TY - GEN
T1 - Analyzing COVID-19 Impact in the US
T2 - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023
AU - Ojeda, Daniel
AU - Champion, Anissa
AU - Huang, Ching Yu
AU - Kwak, Daehan
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The COVID-19 pandemic has had a great impact on the world, with the United States experiencing a disproportionately high burden in terms of infections and deaths compared to other nations. The virus has disrupted daily life across the country and highlighted longstanding health inequalities. Therefore, this study aims to analyze the correlation between total COVID-19 cases and deaths with various demographic variables such as median age, education level, race, ethnicity, income, unemployment rate, disability status, and insurance coverage. The goal is to identify groups of people who are disproportionately affected by the COVID-19 virus and the factors that contribute to this inequality. To achieve this, the study collects data from all 50 states in the United States, using authoritative sources such as the U.S. Census Bureau and the Bureau of Labor. Then, ETL process (Extract, Transform, and Load) is performed to obtain clean and refined datasets and compile them into a final table for correlation analysis. With the help of this study, public health officials and policymakers could initiate the development of targeted interventions to ensure that everybody has the same opportunity to achieve good health, regardless of their demographic, economic, and social status.
AB - The COVID-19 pandemic has had a great impact on the world, with the United States experiencing a disproportionately high burden in terms of infections and deaths compared to other nations. The virus has disrupted daily life across the country and highlighted longstanding health inequalities. Therefore, this study aims to analyze the correlation between total COVID-19 cases and deaths with various demographic variables such as median age, education level, race, ethnicity, income, unemployment rate, disability status, and insurance coverage. The goal is to identify groups of people who are disproportionately affected by the COVID-19 virus and the factors that contribute to this inequality. To achieve this, the study collects data from all 50 states in the United States, using authoritative sources such as the U.S. Census Bureau and the Bureau of Labor. Then, ETL process (Extract, Transform, and Load) is performed to obtain clean and refined datasets and compile them into a final table for correlation analysis. With the help of this study, public health officials and policymakers could initiate the development of targeted interventions to ensure that everybody has the same opportunity to achieve good health, regardless of their demographic, economic, and social status.
KW - Correlation Analysis
KW - Covid-19
KW - Data Mining
KW - Data Visualization
KW - Health Disparities
UR - http://www.scopus.com/inward/record.url?scp=85191166397&partnerID=8YFLogxK
U2 - 10.1109/CSCE60160.2023.00242
DO - 10.1109/CSCE60160.2023.00242
M3 - Conference contribution
AN - SCOPUS:85191166397
T3 - Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023
SP - 1462
EP - 1468
BT - Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 24 July 2023 through 27 July 2023
ER -
