About: A note on tools for prediction under uncertainty and identifiability of SIR-like dynamical systems for epidemiology

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About: A note on tools for prediction under uncertainty and identifiability of SIR-like dynamical systems for epidemiology 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 note on tools for prediction under uncertainty and identifiability of SIR-like dynamical systems for epidemiology
Creator	Piazzola, Chiara Tamellini, Lorenzo Tempone, Raúl
source	ArXiv
abstract	We provide an overview of the methods that can be used for prediction under uncertainty and data fitting of dynamical systems, and of the fundamental challenges that arise in this context. The focus is on SIR-like models, that are being commonly used when attempting to predict the trend of the COVID-19 pandemic. In particular, we raise a warning flag about identifiability of the parameters of SIR-like models; often, it might be hard to infer the correct values of the parameters from data, even for very simple models, making it non-trivial to use these models for meaningful predictions. Most of the points that we touch upon are actually generally valid for inverse problems in more general setups.
has issue date	2020-08-04(xsd:dateTime)
has license	arxiv
sha1sum (hex)	dfb769c7efe4bf60dd1e8f1d70bd5f7b28642173
resource representing a document's title	A note on tools for prediction under uncertainty and identifiability of SIR-like dynamical systems for epidemiology
resource representing a document's body	covid:dfb769c7efe4bf60dd1e8f1d70bd5f7b28642173#body_text
is schema:about of	named entity 'epidemiology' named entity 'Sobol indices' named entity 'measurement errors' named entity 'conditional probabilities' named entity 'non-identifiable' »more»

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