Evidence map›Paper›PMID 32511452›Full record

ArticlemedRxiv : the preprint server for health sciences2020

The effect of human mobility and control measures on the COVID-19 epidemic in China.

Moritz U G Kraemer, Chia-Hung Yang, Bernardo Gutierrez, Chieh-Hsi Wu, Brennan Klein, David M Pigott, open COVID-19 data working group, Louis du Plessis, Nuno R Faria, Ruoran Li and 9 more

Open access · greenAbstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 361 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

19 authors at 8 institutions in 5 countries.

Moritz U G KraemerDepartment of Zoology, University of Oxford, United Kingdom.
Chia-Hung YangNetwork Science Institute, Northeastern University, Boston, United States.
Bernardo GutierrezDepartment of Zoology, University of Oxford, United Kingdom.
Chieh-Hsi WuMathematical Sciences, University of Southampton, Southampton, United Kingdom.
Brennan KleinNetwork Science Institute, Northeastern University, Boston, United States.
David M PigottInstitute for Health Metrics and Evaluation, Department of Health Metrics, University of Washington, Seattle, United States.
open COVID-19 data working group
Louis du PlessisDepartment of Zoology, University of Oxford, United Kingdom.
Nuno R FariaDepartment of Zoology, University of Oxford, United Kingdom.
Ruoran LiHarvard T.H. Chan School of Public Health, Boston, United States.
William P HanageHarvard T.H. Chan School of Public Health, Boston, United States.
John S BrownsteinHarvard Medical School, Harvard University, Boston, United States.
Maylis LayanMathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France.
Alessandro VespignaniNetwork Science Institute, Northeastern University, Boston, United States.
Huaiyu TianState Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China.
Christopher DyeDepartment of Zoology, University of Oxford, United Kingdom.
Simon CauchemezMathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France.
Oliver G PybusDepartment of Zoology, University of Oxford, United Kingdom.
Samuel V ScarpinoNetwork Science Institute, Northeastern University, Boston, United States.
Northeastern University · USBoston University · USUniversity of Oxford · GBCentre National de la Recherche Scientifique · FRHarvard University · USBeijing Normal University · CNInstitute for Health Metrics and Evaluation · USUniversity of Southampton · GB

Funding

Understanding and controlling antibiotic resistanceU54GM088558 · NIGMS · HARVARD SCHOOL OF PUBLIC HEALTH · PI LIPSITCH, MARC · 2009 to 2018
$26.4M
Medical Research Council MR/S019510/1NIGMS NIH HHS U54 GM088558
6 · The paper itself

Abstract

The ongoing COVID-19 outbreak has expanded rapidly throughout China. Major behavioral, clinical, and state interventions are underway currently to mitigate the epidemic and prevent the persistence of the virus in human populations in China and worldwide. It remains unclear how these unprecedented interventions, including travel restrictions, have affected COVID-19 spread in China. We use real-time mobility data from Wuhan and detailed case data including travel history to elucidate the role of case importation on transmission in cities across China and ascertain the impact of control measures. Early on, the spatial distribution of COVID-19 cases in China was well explained by human mobility data. Following the implementation of control measures, this correlation dropped and growth rates became negative in most locations, although shifts in the demographics of reported cases are still indicative of local chains of transmission outside Wuhan. This study shows that the drastic control measures implemented in China have substantially mitigated the spread of COVID-19.

Identifiers

PMID32511452
PMCPMC7239080
OpenAlexW3010270046

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.