Facets (new session)
Description
Metadata
Settings
owl:sameAs
Inference Rule:
b3s
b3sifp
dbprdf-label
facets
http://dbpedia.org/resource/inference/rules/dbpedia#
http://dbpedia.org/resource/inference/rules/opencyc#
http://dbpedia.org/resource/inference/rules/umbel#
http://dbpedia.org/resource/inference/rules/yago#
http://dbpedia.org/schema/property_rules#
http://www.ontologyportal.org/inference/rules/SUMO#
http://www.ontologyportal.org/inference/rules/WordNet#
http://www.w3.org/2002/07/owl#
ldp
oplweb
skos-trans
virtrdf-label
None
About:
Predicting the impact of asymptomatic transmission, non-pharmaceutical intervention and testing on the spread of COVID19 COVID19
Goto
Sponge
NotDistinct
Permalink
An Entity of Type :
schema:ScholarlyArticle
, within Data Space :
covidontheweb.inria.fr
associated with source
document(s)
Type:
Academic Article
research paper
schema:ScholarlyArticle
New Facet based on Instances of this Class
Attributes
Values
type
Academic Article
research paper
schema:ScholarlyArticle
isDefinedBy
Covid-on-the-Web dataset
title
Predicting the impact of asymptomatic transmission, non-pharmaceutical intervention and testing on the spread of COVID19 COVID19
Creator
Bianco, Simone
Hu, Kun
Kaufman, James
Schwartz, Ira
source
MedRxiv
abstract
We introduce a novel mathematical model to analyze the effect of removing non-pharmaceutical interventions on the spread of COVID19 as a function of disease testing rate. We find that relaxing interventions has a strong impact on the size of the epidemic peak as a function of intervention re- moval time. We show that it is essential for predictive models to explicitly capture transmission from asymptomatic carriers and important to obtain precise information on asymptomatic transmission by testing. The asymptomatic reservoir, reported to account for as much as 85% of transmission, will contribute to resurgence of the epidemic if public health interventions are removed too soon. Use of more basic models that fail to capture asymptomatic transmission can result in large errors in predicted clinical caseload or in fitted epidemiological parameters and, therefore, may be unreliable in estimating the risk of a second wave based on the timing of terminated interventions.
has issue date
2020-04-22
(
xsd:dateTime
)
bibo:doi
10.1101/2020.04.16.20068387
has license
medrxiv
sha1sum (hex)
220ade47ef4ba847e55fb3ab7556ce3533ff66ca
schema:url
https://doi.org/10.1101/2020.04.16.20068387
resource representing a document's title
Predicting the impact of asymptomatic transmission, non-pharmaceutical intervention and testing on the spread of COVID19 COVID19
resource representing a document's body
covid:220ade47ef4ba847e55fb3ab7556ce3533ff66ca#body_text
is
schema:about
of
named entity 'peak'
named entity 'public health'
named entity 'function'
named entity 'asymptomatic carriers'
named entity 'Predicting'
»more»
◂◂ First
◂ Prev
Next ▸
Last ▸▸
Page 1 of 4
Go
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