Daily surveillance of COVID-19 using the prospective space-time scan statistic in the United States. (August 2020)

• Record Type: Journal Article
• Authors: Hohl, Alexander; Delmelle, Eric M.; Desjardins, Michael R.; Lan, Yu
• Is Part Of: Spatial and spatio-temporal epidemiology. Volume 34(2020)
• Publication Date: 2020-08
• Subjects: COVID-19 -- SaTScan -- Space-time clusters -- Pandemic -- Disease surveillance; Epidemiology -- Statistical methods -- Periodicals; Epidemiology -- Periodicals; 614.4072
• DOI: 10.1016/j.sste.2020.100354
• Languages: English
• ISSNs: 1877-5845

Abstract

Highlights: 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 . Graphical abstract: Abstract: 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.