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dc.creatorHohl, Alexander
dc.creatorDelmelle, Eric M.
dc.creatorDesjardins, Michael R.
dc.creatorLan, Yu
dc.date.accessioned2020-09-28T15:19:34Z
dc.date.available2020-09-28T15:19:34Z
dc.date.created2020
dc.identifier.issn1877-5845spa
dc.identifier.otherhttps://doi.org/10.1016/j.sste.2020.100354spa
dc.identifier.urihttp://hdl.handle.net/20.500.12010/13892
dc.description.abstractThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first discovered in late 2019 in Wuhan City, China. The virus may cause novel coronavirus disease 2019 (COVID-19) in symptomatic individuals. Since December of 2019, there have been over 7,000,000 confirmed cases and over 400,000 confirmed deaths worldwide. In the United States (U.S.), there have been over 2,000,000 confirmed cases and over 110,000 confirmed deaths. COVID-19 case data in the United States has been updated daily at the county level since the first case was reported in January of 2020. There currently lacks a study that showcases the novelty of daily COVID-19 surveillance using space-time cluster detection techniques. In this paper, we utilize a prospective Poisson space-time scan statistic to detect daily clusters of COVID-19 at the county level in the contiguous 48 U.S. and Washington D.C. As the pandemic progresses, we generally find an increase of smaller clusters of remarkably steady relative risk. Daily tracking of significant space-time clusters can facilitate decision-making and public health resource allocation by evaluating and visualizing the size, relative risk, and locations that are identified as COVID-19 hotspotsspa
dc.format.extent8 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherSpatial and Spatio-temporal Epidemiologyspa
dc.sourcereponame:Expeditio Repositorio Institucional UJTLspa
dc.sourceinstname:Universidad de Bogotá Jorge Tadeo Lozanospa
dc.subjectCOVID-19spa
dc.subjectPandemicspa
dc.subjectSpace-time clustersspa
dc.subjectDisease surveillancespa
dc.titleDaily surveillance of COVID-19 using the prospective space-time scan statistic in the United Statesspa
dc.type.localArtículospa
dc.subject.lembSíndrome respiratorio agudo gravespa
dc.subject.lembCOVID-19spa
dc.subject.lembSARS-CoV-2spa
dc.subject.lembCoronavirusspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersionspa
dc.rights.localAbierto (Texto Completo)spa
dc.identifier.doihttps://doi.org/10.1016/j.sste.2020.100354spa
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1spa


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