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:
Real-time detection of COVID-19 epicenters within the United States using a network of smart thermometers
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
Real-time detection of COVID-19 epicenters within the United States using a network of smart thermometers
Creator
Affiliations,
Singh, I
Ariza, C
Chamberlain, S
Daitch, A
»more»
source
MedRxiv
abstract
Containing outbreaks of infectious disease requires rapid identification of transmission hotspots, as the COVID-19 pandemic demonstrates. Focusing limited public health resources on transmission hotspots can contain spread, thus reducing morbidity and mortality, but rapid data on community-level disease dynamics is often unavailable. Here, we demonstrate an approach to identify anomalously elevated levels of influenza-like illness (ILI) in real-time, at the scale of US counties. Leveraging data from a geospatial network of thermometers encompassing more than one million users across the US, we identify anomalies by generating accurate, county-specific forecasts of seasonal ILI from a point prior to a potential outbreak and comparing real-time data to these expectations. Anomalies are strongly correlated with COVID-19 case counts and may provide an early-warning system to locate outbreak epicenters.
has issue date
2020-04-10
(
xsd:dateTime
)
bibo:doi
10.1101/2020.04.06.20039909
has license
medrxiv
sha1sum (hex)
74963c370ebb91ba4a308e0eed2ff83310fe96f6
schema:url
https://doi.org/10.1101/2020.04.06.20039909
resource representing a document's title
Real-time detection of COVID-19 epicenters within the United States using a network of smart thermometers
resource representing a document's body
covid:74963c370ebb91ba4a308e0eed2ff83310fe96f6#body_text
is
schema:about
of
named entity 'identify'
named entity 'ILI'
named entity 'encompassing'
named entity 'users'
named entity 'identify'
»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