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

Each network corresponds to a metropolitan area, where nodes represent census tracts. For each tract we report information about the number of people belonging to each of the seven high-level ethnic groups defined in the US Census. Physical adjacency networks are undirected and unweighted, and an edge between two tracts A and B indicates that A and B are bordering each other. Commuting flow graphs are undirected and weighted, and the weight of an the edge between A and B corresponds to average of the total number of work commuting trips from A to B and from B to A.

Methods
- Covid Data

The incidence of covid cases and deceased African Americans data was obtained from [1] and the data was used at its original state. The detailed temporal data of Covid cases considered on the multivariate analysis was obtained from [2].

- Adjacency networks

The ethnicity data associated with the node properties were obtained from the NHGIS [3] website where the dataset was split into individual files for each Combined Statistical Area (CSA) according to the column denominated "CSAA". The adjacencies were obtained from the shapefiles with areal units delineations at the census tract level obtained from [3]. With the list of census tracts within a CSA, we looked for the neighbours of each tract if they shared a border and constructed the subgraph of the largest component so that the resulting edge-list and node properties include only the elements of the induced subgraph.

- Commuting networks

The commuting graphs are weighted networks obtained from the information of where people live and work [4]. In particular, the weight of a link going from a node i to a node j is given by the sum of the individuals living in i and working in j and those working in j and living in i. In this case, the population of an ethnicity is the sum of those individuals living in a cell plus the weight of the incoming link times the residents of the same ethnicity in the origin.

- Code

The reader interested in replicating the methods used to create the data can obtain the python scrips in the following repository:

https://mygit.katolaz.net/covid_19_ethnicity/rw-segregation

Note that the repository also includes the code to simulate the CCT and MFPT random walks on the adjacency and commute graphs so that the whole simulation can be replicated.

- References:

[1] Black Population in US — BlackDemographics.com; 2016. [Online; accessed 2020-05-30]. https://blackdemographics.com/black-covid-19-tracker/.

[2] COVID Racial Data Tracker; 2016. [Online; accessed 2020-05-30]. https://covidtracking.com/race.

[3] Manson S, Schroeder J, Riper DV, Ruggles S. IPUMS National Historical Geographic Information System: Version 14.0 [Database]. Minneapolis, MN: IPUMS. 2019. http://doi.org/10.18128/D050.V14.0.

[4] Longitudinal Employer-Household Dynamics; 2016. Online; accessed 2020-05-30. https://lehd.ces.census.gov/.

[5] Bureau UC. American Community Survey 2014-2018 5-Year Data Release. US Govern- ment; 2019. Available from: https://www.census.gov/newsroom/press-kits/2019/acs-5-year.html.