About: Stochastic Compartmental Modelling of SARS-CoV-2 with Approximate Bayesian Computation

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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	Stochastic Compartmental Modelling of SARS-CoV-2 with Approximate Bayesian Computation
Creator	Chandra, Vedant
source	MedRxiv
abstract	In this proof-of-concept study, we model the spread of SARS-CoV-2 in various environments with a stochastic susceptible-infectious-recovered (SIR) compartmental model. We fit this model to the latest epidemic data with an approximate Bayesian computation (ABC) technique. Within this SIR-ABC framework, we extrapolate long-term infection curves for several regions and evaluate their steepness. We propose several applications and extensions of the SIR-ABC technique.
has issue date	2020-04-01(xsd:dateTime)
bibo:doi	10.1101/2020.03.29.20046862
has license	medrxiv
sha1sum (hex)	c3c1f7896932b6295deccd52b23336f3149720fc
schema:url	https://doi.org/10.1101/2020.03.29.20046862
resource representing a document's title	Stochastic Compartmental Modelling of SARS-CoV-2 with Approximate Bayesian Computation
resource representing a document's body	covid:c3c1f7896932b6295deccd52b23336f3149720fc#body_text
is schema:about of	named entity 'Stochastic' named entity 'SARS-COV-2' named entity 'MODEL' named entity 'LONG-TERM' named entity 'APPLICATIONS' »more»

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