Active testing of groups at increased risk of acquiring SARS-CoV-2 in Canada: costs and human resource needs
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".