MétaCan
Menu
Back to cohort
Record W3083783694 · doi:10.1503/cmaj.201128

Active testing of groups at increased risk of acquiring SARS-CoV-2 in Canada: costs and human resource needs

2020· article· en· W3083783694 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCanadian Medical Association Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 detection and testing
Canadian institutionsMcGill UniversityUniversité LavalThe Quebec Population Health Research NetworkMcGill University Health Centre
Fundersnot available
KeywordsMedicineContact tracingPopulationHealth careEnvironmental healthMedical emergencyFamily medicineEmergency medicineCoronavirus disease 2019 (COVID-19)DiseaseInternal medicineInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

BACKGROUND: Testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is largely passive, which impedes epidemic control. We defined active testing strategies for SARS-CoV-2 using reverse transcription polymerase chain reaction (RT-PCR) for groups at increased risk of acquiring SARS-CoV-2 in all Canadian provinces. METHODS: We identified 5 groups who should be prioritized for active RT-PCR testing: contacts of people who are positive for SARS-CoV-2, and 4 at-risk populations - hospital employees, community health care workers and people in long-term care facilities, essential business employees, and schoolchildren and staff. We estimated costs, human resources and laboratory capacity required to test people in each group or to perform surveillance testing in random samples. RESULTS: During July 8-17, 2020, across all provinces in Canada, an average of 41 751 RT-PCR tests were performed daily; we estimated this required 5122 personnel and cost $2.4 million per day ($67.8 million per month). Systematic contact tracing and testing would increase personnel needs 1.2-fold and monthly costs to $78.9 million. Conducted over a month, testing all hospital employees would require 1823 additional personnel, costing $29.0 million; testing all community health care workers and persons in long-term care facilities would require 11 074 additional personnel and cost $124.8 million; and testing all essential employees would cost $321.7 million, requiring 25 965 added personnel. Testing the larger population within schools over 6 weeks would require 46 368 added personnel and cost $816.0 million. Interventions addressing inefficiencies, including saliva-based sampling and pooling samples, could reduce costs by 40% and personnel by 20%. Surveillance testing in population samples other than contacts would cost 5% of the cost of a universal approach to testing at-risk populations. INTERPRETATION: Active testing of groups at increased risk of acquiring SARS-CoV-2 appears feasible and would support the safe reopening of the economy and schools more broadly. This strategy also appears affordable compared with the $169.2 billion committed by the federal government as a response to the pandemic as of June 2020.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.024
GPT teacher head0.251
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it