Optimizing Benefits of Testing Key Workers for Infection with SARS-CoV-2: A Mathematical Modeling Analysis

Abstract

Background: Internationally, key workers such as healthcare staff are advised to stay at home if they or household members experience coronavirus disease 2019 (COVID-19)-like symptoms. This potentially isolates/quarantines many staff without SARS-CoV-2, while not preventing transmission from staff with asymptomatic infection. We explored the impact of testing staff on absence durations from work and transmission risks to others.

Methods: We used a decision-analytic model for 1000 key workers to compare the baseline strategy of (S0) no RT-PCR testing of workers to testing workers (S1) with COVID-19-like symptoms in isolation, (S2) without COVID-19-like symptoms but in household quarantine, and (S3) all staff. We explored confirmatory re-testing scenarios of repeating all initial tests, initially positive tests, initially negative tests, or no re-testing. We varied all parameters, including the infection rate (0.1-20%), proportion asymptomatic (10-80%), sensitivity (60-95%), and specificity (90-100%).

Results: Testing all staff (S3) changes the risk of workplace transmission by -56.9 to +1.0 workers/1000 tests (with reductions throughout at RT-PCR sensitivity ≥65%), and absences by -0.5 to +3.6 days/test but at heightened testing needs of 989.6-1995.9 tests/1000 workers. Testing workers in household quarantine (S2) reduces absences the most by 3.0-6.9 days/test (at 47.0-210.4 tests/1000 workers), while increasing risk of workplace transmission by 0.02-49.5 infected workers/1000 tests (which can be minimized when re-testing initially negative tests).

Conclusions: Based on optimizing absence durations or transmission risk, our modeling suggests testing staff in household quarantine or all staff, depending on infection levels and testing capacities.

Keywords: COVID-19; RT-PCR; SARS-CoV-2; isolation; testing.

Figure 1.

Model decision tree of the explored testing strategies in key workers for infection with SARS-CoV-2, and the assumed absence durations (in days). For a technical description of the underlying model accounting for test sensitivity and specificity see Supplementary Figure 1. Abbreviations: COVID-19, coronavirus disease 2019; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2.

Figure 2.

Change in the days in self-isolation and the transmission risk per test (in rows), shown per strategy (in columns) for different proportions of SARS-CoV-2–infected workers and asymptomatic SARS-CoV-2 infection. Note that negative values of changes represent desirable reductions. Abbreviations: COVID-19, coronavirus disease 2019; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2.

Figure 3.

Change in the days in self-isolation and the transmission risk per test (in rows), shown per strategy (in columns) for different proportions of RT-PCR specificity and sensitivity. Note that negative values of changes represent desirable reductions. Abbreviations: COVID-19, coronavirus disease 2019; RT-PCR, reverse transcriptase–polymerase chain reaction.