Cluster Analysis of US States Based on COVID-19 Incidence, Fatality, and Vaccination

• Activity Number: 442 - Disease Prediction, Statistical Methods for Genetic Epidemiology and Mis
• Type: Contributed
• Date/Time: Thursday, August 12, 2021 : 4:00 PM to 5:50 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Jing Guo* and Faith Fatchen
• Companies: California State University, Chico and California State University, Chico
• Keywords: cluster analysis; principal component analysis; COVID-19

Abstract

The recent COVID-19 pandemic has caused the loss of many lives worldwide. In the US, as of April 10, 2021, there have been more than 30 million accumulative confirmed cases and more than 550,000 reported COVID-19 related fatalities. Existing published studies presented the clustering results based on incidence and mortality information at different levels, including country and county levels. With the availability and accessibility of the COVID-19 vaccines, it is essential to characterize the clustering features at the state-level using the information including the number of COVID-19 cases per 100 thousand in the last seven days, testing positivity rate in the last seven days, deaths per 100k in the last seven days as well as the vaccination rates. In the present study, we use principal component analysis to extract features from highly correlated data such as state-level incidence and mortality, as well as vaccination. We further performed hierarchical cluster analysis based on extracted features from state-level COVID-19 data. The presented cluster results could contribute to the pandemic management, including the policy-making process and health education program.