id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-287027-ahoo6j3o	Lai, Yuan	Unsupervised Learning for County-Level Typological Classification for COVID-19 Research	2020-08-30		.txt	text/plain	3462	208	49	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. The purpose of this study is to summarize publicly available and relevant COVID-19 data sources, to address the benchmarking challenge from the data heterogeneity through clustering, and to classify counties J o u r n a l P r e -p r o o f based on their underlying variations. Particularly at the county-level, previous studies have implemented clustering techniques to analyze various data sources relating J o u r n a l P r e -p r o o f to demographic, geographic, environment, and socioeconomic determinants of health and disease. While previous findings reveal possible geographical clusters of COVID-19 cases at the county-level, our study indicates this is from the underlying typology based on high-dimensional variables.	./cache/cord-287027-ahoo6j3o.txt	./txt/cord-287027-ahoo6j3o.txt