About: Chaos theory applied to the outbreak of Covid-19: an ancillary approach to decision-making in pandemic context

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About: Chaos theory applied to the outbreak of Covid-19: an ancillary approach to decision-making in pandemic context 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	Chaos theory applied to the outbreak of Covid-19: an ancillary approach to decision-making in pandemic context
Creator	Affiliations, Zhang, Y Roger, F Huc, M Kerr, Y Mangiarotti, S Peyre, M
Source	MedRxiv
abstract	Predicting the course of an epidemic is difficult, predicting the course of a pandemic from an emerging virus even more so. The validity of most predictive models relies on numerous parameters, involving biological and social characteristics often unknown or highly uncertain. COVID-19 pandemic brings additional factors such as population density and movements, behaviours, quality of the health system. Data from the COVID-19 epidemics in China, Japan and South Korea were used to build up data-driven deterministic models. Epidemics occurring in selected European countries rapidly evolved to overtake most Chinese provinces, to overtake South Korean model for France and even Hubei in the case of Italy and Spain. This approach was applied to other European countries and provides relevant information to inform disease control decision-making.
has issue date	2020-04-06(xsd:dateTime)
bibo:doi	10.1101/2020.04.02.20051441
has license	medrxiv
sha1sum (hex)	891750a4ccc2abe138f88668ea6dcb75260f7cbf
schema:url	https://doi.org/10.1101/2020.04.02.20051441
resource representing a document's title	Chaos theory applied to the outbreak of Covid-19: an ancillary approach to decision-making in pandemic context
resource representing a document's body	covid:891750a4ccc2abe138f88668ea6dcb75260f7cbf#body_text
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