About: A Recursive Bifurcation Model for Predicting the Peak of COVID-19 Virus Spread in United States and Germany

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About: A Recursive Bifurcation Model for Predicting the Peak of COVID-19 Virus Spread in United States and Germany Goto Sponge NotDistinct Permalink

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	A Recursive Bifurcation Model for Predicting the Peak of COVID-19 Virus Spread in United States and Germany
Creator	Shen, Julia
source	MedRxiv
abstract	Prediction on the peak time of COVID-19 virus spread is crucial to decision making on lockdown or closure of cities and states. In this paper we design a recursive bifurcation model for analyzing COVID-19 virus spread in different countries. The bifurcation facilitates a recursive processing of infected population through linear least-squares fitting. In addition, a nonlinear least-squares fitting is utilized to predict the future values of infected populations. Numerical results on the data from three countries (South Korea, United States and Germany) indicate the effectiveness of our approach.
has issue date	2020-04-14(xsd:dateTime)
bibo:doi	10.1101/2020.04.09.20059329
has license	medrxiv
sha1sum (hex)	8239f8a895b37bb61c33efd2912b55d807db0033
schema:url	https://doi.org/10.1101/2020.04.09.20059329
resource representing a document's title	A Recursive Bifurcation Model for Predicting the Peak of COVID-19 Virus Spread in United States and Germany
resource representing a document's body	covid:8239f8a895b37bb61c33efd2912b55d807db0033#body_text
is schema:about of	named entity 'BIFURCATION' named entity 'INFECTED POPULATION' named entity 'COVID-19 VIRUS' named entity 'DECISION MAKING' named entity 'PREDICT' »more»

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