About: Parameter inference in small world network disease models with approximate Bayesian Computational methods

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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	Parameter inference in small world network disease models with approximate Bayesian Computational methods
Creator	Walker, David Small, Michael Allingham, David Lee, Joseph Wing, Heung
source	Elsevier; Medline; PMC
abstract	Abstract Small world network models have been effective in capturing the variable behaviour of reported case data of the SARS coronavirus outbreak in Hong Kong during 2003. Simulations of these models have previously been realized using informed “guesses” of the proposed model parameters and tested for consistency with the reported data by surrogate analysis. In this paper we attempt to provide statistically rigorous parameter distributions using Approximate Bayesian Computation sampling methods. We find that such sampling schemes are a useful framework for fitting parameters of stochastic small world network models where simulation of the system is straightforward but expressing a likelihood is cumbersome.
has issue date	2010-02-01(xsd:dateTime)
bibo:doi	10.1016/j.physa.2009.09.053
bibo:pmid	32288082
has license	els-covid
sha1sum (hex)	3f7bee005e8054be25b0a8796bb777bf2f8c7023
schema:url	https://doi.org/10.1016/j.physa.2009.09.053
resource representing a document's title	Parameter inference in small world network disease models with approximate Bayesian Computational methods
has PubMed Central identifier	PMC7126433
has PubMed identifier	32288082
schema:publication	Physica A: Statistical Mechanics and its Applications
resource representing a document's body	covid:3f7bee005e8054be25b0a8796bb777bf2f8c7023#body_text
is schema:about of	named entity 'stochastic' named entity 'informed' named entity 'coronavirus outbreak' named entity 'sampling methods' named entity 'Parameter' »more»

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