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


Journal

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
ISSN: 1537-6591
Titre abrégé: Clin Infect Dis
Pays: United States
ID NLM: 9203213

Informations de publication

Date de publication:
15 12 2020
Historique:
received: 03 05 2020
accepted: 02 07 2020
pubmed: 8 7 2020
medline: 28 1 2021
entrez: 8 7 2020
Statut: ppublish

Résumé

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. 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%). 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). 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.

Sections du résumé

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.

Identifiants

pubmed: 32634823
pii: 5868532
doi: 10.1093/cid/ciaa901
pmc: PMC7454477
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

3196-3203

Subventions

Organisme : Medical Research Council
ID : MC_PC_19012
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/R015600/1
Pays : United Kingdom

Informations de copyright

© Crown copyright 2020.

Auteurs

Frank G Sandmann (FG)

Statistics, Modelling, and Economics Department, National Infection Service, Public Health England, London, United Kingdom.
Department of Infectious Disease Epidemiology and NIHR Health Protection Research Unit in Modelling and Health Economics, London School of Hygiene and Tropical Medicine, London, United Kingdom.

Peter J White (PJ)

Statistics, Modelling, and Economics Department, National Infection Service, Public Health England, London, United Kingdom.
MRC Centre for Global Infectious Disease Analysis and NIHR Health Protection Research Unit in Modelling and Health Economics, Imperial College London, London, United Kingdom.

Mary Ramsay (M)

Immunisation and Countermeasures Department, National Infection Service, Public Health England, London, United Kingdom.

Mark Jit (M)

Statistics, Modelling, and Economics Department, National Infection Service, Public Health England, London, United Kingdom.
Department of Infectious Disease Epidemiology and NIHR Health Protection Research Unit in Modelling and Health Economics, London School of Hygiene and Tropical Medicine, London, United Kingdom.

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