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

Author(s)
Lai, Yuan; Charpignon, Marie-Laure; Ebner, Daniel K.; Celi, Leo Anthony G.
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Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/
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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.
Date issued
2020-08
URI
https://hdl.handle.net/1721.1/127198
Department
Massachusetts Institute of Technology. Department of Urban Studies and Planning; Massachusetts Institute of Technology. Institute for Data, Systems, and Society; Massachusetts Institute of Technology. Institute for Medical Engineering & Science; Harvard--MIT Program in Health Sciences and Technology. Laboratory for Computational Physiology
Journal
Intelligence-Based Medicine
Publisher
Elsevier BV
Citation
Lai, Yuan et al. "Unsupervised learning for county-level typological classification for COVID-19 research." Forthcoming in Intelligence-Based Medicine 1-2 (November 2020): 100002
Version: Final published version
ISSN
2666-5212
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