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

Abstract

<jats:title>Abstract</jats:title> <jats:sec> <jats:title>BACKGROUND</jats:title> <jats:p>A range of near-real-time online/mobile mapping dashboards and applications have been used to track the COVID-19 pandemic worldwide. It remains unknown about small area-based spatiotemporal patterns of COVID-19 in the United States.</jats:p> </jats:sec> <jats:sec> <jats:title>METHODS</jats:title> <jats:p>We obtained county-based counts of COVID-19 cases confirmed in the United States from January 22 to May 13, 2020 (N=1,386,050). We characterized the dynamics of 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.</jats:p> </jats:sec> <jats:sec> <jats:title>RESULTS</jats:title> <jats:p>Along with the national plateau reached in early April, COVID-19 incidence significantly decreased in the Northeast (estimated weekly percentage changes [EWPC]: -16.6%), but remained increasing in the Midwest, South and West Regions (EWPCs: 13.2%, 5.6%, and 5.7%, respectively). Higher risks of clustering and incidence of COVID-19 were consistently observed in metropolitan vs rural counties, counties closest to core airports, most populous counties, and counties with highest proportion of racial/ethnic minority counties. However, geographic differences in the incidence have shrunk since early April, driven by a significant decrease in the incidence in these counties (EWPC range: -2.0% – -4.2%) and a consistent increase in other areas (EWPC range: 1.5% – 20.3%).</jats:p> </jats:sec> <jats:sec> <jats:title>CONCLUSIONS</jats:title> <jats:p>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-opening for states and localities.</jats:p> </jats:sec>

RETRACTED ARTICLE: Stay-at-home policy is a case of exception fallacy: an internet-based ecological study

“…The same explanation for the discrepancy can be applied to other publications where mathematical models were created to predict outcomes 14 – 18 . Most of these studies dealt with COVID-19 cases 33 , 34 and not observed deaths. Despite its limitations, reported deaths are likely to be more reliable than new case data.…”

“…Studies using Google COVID-19 Community Mobility Reports and the daily number of new COVID-19 cases have shown that over 7 weeks a strong correlation between staying at home and the reduction of COVID-19 cases in 20 counties in the United States 32 ; COVID-19 cases decreased by 49% after 2 weeks of staying at home 33 ; the incidence of new cases/100,000 people was also reduced 34 ; social distancing policies were associated with reduction in COVID-19 spread in the US 35 ; as well as in 49 countries around the world 24 . A recent report using Brazilian and European data has shown a correlation between NPI stringency and the spread of COVID-19 36 , 37 ; these analyses are debatable, however, due to their short time span and the type of time series behavior 38 , or for their use of Pearson’s correlation in the context of non-stationary time series 35 .…”

Associations Between Governor Political Affiliation and COVID-19 Cases, Deaths, and Testing in the U.S.

“…7,9,10 As such, political affiliation of state leaders, and specifically governors, might best capture the omnibus impact of state policies. Therefore, the purpose of this study is to quantify differences in incidence, death, testing, and test positivity rates over time, stratified by governors' political affiliation among the 50 states and 3,4,6,7 it was hypothesized that states with Democratic governors would have higher incidence, death, and test positivity rates early in the pandemic due to points of entry for the virus, 11,12 but that the trends would reverse in later months, reflecting policy differences that break along party lines. The IRBs at the Medical University of South Carolina and Johns Hopkins Bloomberg School of Public Health deemed this research exempt.…”

SARS-CoV-2, Influenza und Norovirus

“…Der Vergleich der 3 Infektionserkrankungen zeigt zwar Parallelen die Erkrankungshäufigkeiten bei zunehmendem Alter betreffend, aber auch massive Unterschiede in der Altersverteilung der hospitalisierten Patienten, die für SARS-CoV‑2 keine Häufung im Kindes- und Jugendalter ergaben (Abb. 1 b; [ 16 , 20 ]).…”