Journal Article
Unsupervised learning for county-level typological classification for COVID-19 research

The analysis of county-level COVID-19 pandemic data faces computational and analytic challenges, particularly when considering the heterogeneity of data sources with variation in geographic, demographic, and socioeconomic factors between counties. This study presents a method to join relevant data from different sources to investigate underlying typological effects and disparities across typologies. Both consistencies within and variations between urban and non-urban counties are demonstrated. When different county types were stratified by age group distribution, this method identifies significant community mobility differences occurring before, during, and after the shutdown. Counties with a larger proportion of young adults (age 20–24) have higher baseline mobility and had the least mobility reduction during the lockdown.

Title
Publication TypeJournal Article
Year of Publication2020
AuthorsLai Y, Charpignon M-L, Ebner DK, Celi LA
JournalIntelligence-Based Medicine
Volume1
Issue2
Pagination100002
Date Published11/2020
Type of ArticleResearch article
Keywordsdata science, Machine learning, public health, Urban science
Abstract

The analysis of county-level COVID-19 pandemic data faces computational and analytic challenges, particularly when considering the heterogeneity of data sources with variation in geographic, demographic, and socioeconomic factors between counties. This study presents a method to join relevant data from different sources to investigate underlying typological effects and disparities across typologies. Both consistencies within and variations between urban and non-urban counties are demonstrated. When different county types were stratified by age group distribution, this method identifies significant community mobility differences occurring before, during, and after the shutdown. Counties with a larger proportion of young adults (age 20–24) have higher baseline mobility and had the least mobility reduction during the lockdown.

URLhttps://www.sciencedirect.com/science/article/pii/S2666521220300028
DOI10.1016/j.ibmed.2020.100002