Effectiveness of quarantine and testing to prevent COVID-19 transmission from arriving travelers
Bibliographic record
Abstract
ABSTRACT Objective To assess the efficacy of policies designed to reduce the risk of international travelers importing SARS-CoV-2 into a country. Method We developed a simulation model and compared mandatory quarantine, testing, and combined quarantine and testing. We assessed the sensitivity of policy effectiveness to the timing of testing, compliance with quarantine and isolation, and other factors. Results In the base scenario, a 2-day quarantine reduced more risk than testing alone. The effectiveness of a 5-day quarantine requirement with perfect compliance was similar to a 14-day quarantine with moderate compliance. Testing 72h before arrival reduced less than 10% of in-country transmission risk across all scenarios. The addition of testing to quarantine added value for shorter quarantine lengths, when testing compliance was enforced, and when testing was performed near the end of quarantine. Conclusions Quarantine is more effective at preventing SARS-CoV-2 transmission from arriving travelers than testing alone, but testing combined with quarantine can add value if longer quarantine requirements are infeasible. Enforcing compliance with quarantine and isolation is critical. Requiring a negative test up to 72h before arrival may have limited effectiveness.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".