SARS-CoV-2 molecular diagnosis at airports to minimize travel-related COVID-19 spread
Bibliographic record
Abstract
Following the identification of SARS-CoV-2, screening for air travel helped mitigate spread, yet lessons learned from a case study of air travel within Canada display enhanced techniques to better identify infected individuals, informing future responsive screening. While international travel bans limit infectious spread beyond a country's borders, such measures are hardly sustainable economically and infrequently address domestic travel. Here, we describe a case study from Canada, where a diagnostic laboratory at point of travel conducted real-time PCR-based detection of SARS-CoV-2 in support of existing interventions, including clinical and epidemiological questionnaires, and temperature checks. All mining workers departing from a populated urban area flying to one of two sites (Site A and B) in a remote northern Canadian region, which we deemed "at-risk", because healthcare services are limited and vulnerable to epidemics. Data collected between June and November 2020 on 15,873 clinical samples, indicate that molecular diagnosis allowed for identification of 13 infected individuals, who would have otherwise been missed by using solely nonpharmaceutical interventions. Overall, no outbreaks, COVID-19-related or other, were detected at the point of travel up to December 2021 since the implementation of the laboratory, suggesting this screening process is an effective means to protect at-risk communities. The success of this study suggests a process more practical than travel bans or an unfocused screening of air travelers everywhere.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 0.001 |
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".