Spatial correlations in geographical spreading of COVID-19 in the United States

Abstract

The global spread of the COVID-19 pandemic has followed complex pathways, largely attributed to the high virus infectivity, human travel patterns, and the implementation of multiple mitigation measures. The resulting geographic patterns describe the evolution of the epidemic and can indicate areas that are at risk of an outbreak. Here, we analyze the spatial correlations of new active cases in the USA at the county level and characterize the extent of these correlations at different times. We show that the epidemic did not progress uniformly and we identify various stages which are distinguished by significant differences in the correlation length. Our results indicate that the correlation length may be large even during periods when the number of cases declines. We find that correlations between urban centers were much more significant than between rural areas and this finding indicates that long-range spreading was mainly facilitated by travel between cities, especially at the first months of the epidemic. We also show the existence of a percolation transition in November 2020, when the largest part of the country was connected to a spanning cluster, and a smaller-scale transition in January 2021, with both times corresponding to the peak of the epidemic in the country.

Predicting COVID-19 transmission in a student population in Seoul, South Korea, 2020–2021

“…A study from U.S. showed that the correlations between urban centers were significant indicating that spreading was facilitated by travel between cities. 8) The central part of the city, with heavy commuter as it serves as business district and relatively smaller number of population residing, was shown to have the highest attack rate among all districts, as in line with this finding. Given that the school closure measures were uniformly practiced in all 25 districts in Seoul, our finding suggest that the community-onset SARS-CoV-2 transmission was likely the cause of COVID-19 clusters in student population.…”

Disruption of National Cancer Database Data Models in the First Year of the COVID-19 Pandemic

“…3,17 These trends mirror timelines previously documented for cancer screening, diagnosis, and treatment, as the health care system was strained in the early days of the pandemic. [6][7][8]16,18,19 Temporal geographic variation in pandemic spread has been well documented 20,21 and significant variance in geographic distribution of cancer cases in the NCDB was noted in 2020.…”

On the intrinsic dimensionality of Covid-19 data: a global perspective

“…McLafferty (2010) suggests that geographically close countries are likely to share spatio-temporal dynamics due to human spatial dynamics and similar demographic factors across geographic regions. In reviewing the available literature, McMahon et al (2022) highlight that a country's interconnectedness influences the spreading dynamics of Covid-19. This literature suggests that geographical closeness and interconnectivity have substantial implications for the spreading dynamics of Covid-19, allowing this to be a potential explanation for the spatial autocorrelation identified in the complexity of spreading dynamics observed in the analysis.…”