  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.
