Spatiotemporal Characteristics of the COVID-19 Epidemic in the United States

Yun Wang, Ying Liu, James Struthers, Min Lian

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Background: A range of near-real-time online/mobile mapping dashboards and applications have been used to track the coronavirus disease 2019 (COVID-19) pandemic worldwide; however, small area-based spatiotemporal patterns of COVID-19 in the United States remain unknown. Methods: We obtained county-based counts of COVID-19 cases confirmed in the United States from 22 January to 13 May 2020(050). We characterized the dynamics of the COVID-19 epidemic through detecting weekly hotspots of newly confirmed cases using Spatial and Space-Time Scan Statistics and quantifying the trends of incidence of COVID-19 by county characteristics using the Joinpoint analysis. Results: Along with thenational plateau reached in early April, COVID-19 incidence significantly decreased in the Northeast (estimated weekly percentage change [EWPC]: -16.6%) but continued increasing in the Midwest, South, and West (EWPCs: 13.2%, 5.6%, and 5.7%, respectively). Higher risks of clustering and incidence of COVID-19 were consistently observed in metropolitan versus rural counties, counties closest to core airports, the most populous counties, and counties with the highest proportion of racial/ethnicminorities. However, geographic differences in incidence have shrunk since early April, driven by asignificant decrease in the incidence in these counties (EWPC range: -2.0%, -4.2%) and a consistentincrease in other areas (EWPC range: 1.5-20.3%). Conclusions: To substantially decrease the nationwide incidence of COVID-19, strict social-distancing measures should be continuously implemented, especially in geographic areas with increasing risks, including rural areas. Spatiotemporal characteristics and trends of COVID-19 should be considered in decision making on the timeline of re-openingfor states and localities.

Original languageEnglish
Pages (from-to)643-651
Number of pages9
JournalClinical Infectious Diseases
Volume72
Issue number4
DOIs
StatePublished - Feb 15 2021

Keywords

  • COVID-19
  • clustering
  • epidemiology
  • geography
  • spatiotemporal trend

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