About: Adjoint-based Data Assimilation of an Epidemiology Model for the Covid-19 Pandemic in 2020

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About: Adjoint-based Data Assimilation of an Epidemiology Model for the Covid-19 Pandemic in 2020 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	Adjoint-based Data Assimilation of an Epidemiology Model for the Covid-19 Pandemic in 2020
Creator	Sesterhenn, Jörn
source	ArXiv
abstract	Data assimilation is used to optimally fit a classical epidemiology model to the Johns Hopkins data of the Covid-19 pandemic. The optimisation is based on the confirmed cases and confirmed deaths. This is the only data available with reasonable accuracy. Infection and recovery rates can be infered from the model as well as the model parameters. The parameters can be linked with government actions or events like the end of the holiday season. Based on this numbers predictions for the future can be made and control targets specified. With other words: We look for a solution to a given model which fits the given data in an optimal sense. Having that solution, we have all parameters.
has issue date	2020-03-29(xsd:dateTime)
bibo:doi	10.5281/zenodo.3732292
has license	arxiv
sha1sum (hex)	90839d82434a389bcb9d15e5bc18bab8ed7a8574
schema:url	https://doi.org/10.5281/zenodo.3732292
resource representing a document's title	Adjoint-based Data Assimilation of an Epidemiology Model for the Covid-19 Pandemic in 2020
resource representing a document's body	covid:90839d82434a389bcb9d15e5bc18bab8ed7a8574#body_text
is schema:about of	named entity 'This' named entity 'optimisation' named entity 'Covid-19' named entity 'TARGETS' named entity 'RATES' »more»

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