TY - GEN
T1 - COVID-19 and US Labor Force
T2 - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023
AU - Champion, Anissa
AU - Ojeda, Daniel
AU - Huang, Ching Yu
AU - Kwak, Daehan
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The COVID-19 pandemic has highlighted the crucial role of socio-economic factors in shaping health outcomes. Understanding the correlations between different societal attributes and how each one contributed to COVID-19 outcomes in the United States is essential. Therefore, this study utilized real-time reported COVID-19 data from a database known as SimpleMaps and integrated it with US county data to analyze the societal attribute, labor force, and its correlation with COVID-19 outcomes. This study reveals that the labor force has a correlation with COVID-19 cases and deaths. In addition to identifying correlations, the study creates visuals to help visualize the trends better. These findings and visuals can offer valuable insights for policymakers in implementing targeted strategies to mitigate the impact of COVID-19 on vulnerable populations and enhance the understanding of socio-economic factors' impact on COVID-19 outcomes.
AB - The COVID-19 pandemic has highlighted the crucial role of socio-economic factors in shaping health outcomes. Understanding the correlations between different societal attributes and how each one contributed to COVID-19 outcomes in the United States is essential. Therefore, this study utilized real-time reported COVID-19 data from a database known as SimpleMaps and integrated it with US county data to analyze the societal attribute, labor force, and its correlation with COVID-19 outcomes. This study reveals that the labor force has a correlation with COVID-19 cases and deaths. In addition to identifying correlations, the study creates visuals to help visualize the trends better. These findings and visuals can offer valuable insights for policymakers in implementing targeted strategies to mitigate the impact of COVID-19 on vulnerable populations and enhance the understanding of socio-economic factors' impact on COVID-19 outcomes.
KW - correlation analysis
KW - Covid-19
KW - labor force
KW - socio-economics factors
UR - http://www.scopus.com/inward/record.url?scp=85191189632&partnerID=8YFLogxK
U2 - 10.1109/CSCE60160.2023.00312
DO - 10.1109/CSCE60160.2023.00312
M3 - Conference contribution
AN - SCOPUS:85191189632
T3 - Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023
SP - 1891
EP - 1893
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 -