The use of quarantine as an international travel measure during the COVID-19 pandemic: A comparative analysis of implementation and equity impacts in five “exemplar” countries
Bibliographic record
Abstract
During the COVID-19 pandemic, virtually all countries used a range of measures to mitigate the risks of virus introduction and onward transmission via international travel. Quarantine was a key international travel measure (ITM) used by governments to achieve public health goals. However, the highly varied ways in which quarantine was implemented makes lesson learning for future pandemics challenging. Moreover, most studies overlook the secondary impacts of ITMs on individuals and populations including potential inequities in their distribution. This paper comparatively analyses five countries deemed exemplary in their implementation of quarantine during COVID-19. Building on Damschroder's Consolidated Framework for Implementation Research, we apply eight variables to identify similarities and differences in quarantine use. We drew on our standardized coding of ITMs in the WHO Public Health and Social Measures dataset, and additional on-line searches, to compile data on each variable. Findings show that the five countries were early adopters of quarantine, applied them relatively stringently, and maintained them throughout the emergency phase of the pandemic to effectively advance public health goals. However, the countries differed in how secondary impacts were managed, resulting in the inequitable distribution of opportunity and burden for some individuals and populations. We conclude that exemplary implementation of quarantine during future public health emergencies should consider both public health goals and the equity of secondary impacts.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".