Optimising benefits of testing key workers for infection with SARS-CoV-2: A mathematical modelling analysis.


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, whilst 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.

We used a decision-analytic model for 1,000 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%).

Testing all staff (S3) changes the risk of workplace transmission by -56.9 to +1.0 workers per 1,000 tests (with reductions throughout at RT-PCR sensitivity of ≥65%), and absences by 0.5 to +3.6 days per test but at heightened testing needs of 989.6-1995.9 tests per 1,000 workers. Testing workers in household-quarantine (S2) reduces absences the most by 3.0-6.9 days per test (at 47.0-210.4 tests per 1,000 workers), while increasing risk of workplace transmission by 0.02-49.5 infected workers per 1,000 tests (which can be minimised when re-testing initially-negative tests).

Based on optimising absence durations or transmission risk our modelling suggests testing staff in household-quarantine or all staff, depending on infection levels and testing capacities.

MIDAS Network Members

Mark Jit

Professor of Vaccine Epidemiology
London School of Hygiene & Tropical Medicine

Peter White

Professor in Public Health Modelling
Imperial College London