Geographical patterns of social cohesion drive disparities in early COVID infection hazard

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.

Automated summary

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.