About: Estimating the establishment of local transmission and the cryptic phase of the COVID-19 pandemic in the USA

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About: Estimating the establishment of local transmission and the cryptic phase of the COVID-19 pandemic in the USA Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Estimating the establishment of local transmission and the cryptic phase of the COVID-19 pandemic in the USA
Creator	Viboud, Cécile Vespignani, Alessandro Ajelli, Marco Merler, Stefano Davis, Jessica Rossi, Luca Perra, Nicola Halloran, M Litvinova, Maria Longini, Ira Sun, Kaiyuan Pastore Y Piontti, Ana Chinazzi, Matteo Gioannini, Corrado Xiong, Xinyue Dean, Natalie Mu, Kunpeng
Source	MedRxiv; Medline
abstract	We use a global metapopulation transmission model to study the establishment of sustained and undetected community transmission of the COVID-19 epidemic in the United States. The model is calibrated on international case importations from mainland China and takes into account travel restrictions to and from international destinations. We estimate widespread community transmission of SARS-CoV-2 in February, 2020. Modeling results indicate international travel as the key driver of the introduction of SARS-CoV-2 in the West and East Coast metropolitan areas that could have been seeded as early as late-December, 2019. For most of the continental states the largest contribution of imported infections arrived through domestic travel flows.
has issue date	2020-07-07(xsd:dateTime)
bibo:doi	10.1101/2020.07.06.20140285
bibo:pmid	32676609
has license	medrxiv
sha1sum (hex)	99e62a063bc6b8aeec3dc76114b54ca7ebcc5cca
schema:url	https://doi.org/10.1101/2020.07.06.20140285
resource representing a document's title	Estimating the establishment of local transmission and the cryptic phase of the COVID-19 pandemic in the USA
has PubMed identifier	32676609
schema:publication	medRxiv : the preprint server for health sciences
resource representing a document's body	covid:99e62a063bc6b8aeec3dc76114b54ca7ebcc5cca#body_text
is schema:about of	named entity 'mainland China' named entity 'license' named entity 'pandemic' named entity 'PREPRINT' named entity 'METAPOPULATION' named entity 'COVID-19' named entity 'model' named entity 'epidemic' named entity 'air traffic' named entity 'mainland China' named entity 'infection' named entity 'epidemic' named entity 'epidemic' named entity 'Global Health Security Index' named entity 'peer review' named entity 'SARS-CoV-2' named entity 'inversely proportional' named entity 'subpopulation' named entity 'point in time' named entity '2.6' named entity 'epidemic' named entity 'early start' named entity 'epidemiology' named entity 'correlation' named entity 'medRxiv' named entity 'China' named entity 'epidemic' named entity 'peer review' named entity 'infection' named entity 'epidemic' named entity 'ABC' named entity 'infection' named entity 'medRxiv' named entity 'medRxiv' named entity 'local transmission' named entity 'CC-BY-NC-ND 4.0' named entity 'SARS-CoV-2' named entity 'SARS-CoV-2' named entity 'CC-BY-NC-ND 4.0' named entity 'medRxiv' named entity 'medRxiv' named entity 'epidemic' named entity 'posterior distribution' named entity 'California' named entity 'medRxiv' named entity 'ABC' named entity 'modeling of data' named entity 'peer review' named entity 'SARS-CoV-2' named entity 'Europe' named entity 'medRxiv' named entity 'heterogeneities' named entity 'SARS-CoV-2' named entity 'epidemic' named entity 'medRxiv' named entity 'International Air Transport Association' named entity 'China' named entity 'approximate Bayesian computation' named entity 'subpopulation' named entity 'epidemic' named entity 'positive correlation' named entity 'social distancing' named entity 'virus' named entity 'epidemic' named entity 'epidemic' named entity 'local transmission'

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