The association of community mobility with the time-varying reproduction number (R) of SARS-CoV-2: a modelling study across 330 local UK authorities
Notice bibliographique
Résumé
BACKGROUND: Community mobility data have been used to assess adherence to non-pharmaceutical interventions and its impact on SARS-CoV-2 transmission. We assessed the association between location-specific community mobility and the reproduction number (R) of SARS-CoV-2 across UK local authorities. METHODS: In this modelling study, we linked data on community mobility from Google with data on R from 330 UK local authorities, for the period June 1, 2020, to Feb 13, 2021. Six mobility metrics are available in the Google community mobility dataset: visits to retail and recreation places, visits to grocery and pharmacy stores, visits to transit stations, visits to parks, visits to workplaces, and length of stay in residential places. For each local authority, we modelled the weekly change in R (the R ratio) per a rescaled weekly percentage change in each location-specific mobility metric relative to a pre-pandemic baseline period (Jan 3-Feb 6, 2020), with results synthesised across local authorities using a random-effects meta-analysis. FINDINGS: On a weekly basis, increased visits to retail and recreation places were associated with a substantial increase in R (R ratio 1·053 [99·2% CI 1·041-1·065] per 15% weekly increase compared with baseline visits) as were increased visits to workplaces (R ratio 1·060 [1·046-1·074] per 10% increase compared with baseline visits). By comparison, increased visits to grocery and pharmacy stores were associated with a small but still statistically significant increase in R (R ratio 1·011 [1·005-1·017] per 5% weekly increase compared with baseline visits). Increased visits to parks were associated with a decreased R (R ratio 0·972 [0·965-0·980]), as were longer stays at residential areas (R ratio 0·952 [0·928-0·976]). Increased visits to transit stations were not associated with R nationally, but were associated with a substantial increase in R in cities. An increasing trend was observed for the first 6 weeks of 2021 in the effect of visits to retail and recreation places and workplaces on R. INTERPRETATION: Increased visits to retail and recreation places, workplaces, and transit stations in cities are important drivers of increased SARS-CoV-2 transmission; the increasing trend in the effects of these drivers in the first 6 weeks of 2021 was possibly associated with the emerging alpha (B.1.1.7) variant. These findings provide important evidence for the management of current and future mobility restrictions. FUNDING: Wellcome Trust and Data-Driven Innovation initiative.
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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 | 0,010 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».