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, resulting in excess mortality and strained health systems in the United States. 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. Our work focuses on daily COVID-19 surveillance using 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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A description of data and methods relevant for covid19scan is found here:
Hohl, A., Delmelle, E.M., Desjardins, M.R., & Lan, Y. (2020). Daily Surveillance of COVID-19 using the Prospective Space-Time Scan Statistic in the United States. Spatial and Spatiotemporal Epidemiology. https://doi.org/10.1016/j.sste.2020.100354
Other relevant literature:
Hohl, A., Delmelle, E., & Desjardins, M. (2020). Rapid detection of COVID-19 clusters in the United States using a prospective space-time scan statistic: an update. SIGSPATIAL Special, 12(1), 27-33.
Desjardins, M. R., Hohl, A., & Delmelle, E. M. (2020). Rapid surveillance of COVID-19 in the United States using a prospective space-time scan statistic: Detecting and evaluating emerging clusters. Applied Geography, 102202.