International air travel-related control measures to contain the Covid-19 pandemic: A companion review to a Cochrane rapid review
Bibliographic record
Abstract
Background: COVID-19 has proven to be challenging to manage for many reasons, including its high infection rate. One of the potential ways to limit its spread is by limiting international travel. The objective of this systematic review was to identify, critically appraise and summarise evidence on international air travel-related control measures for COVID-19. Methods: and followed the same methods. In brief, we searched for clinical and modelling studies in general health and COVID-19-specific bibliographic databases. The primary outcome categories were (i) cases avoided, (ii) a shift in epidemic development and, (iii) cases detected. Results: From 6,202 citations identified by the search strategy, we included 22 new studies (modelling = 9, observational = 13) in addition to the 62 studies identified in the Cochrane review. Studies suggest that quarantine or microbial detection or a combination may avoid further cases. Similarly, these interventions may produce a positive shift in epidemic development and case detection may improve. Most studies were evaluated as having a moderate to critical risk of bias. The studies did not change the main conclusions of the Cochrane review nor the quality of the evidence (very low certainty); however, they added to the evidence base for most outcomes. Conclusions: Weak evidence supports the use of international air travel-related control measures to limit the spread of COVID-19 via air travel. More real-world studies are required to support these conclusions.
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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 | 0.011 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".