About: Modelling COVID-19

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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	Modelling COVID-19
Creator	Dye, Christopher Tian, Huaiyu Lloyd-Smith, James Leung, Gabriel Gutierrez, Bernardo Vespignani, Alessandro Kraemer, Moritz Scarpino, Samuel Leung, Kathy Eggo, Rosalind Wu, Joseph Shrestha, Munik
Source	PMC
abstract	As the COVID-19 pandemic continues, mathematical epidemiologists share their views on what models reveal about how the disease has spread, the current state of play and what work still needs to be done.
has issue date	2020-05-06(xsd:dateTime)
bibo:doi	10.1038/s42254-020-0178-4
has license	no-cc
sha1sum (hex)	e323aafda58773f40e4c5d720fa73be2814160cd
schema:url	https://doi.org/10.1038/s42254-020-0178-4
resource representing a document's title	Modelling COVID-19
has PubMed Central identifier	PMC7201389
schema:publication	Nat Rev Phys
resource representing a document's body	covid:e323aafda58773f40e4c5d720fa73be2814160cd#body_text
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