Unsupervised learning for county-level typological classification for COVID-19 research
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1-s2.0-S2666521220300028-main.pdf
Size
1.86 MB
Format
Adobe PDF
Checksum (MD5)
51f152e90c8fe0ee2e07d9ba8abae187
Author(s)
Lai, Yuan
Charpignon, Marie-Laure
Ebner, Daniel K.
Celi, Leo Anthony G.
Date Issued
August 2020
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
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.
MIT 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
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.ibmed.2020.100002
