A syndromic surveillance tool to detect anomalous clusters of COVID-19 symptoms in the United States

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 illne...

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Autores principales:	Amparo Güemes, Soumyajit Ray, Khaled Aboumerhi, Michael R. Desjardins, Anton Kvit, Anne E. Corrigan, Brendan Fries, Timothy Shields, Robert D. Stevens, Frank C. Curriero, Ralph Etienne-Cummings
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c25c570211104bd6968c067d6bb6089d
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spelling	oai:doaj.org-article:c25c570211104bd6968c067d6bb6089d2021-12-02T13:34:51ZA syndromic surveillance tool to detect anomalous clusters of COVID-19 symptoms in the United States10.1038/s41598-021-84145-52045-2322https://doaj.org/article/c25c570211104bd6968c067d6bb6089d2021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84145-5https://doaj.org/toc/2045-2322Abstract 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.Amparo GüemesSoumyajit RayKhaled AboumerhiMichael R. DesjardinsAnton KvitAnne E. CorriganBrendan FriesTimothy ShieldsRobert D. StevensFrank C. CurrieroRalph Etienne-CummingsNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Amparo Güemes Soumyajit Ray Khaled Aboumerhi Michael R. Desjardins Anton Kvit Anne E. Corrigan Brendan Fries Timothy Shields Robert D. Stevens Frank C. Curriero Ralph Etienne-Cummings A syndromic surveillance tool to detect anomalous clusters of COVID-19 symptoms in the United States
description	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.
format	article
author	Amparo Güemes Soumyajit Ray Khaled Aboumerhi Michael R. Desjardins Anton Kvit Anne E. Corrigan Brendan Fries Timothy Shields Robert D. Stevens Frank C. Curriero Ralph Etienne-Cummings
author_facet	Amparo Güemes Soumyajit Ray Khaled Aboumerhi Michael R. Desjardins Anton Kvit Anne E. Corrigan Brendan Fries Timothy Shields Robert D. Stevens Frank C. Curriero Ralph Etienne-Cummings
author_sort	Amparo Güemes
title	A syndromic surveillance tool to detect anomalous clusters of COVID-19 symptoms in the United States
title_short	A syndromic surveillance tool to detect anomalous clusters of COVID-19 symptoms in the United States
title_full	A syndromic surveillance tool to detect anomalous clusters of COVID-19 symptoms in the United States
title_fullStr	A syndromic surveillance tool to detect anomalous clusters of COVID-19 symptoms in the United States
title_full_unstemmed	A syndromic surveillance tool to detect anomalous clusters of COVID-19 symptoms in the United States
title_sort	syndromic surveillance tool to detect anomalous clusters of covid-19 symptoms in the united states
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/c25c570211104bd6968c067d6bb6089d
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A syndromic surveillance tool to detect anomalous clusters of COVID-19 symptoms in the United States

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