This project will use national and local incidence, mortality and testing data, with GIS driven agent-based disease modelling to identify the ?neighborhood effects? that drive the uneven patterns of transmission and serious outcomes from COVID-19. Neighborhood environments have been associated with chronic disease mortality, disability, cumulative stress, cognitive decline, loss of physical functioning. These same neighborhood effects are implicit in exacerbating disparities in the spread and health outcomes of COVID-19, however the exact mechanisms and the magnitude of the impact of entrenched social disparities specifically on COVID-19 outcomes are not yet known. The key hypothesis is that agent based disease spread modeling of the existing retrospective COVID-19 data sources at the national and local levels with geospatial data input and spatial-temporal analysis will provide powerful knowledge on the structural factors that influenced the pandemic?s spread in local areas and will facilitate the development of valuable recommendations on how to mitigate current and future disparities in impacts of COVID-19 and other future infectious epidemics and pandemics. We will test our key hypothesis and accomplish our objectives via the following Specific Aims. Determine what Counties factors related to the social determinants of health have influenced the spread of coronavirus in the United States. 1) Determine, within the identified Counties, which social determinants have had significant impact on either spread or inhibit spread of CV-19. 2) Determine which populations groups have been impacted more severely in these local contexts and why? 3) Determine what specific recommendations can be made from these findings? The expected outcomes of this research are published manuscripts and scholarly presentations that provide science- based recommendations on the how health authorities and policy makers can proactively address social factors that amplify disease and poor health outcomes in certain communities.
This project will used spatial epidemiologic methods to model how COVID-19 spread unevenly through metropolitan areas and identify the neighborhood factors that led to urban minorities being disproportionately impacted by the pandemic.
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