International Travel-Related Control Measures to contain The Covid-19 Pandemic: An update to a Cochrane Rapid Review
Bibliographic record
Abstract
Abstract Background COVID-19 has proven to be more difficult to manage for many reasons including its high infectivity rate. One of the potential ways to limit its spread is by controlling international travel. The objective of this systematic review is to identify, critically-appraise and summarize evidence on international travel-related control measures. Methods This review is based on the Cochrane review: International travel-related control measures to contain the COVID-19 pandemic 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. Secondary outcomes were other infectious disease transmission outcomes, healthcare utilisation, resource requirements and adverse effects if identified in studies assessing at least one primary outcome. Results We assessed 66 full-text articles that met with our inclusion criteria. Seventeen new studies (modelling = 9, observational = 8) were identified in the updated search. Most studies were of critical to moderate risk of bias. The added studies did not change the main conclusions of the Cochrane review nor the quality of the evidence (very low to low certainty). However, it did add to the evidence base for most outcomes. Conclusions Weak evidence supports the use of international travel-related control measures to limit the spread of COVID-19 via air travel. 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 | 0.020 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".