Colors of COVID

This was a special funding opportunity meant to quickly respond to the beginning of the pandemic and is now closed.

While the Center for the Study of Race, Ethnicity & Equity (CRE²) had already designed one of its core areas of inquiry around the intersections of race, ethnicity, public health, and medicine, we launched a deeper focus into the set of issues raised by the novel coronavirus 2019 (COVID-19). The COVID-19 pandemic made more urgent the need for robust cross-disciplinary and collaborative research on these links, also foregrounding issues of class, workforce, and community networks.

CRE² invited proposals for transdisciplinary projects that would generate new research frameworks and methodologies that capture the challenges and insights revealed by the racially and ethnically disparate impacts of COVID-19. These Special Focus Grants accompanied our regular grants program focused on transdisciplinary projects that enrich scholarly engagement related to the Center’s mission. The Special Focus Grants required the production of specific deliverables. They spanned the medical and public health fields as well as the social sciences, humanistic social sciences, humanities, engineering sciences, and arts, architecture, and design. Below were the recipients of the Colors of COVID Special Focus Grant.

Colors of COVID Projects

Housing Instability Post-COVID: Combining Community and Data Sciences to Disrupt Racial/Ethnic Segregation in St. Louis

The study proposed a cross campus collaboration with deep connections into the St.  Louis community that will map posthaste COVID housing instability and oracle racial and ethnic housing segregation. Community coordinated responses to address participatory action research method based system dynamics will engage local decision makers and families in the identification of systematic housing disparities as well as the design of coordinated responses that intervene on racial and ethnic discrimination. Data science informed efforts by visualizing disparate rates of housing instability (need for assistance, eviction filings, and executions, and homelessness) in real-time, while machine learning will predict neighborhoods at greatest risk for housing insecurity.

The investigative team included faculty collaborators and graduate students that span the Brown School and Computer Science & Engineering as well as engages St.  Louis regional leaders responsible for planning and implementing coordinated responses to COVID-19.  The innovative study developed a replicable method for co-creating actionable community- and data driven responses to historical racism in the context of the COVID pandemic.

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Mapping Spatiotemporal Variation of Racial/ethnic Disparities in COVID

In the United States, coronavirus disease 2019 (COVID-19) is disproportionately affecting the racial/ethnic minority population. It remains unknown about spatiotemporal patterns of the racial/ethnic disparities in COVID-19 risks. Leveraging varied publicly-available data sources from multiple COVID-19 tracking systems, the Departments of Health, American Community Surveys, Small Area Health Insurance Estimates, and Behavioral Risk Factor Surveillance System (BRFSS), we performed a series of spatial statistical and geographic information system (GIS) analyses to examine small-area spatiotemporal variation and racial/ethnic disparities in COVID-19, identified associated neighborhood characteristics, and assessed the impacts of policy interventions across the country.

First, we computed the county-based rates of multiple COVID-19 outcomes, detected weekly hotspots using the Spatial and Space-Time Scan Statistics, and further characterized the trends of transmission, incidence, and mortality of COVID-19 by demographic factors (including the county-level percentage of racial/ethnic minority population, rural-urban context, population density, percentage of the population 65 years or older, and percentage of the population uninsured), socioeconomic deprivation, and neighborhood vulnerability (accounting for county-level percentage of the pre-existing comorbidities of the population, geographic allocation of the intensive care unit (ICU) beds, and geographic accessibility to infectious disease specialties). Second, using a spatial statistical modeling approach and weighted nonlinear regression, we will further assessed the impacts of COVID-19 related public policies, including the reopening and reclosing plans and preventative strategies (for example, mask requirements) on the COVID-19 pandemic across the country and the majority-minority counties, respectively.

This project sought to 1) to identify the high-risk counties for COVID-19 and assess the association of county-level race/ethnicity profile and other area characteristics with multiple COVID-19 outcomes, including daily testing capacity, incidence, mortality, etc.; and 2) to evaluate the effectiveness of the reopening and reclosing plans and preventative strategies especially for majority-minority counties. Along with the challenge to create a national COVID-19 patient-level database, we sought be among the first to utilize the publicly-available small-area-level data to assess and map racial/ethnic disparities in COVID-19 and inform the policymakers to improve the control and prevention of COVID-19.