Estimating effects of physical distancing on the COVID-19 pandemic using an urban mobility index
Notice bibliographique
Résumé
Abstract Background Governments have implemented population-wide physical distancing measures to control COVID-19, but metrics evaluating their effectiveness are not readily available. Methods We used a publicly available mobility index from a popular transit application to evaluate the effect of physical distancing on infection growth rates and reproductive numbers in 40 jurisdictions between March 23 and April 12, 2020. Findings A 10% decrease in mobility was associated with a 14.6% decrease (exp(β) = 0·854; 95% credible interval: 0·835, 0·873) in the average daily growth rate and a −0·061 (95% CI: −0·071, −0·052) change in the instantaneous reproductive number two weeks later. Interpretation Our analysis demonstrates that decreases in urban mobility were predictive of declines in epidemic growth. Mobility metrics offer an appealing method to calibrate population-level physical distancing policy and implementation, especially as jurisdictions relax restrictions and consider alternative physical distancing strategies. Funding No external funding was received for this study. Research in Context Evidence before this study Widespread physical distancing interventions implemented in response to the COVID-19 pandemic led to sharp declines in global mobility throughout March 2020. Real-time metrics to evaluate the effects of these measures on future case growth rates will be useful for calibrating further interventions, especially as jurisdictions begin to relax restrictions. We searched PubMed on May 22, 2020 for studies reporting the use of aggregated mobility data to measure the effects of physical distancing on COVID-19 cases, using the keywords “COVID-19”, “2019-nCoV”, or “SARS-CoV-2” in combination with “mobility”, “movement”, “phone”, “Google”, or “Apple”. We scanned 252 published studies and found one that used mobility data to estimate the effects of physical distancing. This study evaluated temporal trends in reported cases in four U.S. metropolitan areas using a metric measuring the percentage of cell phone users leaving their homes. Many published papers examined how national and international travel predicted the spatial distribution of cases (particularly outflow from Wuhan, China), but very little has been published on metrics that could be used as prospective, proximal indicators of future case growth. We also identified a series of reports released by the Imperial College COVID-19 Response Team and several manuscripts deposited on preprint servers such as medRxiv addressing this topic, demonstrating this is an active area of research. Added value of this study We demonstrate that changes in a publicly available urban mobility index reported in over 40 global cities were associated with COVID-19 case growth rates and estimated reproductive numbers two to three weeks later. These cities, spread over 5 continents, include many regional epicenters of COVID-19 outbreaks. This is one of only a few studies using a mobility metric applicable to future growth rates that is both publicly available and international in scope. Implications of all the available evidence Restrictions on human mobility have proved effective for controlling COVID-19 in China and the rest of the world. However, such drastic public health measures cannot be sustained indefinitely and are currently being relaxed in many jurisdictions. Publicly available mobility metrics offer a method of estimating the effects of changes in mobility before they are reflected in the trajectory of COVID-19 case growth rates and thus merit further evaluation.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,020 | 0,085 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,007 |
| Bibliométrie | 0,005 | 0,005 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 source (Gemma direct ou Codex distillé), 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 ».