2020/07/08 by Aleix Bassolas, Bassolas, Aleix, Sandro Sousa +3
Mathematics · Medicine · Social Sciences · #COVID-19 epidemiological studies #Data-Driven Disease Surveillance #Urban, Neighborhood, and Segregation Studies
paper · pdf · doi:10.48550/arxiv.2007.04130
Socio-economic disparities quite often have a central role in the unfolding\nof large-scale catastrophic events. One of the most concerning aspects of the\nongoing COVID-19 pandemics is that it disproportionately affects people from\nBlack and African American backgrounds creating an unexpected infection gap.\nInterestingly, the abnormal impact on these ethnic groups seem to be almost\nuncorrelated with other risk factors, including co-morbidity, poverty, level of\neducation, access to healthcare, residential segregation, and response to\ncures. A proposed explanation for the observed incidence gap is that people\nfrom African American backgrounds are more often employed in low-income service\njobs, and are thus more exposed to infection through face-to-face contacts, but\nthe lack of direct data has not allowed to draw strong conclusions in this\nsense so far. Here we introduce the concept of dynamic segregation, that is the\nextent to which a given group of people is internally clustered or exposed to\nother groups, as a result of mobility and commuting habits. By analysing census\nand mobility data on more than 120 major US cities, we found that the dynamic\nsegregation of African American communities is significantly associated with\nthe weekly excess COVID-19 incidence and mortality in those communities. The\nresults confirm that knowing where people commute to, rather than where they\nlive, is much more relevant for disease modelling.\n