About: An international assessment of the COVID-19 pandemic using ensemble data assimilation

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: An international assessment of the COVID-19 pandemic using ensemble data assimilation Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	An international assessment of the COVID-19 pandemic using ensemble data assimilation
Creator	Jones, Christopher Amezcua, Javier Bocquet, Marc Carrassi, Alberto De Moraes, Rafael Evensen, Geir Farchi, Alban Fowler, Alison Houtekamer, Peter Pulido, Manuel Sampson, Christian Vossepoel, Femke
source	MedRxiv
abstract	This work shows how one can use iterative ensemble smoothers to effectively estimate parameters of an SEIR model with age-classes and compartments of sick, hospitalized, and dead. The data conditioned on are the daily numbers of accumulated deaths and the number of hospitalized. Also, it is possible to condition on the number of cases obtained from testing. We start from a wide prior distribution for the model parameters; then, the ensemble conditioning leads to a posterior ensemble of estimated parameters leading to model predictions in close agreement with the observations. The updated ensemble of model simulations have predictive capabilities and include uncertainty estimates. In particular, we estimate the effective reproductive number as a function of time, and we can assess the impact of different intervention measures. By starting from the updated set of model parameters, we can make accurate short-term predictions of the epidemic development given knowledge of the future effective reproductive number. Also, the model system allows for the computation of long-term scenarios of the epidemic under different assumptions. We have applied the model system on data sets from several countries with vastly different developments of the epidemic, and we can accurately model the development of the COVID-19 outbreak in these countries. We realize that more complex models, e.g., with regional compartments, may be desirable, and we suggest that the approach used here should be applicable also for these models.
has issue date	2020-06-12(xsd:dateTime)
bibo:doi	10.1101/2020.06.11.20128777
has license	medrxiv
sha1sum (hex)	68f6b55fc18afd18351f94c564a32ad22ee7bbfc
schema:url	https://doi.org/10.1101/2020.06.11.20128777
resource representing a document's title	An international assessment of the COVID-19 pandemic using ensemble data assimilation
resource representing a document's body	covid:68f6b55fc18afd18351f94c564a32ad22ee7bbfc#body_text
is schema:about of	named entity 'reproductive number' named entity 'long-term' named entity 'England' named entity 'Pará' named entity 'England' named entity 'COVID-19' named entity 'Alabama' named entity 'basic reproduction number' named entity 'fitting model' named entity 'South America' named entity 'correlation' named entity 'Italy' named entity 'COVID-19' named entity 'Bayesian' named entity '8,000' named entity 'ensemble methods' named entity 'epidemics' named entity 'epidemic' named entity 'medRxiv' named entity 'Italy' named entity 'standard deviation' named entity 'reproductive number' named entity 'France' named entity 'PHE' named entity 'medRxiv' named entity 'virus' named entity 'square matrix' named entity 'health service' named entity 'Kalman' named entity 'general population' named entity 'France' named entity 'COVID-19' named entity 'NHS' named entity 'England and Wales' named entity 'parameter space' named entity 'epidemic' named entity 'polymerase chain reaction' named entity 'correlation' named entity 'sample covariances' named entity 'North Brabant' named entity 'data assimilation' named entity 'quarantine' named entity 'SARS-Cov-2' named entity 'medRxiv' named entity 'ICU' named entity 'social distancing' named entity 'Norway' named entity 'data assimilation' named entity 'SEIR' named entity 'medRxiv' named entity 'parametrization' named entity 'medRxiv' named entity 'Quebec' named entity 'Europe' named entity 'evolution' named entity 'standard deviation' named entity 'epidemic' named entity 'virus' named entity 'England' named entity 'ensemble methods' named entity 'California' named entity 'Norway' named entity 'COVID-19' named entity 'data assimilation' named entity 'peer review' named entity 'NHS' named entity '1.2' named entity 'Norway' named entity 'COVID-19' named entity 'INSERM' named entity 'United States' named entity 'SEIR' named entity 'epidemics' named entity 'correlation'

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2025 OpenLink Software