Unsupervised learning for county-level typological classification for COVID-19 research.
Intell Based Med
; 1: 100002, 2020 Nov.
Article
in English
| MEDLINE | ID: covidwho-733824
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Experimental Studies
Language:
English
Journal:
Intell Based Med
Year:
2020
Document Type:
Article
Affiliation country:
J.ibmed.2020.100002
Similar
MEDLINE
...
LILACS
LIS