Daily surveillance of COVID-19 using the prospective space-time scan statistic in the United States
Data
2020Autor
Hohl, Alexander
Delmelle, Eric M.
Desjardins, Michael R.
Lan, Yu
Metadata
Mostrar registro completoResumo
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
Palabras clave
COVID-19; Pandemic; Space-time clusters; Disease surveillanceLink para o recurso
https://doi.org/10.1016/j.sste.2020.100354Collections
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