The impact of vaccination status on importation of COVID-19 among international travellers
Bibliographic record
Abstract
Governments worldwide are looking for ways to safely enable international travel while mitigating the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and the associated coronavirus disease 2019 (COVID-19). However, few data describe the impact of vaccination on importation of COVID-19. We took advantage of the sequential introduction of two government policies in Canada to evaluate the real-world evidence of vaccine effectiveness among 30,361 international travellers arriving by air in Alberta, Canada. The proportion of COVID-19-positive results for travellers who were either fully vaccinated or partially vaccinated was 0.02% (95% CI: 0.00-0.10) (i.e. one positive case among 5,817 travellers). In contrast, 1.42% (95% CI: 1.27-1.58) of unvaccinated travellers tested positive for SARS-CoV-2 (341 cases among 24,034 travellers). These findings suggest that COVID-19 vaccinations approved in Canada, substantially reduced the risk of travel-related importation of COVID-19 when combined with other public health measures. The low absolute rate of infection among fully vaccinated or partially vaccinated international travellers may inform quarantine requirements in this population.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".