemics: A COVID-19 inspired probabilistic model

“…It has been recommended that with the adoption of large-scale trace and test intervention along with moderate social distancing, the schools in I ˆle-de-France region can be reopened with maximum attendance of 50% that could avoid second wave [31]. How designing an optimal employee screening strategy can reduce the on-site infection in a workplace and based on this strategy, the feasibility of return to work policy are investigated in [32].…”