Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 119, Issue 12, Pages -Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.2121675119
Keywords
COVID-19; spatial heterogeneity; diffusion; health disparities; social networks
Categories
Funding
- NSF [IIS-1939237, SES-1826589]
- NIH [P2CHD042828]
- University of California, Irvine Council on Research, Computing and Libraries grant
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The uneven spread of COVID-19 has led to different experiences for marginalized populations in urban areas. Computational models show that strong local cohesion in social contact networks in San Francisco leads to more early COVID-19 infections, particularly affecting Black and Hispanic communities. Therefore, local social cohesion can serve as a hidden source of risk for COVID-19 infection.
The uneven spread of COVID-19 has resulted in disparate experiences for marginalized populations in urban centers. Using computational models, we examine the effects of local cohesion on COVID-19 spread in social contact networks for the city of San Francisco, finding that more early COVID-19 infections occur in areas with strong local cohesion. This spatially correlated process tends to affect Black and Hispanic communities more than their non-Hispanic White counterparts. Local social cohesion thus acts as a potential source of hidden risk for COVID-19 infection.
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