Diffusion segregation and the disproportionate incidence of COVID-19 in African American communities

Abstract

One of the most concerning aspects of the ongoing COVID-19 pandemic is that it disproportionately affects people from some specific ethnic and socio-economic minorities. In particular, since from the beginning of the pandemic it has been clear that people from Black and African American backgrounds seem to be hit especially hard by the virus, creating a substantial infection gap. The observed abnormal impact on these ethnic groups could probably be due to the co-occurrence of other known risk factors, including co-morbidity, poverty, level of education, access to healthcare, residential segregation and response to cures, although those factors do not seem able to explain fully and in depth the excess incidence of infections and deaths among African Americans. Here, we introduce the concept of diffusion 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 major US cities, we found that the weekly excess COVID-19 incidence and mortality in African American communities at the beginning of the COVID-19 pandemic is significantly associated with their level of diffusion segregation. The results confirm that knowing where people commute to, rather than where they live, is potentially much more important to contain and curb the spreading of infectious diseases.

Quantifying ethnic segregation in cities through random walks

“…Despite we have focused exclusively on the characterisation of ethnic segregation, the methodology introduced here can be used to quantify the spatial variance and spatial diversity of the distribution of any categorical variable, including socio-economic indicators like income, access to services, education level, and so forth 44 . The consistent behaviour of Δ μ , Δ σ and Δ ϱ across different scales is indeed a very desirable property of segregation measures, as also pointed out by both classical and more recent works 45 , 46 .…”

“…In some extreme situations, a walker will take a considerably large amount of time to encounter a given ethnicity if there are only a handful of citizens belonging to it, and they are all concentrated in one node. Nevertheless, a similar approach has recently been shown to be particularly useful when dealing with coarse-grained ethnic data 44 , where the absence of rare ethnicities avoids the emergence of spurious effects on the statistics of the random walk. We believe that the choice we made avoids the many potential biases introduced by aggregating all the rare ethnicities in a small number of arbitrary classes, since these aggregations vary across countries and administrations.…”

COVID-19 Infections and Mortality in Florida Counties: Roles of Race, Ethnicity, Segregation, and 2020 Election Results

“…While the focus on a single state with Florida’s unique sociopolitical environs requires careful generalization, is also reduces distortions introduced by inter-state differences in case counting and definitions of “COVID-19-related deaths” [ 45 ]. This study uses dissimilarity segregation measures, though other dimensions such as clustering might yield different findings [ 46 ]. The fluctuation of the coefficient on population density between statistical significance and insignificance suggests an interaction effect with either political preference or segregation that might benefit from future study.…”

Metadata-informed community detection with lazy encoding using absorbing random walks

“….} of an unbiased random walk on G starting from node i 0 retains plentiful information about correlations and heterogeneity in the underlying distribution of metadata at different scales [20,21].…”