Journal article
Daily surveillance of COVID-19 using the prospective space-time scan statistic in the United States
Spatial and spatio-temporal epidemiology, v 34, 100354
01 Aug 2020
PMID: 32807396
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
•Prospective space-time statistics are particularly useful for COVID-19 surveillance.•Daily cluster detection can track the evolution of key hotspots of COVID-19.•Temporal trend towards smaller but more numerous clusters.•Time-periodic surveillance of COVID-19 facilitates decision-making in public health.•Web application for live results http://covid19scan.net.
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The 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 hotspots.
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Details
- Title
- Daily surveillance of COVID-19 using the prospective space-time scan statistic in the United States
- Creators
- Alexander Hohl - University of UtahEric M. Delmelle - University of North Carolina at CharlotteMichael R. Desjardins - Johns Hopkins UniversityYu Lan - University of North Carolina at Charlotte
- Publication Details
- Spatial and spatio-temporal epidemiology, v 34, 100354
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Urban Health Collaborative
- Web of Science ID
- WOS:000577470600003
- Scopus ID
- 2-s2.0-85087163958
- Other Identifier
- 991021874550204721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Public, Environmental & Occupational Health