About: Comparative prediction of confirmed cases with COVID-19 pandemic by machine learning, deterministic and stochastic SIR models

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About: Comparative prediction of confirmed cases with COVID-19 pandemic by machine learning, deterministic and stochastic SIR models 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	Comparative prediction of confirmed cases with COVID-19 pandemic by machine learning, deterministic and stochastic SIR models
Creator	Ndiaye, Babacar Seck, Diaraf Tendeng, Lena
Source	ArXiv
abstract	In this paper, we propose a machine learning technics and SIR models (deterministic and stochastic cases) with numerical approximations to predict the number of cases infected with the COVID-19, for both in few days and the following three weeks. Like in [1] and based on the public data from [2], we estimate parameters and make predictions to help on how to find concrete actions to control the situation. Under optimistic estimation, the pandemic in some countries will end soon, while for most of the countries in the world, the hit of anti-pandemic will be no later than the beginning of May.
has issue date	2020-04-24(xsd:dateTime)
has license	arxiv
sha1sum (hex)	1d93c5f7a7851227cc1aaf7ed0f4ee3aa72ac159
resource representing a document's title	Comparative prediction of confirmed cases with COVID-19 pandemic by machine learning, deterministic and stochastic SIR models
resource representing a document's body	covid:1d93c5f7a7851227cc1aaf7ed0f4ee3aa72ac159#body_text
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