About: A Syndromic Surveillance Tool to Detect Anomalous Clusters of COVID-19 Symptoms in the United States

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About: A Syndromic Surveillance Tool to Detect Anomalous Clusters of COVID-19 Symptoms in the United States Goto Sponge NotDistinct Permalink

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	A Syndromic Surveillance Tool to Detect Anomalous Clusters of COVID-19 Symptoms in the United States
Creator	Curriero, Frank Desjardins, Michael Stevens, Robert Aboumerhi, Khaled Corrigan, Anne »more»
source	MedRxiv
abstract	Coronavirus SARS-COV-2 infections continue to spread across the world, yet effective large-scale disease detection and prediction remain limited. COVID Control: A Johns Hopkins University Study, is a novel syndromic surveillance approach, which collects body temperature and COVID-like illness (CLI) symptoms across the US using a smartphone app and applies spatio-temporal clustering techniques and cross-correlation analysis to create maps of abnormal symptomatology incidence that are made publicly available. The results of the cross-correlation analysis identify optimal temporal lags between symptoms and a range of COVID-19 outcomes, with new taste/smell loss showing the highest correlations. We also identified temporal clusters of change in taste/smell entries and confirmed COVID-19 incidence in Baltimore City and County. Further, we utilized an extended simulated dataset to showcase our analytics in Maryland. The resulting clusters can serve as indicators of emerging COVID-19 outbreaks, and support syndromic surveillance as an early warning system for disease prevention and control.
has issue date	2020-08-21(xsd:dateTime)
bibo:doi	10.1101/2020.08.18.20177295
has license	medrxiv
sha1sum (hex)	e1678dc6fca6908536bfe009757ae12506bcada8
schema:url	https://doi.org/10.1101/2020.08.18.20177295
resource representing a document's title	A Syndromic Surveillance Tool to Detect Anomalous Clusters of COVID-19 Symptoms in the United States
resource representing a document's body	covid:e1678dc6fca6908536bfe009757ae12506bcada8#body_text
is schema:about of	named entity 'symptoms' named entity 'support' named entity 'app' covid:arg/e1678dc6fca6908536bfe009757ae12506bcada8 named entity 'temporal' »more»

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