The adoption of international travel measures during the first year of the COVID-19 pandemic: a descriptive analysis
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».