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 f...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
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Elsevier BV
2020
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| Online Access: | https://hdl.handle.net/1721.1/127198 |
| _version_ | 1826210514971131904 |
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| author | Lai, Yuan Charpignon, Marie-Laure Ebner, Daniel K. Celi, Leo Anthony G. |
| author2 | Massachusetts Institute of Technology. Department of Urban Studies and Planning |
| author_facet | Massachusetts Institute of Technology. Department of Urban Studies and Planning Lai, Yuan Charpignon, Marie-Laure Ebner, Daniel K. Celi, Leo Anthony G. |
| author_sort | Lai, Yuan |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T14:51:12Z |
| format | Article |
| id | mit-1721.1/127198 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T14:51:12Z |
| publishDate | 2020 |
| publisher | Elsevier BV |
| record_format | dspace |
| spelling | mit-1721.1/1271982022-09-29T10:59:27Z Unsupervised learning for county-level typological classification for COVID-19 research Lai, Yuan Charpignon, Marie-Laure Ebner, Daniel K. Celi, Leo Anthony G. 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 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. National Institutes of Health (Grant R01 EB017205) 2020-09-08T15:48:34Z 2020-09-08T15:48:34Z 2020-08 2020-08 Article http://purl.org/eprint/type/JournalArticle 2666-5212 https://hdl.handle.net/1721.1/127198 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 http://dx.doi.org/10.1016/j.ibmed.2020.100002 Intelligence-Based Medicine Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier BV Elsevier |
| spellingShingle | Lai, Yuan Charpignon, Marie-Laure Ebner, Daniel K. Celi, Leo Anthony G. Unsupervised learning for county-level typological classification for COVID-19 research |
| title | Unsupervised learning for county-level typological classification for COVID-19 research |
| title_full | Unsupervised learning for county-level typological classification for COVID-19 research |
| title_fullStr | Unsupervised learning for county-level typological classification for COVID-19 research |
| title_full_unstemmed | Unsupervised learning for county-level typological classification for COVID-19 research |
| title_short | Unsupervised learning for county-level typological classification for COVID-19 research |
| title_sort | unsupervised learning for county level typological classification for covid 19 research |
| url | https://hdl.handle.net/1721.1/127198 |
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