TY - JOUR
T1 - Spatiotemporal Characteristics of the COVID-19 Epidemic in the United States
AU - Wang, Yun
AU - Liu, Ying
AU - Struthers, James
AU - Lian, Min
N1 - Publisher Copyright:
© 2020 The Author(s). Published by Oxford University Press forthe Infectious Diseases Society of America. All rights reserved.
PY - 2021/2/15
Y1 - 2021/2/15
N2 - 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.
AB - 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.
KW - COVID-19
KW - clustering
KW - epidemiology
KW - geography
KW - spatiotemporal trend
UR - http://www.scopus.com/inward/record.url?scp=85102152905&partnerID=8YFLogxK
U2 - 10.1093/cid/ciaa934
DO - 10.1093/cid/ciaa934
M3 - Article
C2 - 32640020
AN - SCOPUS:85102152905
SN - 1058-4838
VL - 72
SP - 643
EP - 651
JO - Clinical Infectious Diseases
JF - Clinical Infectious Diseases
IS - 4
ER -