Delmelle, Eric
Hohl, Alexander
Desjardins, Michael R.
Lan, Yu

Faculty

1 online resource

2020

Journal article

articles

text

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 in- dividuals. Since December of 2019, there have been over 7,0 0 0,0 0 0 confirmed cases and over 40 0,0 0 0 confirmed deaths worldwide. In the United States (U.S.), there have been over 2,0 0 0,0 0 0 confirmed cases and over 110,0 0 0 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 gen- erally 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.

COVID-19 (Disease)
Virus diseases--Transmission

Spatial and spatio-temporal epidemiology

English

doi:10.1016/j.sste.2020.100354

https://doi.org/10.1016/j.sste.2020.100354

http://hdl.handle.net/20.500.13093/work:228

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