COVID-19 International Border Surveillance Cohort Study at Toronto’s Pearson Airport
Bibliographic record
Abstract
Abstract Objectives The primary objective was to estimate the positivity rate of air travelers coming to Toronto, Canada in September and October, 2020, at arrival, day 7 and day 14. Secondary objectives were to estimate degree of risk based on country of origin; to assess knowledge and attitudes towards COVID-19 control measures; and subjective well-being during the quarantine period. Design Prospective cohort of arriving international travelers. Setting Toronto Pearson Airport Terminal 1, Toronto, Canada. Participants Passengers arriving on international flights. Inclusion criteria were those aged 18 or older who had a final destination within 100 km of the airport; spoke English or French; and provided consent. Excluded were those taking a connecting flight; who had no internet access; who exhibited symptoms of COVID-19 on arrival; or who were exempted from quarantine. Main outcome measures Positive for SARS-CoV-2 virus on RT-PCR with self-administered nasal-oral swab, and general well-being using the WHO-5 index. Results Of 16,361 passengers enrolled, 248 (1·5%, 95% CI 1.3%,1.5%) tested positive. Of these, 167 (67%) were identified on arrival, 67 (27%) on day 7, and 14 (6%) on day 14. The positivity rate increased from 1% in September to 2% in October. Average well-being score declined from 19.8 (out of a maximum of 25) to 15.5 between arrival and day 7 (p<0.001). Conclusions A single arrival test will pick up two-thirds of individuals who will become positive, with most of the rest detected on the second test at day 7. These results support strategies identified through mathematical models that a reduced quarantine combined with testing can be as effective as a 14 day quarantine. Article Summary Strengths and limitations of this study Decisions regarding border restrictions have been based on trial and error and mathematical models with limited empirical data to support such decision-making. This study assessed the prevalence of SARS-CoV-2 in a cohort of international travellers at arrival, day 7 and 14 of quarantine. It is limited to one airport and there is the potential from bias due to non-participation and loss to follow-up. Self-collected nasal-oral swabs were used which facilitated participation but may have reduced sensitivity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".