Disproportionate incidence of COVID-19 in African Americans correlates with dynamic segregation

TL;DR

This study links the higher COVID-19 rates in African American communities to their dynamic segregation and mobility patterns, emphasizing the importance of commuting behaviors over residential location in disease spread.

Contribution

Introduces the concept of dynamic segregation and demonstrates its significant association with COVID-19 incidence and mortality in African American communities using extensive mobility data.

Findings

Dynamic segregation correlates with COVID-19 incidence and mortality.

Mobility and commuting patterns are more relevant than residential location.

Understanding where people commute helps improve disease modeling.

Abstract

Socio-economic disparities quite often have a central role in the unfolding of large-scale catastrophic events. One of the most concerning aspects of the ongoing COVID-19 pandemics is that it disproportionately affects people from Black and African American backgrounds creating an unexpected infection gap. Interestingly, the abnormal impact on these ethnic groups seem to be almost uncorrelated with other risk factors, including co-morbidity, poverty, level of education, access to healthcare, residential segregation, and response to cures. A proposed explanation for the observed incidence gap is that people from African American backgrounds are more often employed in low-income service jobs, and are thus more exposed to infection through face-to-face contacts, but the lack of direct data has not allowed to draw strong conclusions in this sense so far. Here we introduce the concept of dynamic segregation, that is the extent to which a given group of people is internally clustered or exposed to other groups, as a result of mobility and commuting habits. By analysing census and mobility data on more than 120 major US cities, we found that the dynamic segregation of African American communities is significantly associated with the weekly excess COVID-19 incidence and mortality in those communities. The results confirm that knowing where people commute to, rather than where they live, is much more relevant for disease modelling.