This project presents a comprehensive quantitative analysis of the impact of the COVID-19 pandemic on the United States, highlighting the exacerbation of social inequalities. Conducted at a county level, the study employs spatial analysis techniques to explore the relationships between social vulnerabilities, racial and economic disparities, and the incidence of COVID-19.
The COVID-19 pandemic has spotlighted and intensified existing social vulnerabilities within the United States, revealing a pressing need for social change. This research utilizes county-level data to examine the disproportionate effects of the pandemic on vulnerable populations, employing global and local regression models for a spatial analysis that answers critical questions about the distribution and magnitude of these impacts.
Crises like the COVID-19 pandemic unveil the societal rifts that lie beneath the surface, disproportionately affecting marginalized communities. This study investigates the 'why,' 'where,' and 'how much' of these disparities, aiming to quantify the varying impacts across different communities and locations within the US.
The research methodology integrates various data analytics techniques, including spatial visualization, regression analysis, and geographically weighted regression (GWR), to assess the influence of social vulnerabilities on COVID-19 death rates across US counties. The study considers factors such as socioeconomic status, racial minority status, community resilience, political affiliation, health determinants, and government actions.
Data was sourced from reputable institutions and databases, focusing on 14 variables across six thematic areas: Socioeconomic Status, Racial Minority, Lack of Community Resilience, Conservatism, Health Determinants, and Government Action.
The study proposes an expected regression equation to model the cumulative COVID-19 deaths per 100,000 people in relation to the identified variables.
Spatial analysis played a crucial role in identifying areas of health inequity and the disproportionate impact of the pandemic. This included bivariate and hotspot analysis, global regression models (MLR and SER), and localized models using GWR.
Findings confirm that social vulnerabilities significantly contribute to the increased incidence of COVID-19, with certain communities and locations experiencing more severe impacts. The analysis reveals that disparities in healthcare access, vaccination rates, and socioeconomic conditions are closely linked to higher COVID-19 death rates.
The research highlights the critical need for targeted interventions and policy measures to address the underlying social vulnerabilities exacerbated by the pandemic. It also underscores the importance of localized responses to effectively mitigate the impact on the most affected communities.
Gratitude is extended to the academic advisors, family members, friends, and institutions that supported this research. Special thanks to Prof. Linda Shi, Prof. Stephan Schmidt, and Prof. Linda K. Nozick for their invaluable guidance.
For a detailed exploration of the study, findings, and methodologies, please refer to the Full Research Paper located in this repository.