About: Predicting the COVID-19 epidemic in Algeria using the SIR model

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

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Predicting the COVID-19 epidemic in Algeria using the SIR model
Creator	Boudrioua, Abderrahmane Boudrioua, Mohamed
source	MedRxiv
abstract	The aim of this study is to predict the daily infected cases with Coronavirus (COVID-19) in Algeria. We apply the SIR model on data from 25 February 2020 to 29 April 2020 for the prediction. We estimate the parameters of our model by minimizing the negative log likelihood function using the Nelder- Mead method. We found that the epidemic peak reach July month and the COVID-19 disease will disappear in the month of October. We suggest that Algerian authorities need to implement a strict containment strategy over a long period to effectively reduce the epidemic size.
has issue date	2020-04-29(xsd:dateTime)
bibo:doi	10.1101/2020.04.25.20079467
has license	medrxiv
sha1sum (hex)	7f90c6f3c1e95dee62ba10ff233bb4734c0aa45a
schema:url	https://doi.org/10.1101/2020.04.25.20079467
resource representing a document's title	Predicting the COVID-19 epidemic in Algeria using the SIR model
resource representing a document's body	covid:7f90c6f3c1e95dee62ba10ff233bb4734c0aa45a#body_text
is schema:about of	named entity 'AIM' named entity 'ALGERIA' named entity 'INFECTED' named entity 'DAILY' named entity 'BDF' »more»

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