The adoption of international travel measures during the first year of the COVID-19 pandemic: a descriptive analysis
Bibliographic record
Abstract
OBJECTIVE: To describe the adoption of international travel measures during the first year of the COVID-19 pandemic. METHODS: To comprehensively analyze the measures adopted, we constructed a dataset based on the WHO's Public Health and Social Measures (PHSM) database, which covered 252 countries, territories, or other areas (CTAs), including all 194 WHO Member States, from December 31, 2019, to December 31, 2020. We examined the adoption of measures by type, over time, and by the implementing and targeted CTA, including their levels of income. FINDINGS: We identified 11,431 international travel measures implemented during the first year of the pandemic. The adoption of measures was rapid and widespread: over 60% of Member States had adopted a travel measure before the WHO declared COVID-19 a Public Health Emergency of International Concern on January 30, 2020. Initially, health screening and travel restrictions were the most adopted measures; however, quarantine and testing became more widely adopted over time. Although only a small portion of the total measures adopted constituted full border closure, approximately half of all Member States implemented this measure. Many travel measures targeted all CTAs but were unlikely to have been adopted universally enough to provide public health benefits. Low-income countries relied more on more universal measures, including full border closure, and were slower in scaling up testing compared to higher-income countries. CONCLUSION: The adoption of international travel measures during the first year of the COVID-19 pandemic varied across jurisdictions and over time. Lower-income countries used a different mix and scaled-up measures slower than higher-income countries. Understanding what measures were used is crucial for assessing their effectiveness in controlling the spread of COVID-19, reviewing the usefulness of the International Health Regulations, and informing future pandemic preparedness and response activities.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".