A machine learning and clustering-based approach for county-level COVID-19 analysis.
COVID-19 is a global pandemic threatening the lives and livelihood of millions of people across the world. Due to its novelty and quick spread, scientists have had difficulty in creating accurate forecasts for this disease. In part, this is due to variation in human behavior and environmental factor...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0267558&type=printable |
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| author | Charles Nicholson Lex Beattie Matthew Beattie Talayeh Razzaghi Sixia Chen |
| author_facet | Charles Nicholson Lex Beattie Matthew Beattie Talayeh Razzaghi Sixia Chen |
| author_sort | Charles Nicholson |
| collection | DOAJ |
| description | COVID-19 is a global pandemic threatening the lives and livelihood of millions of people across the world. Due to its novelty and quick spread, scientists have had difficulty in creating accurate forecasts for this disease. In part, this is due to variation in human behavior and environmental factors that impact disease propagation. This is especially true for regionally specific predictive models due to either limited case histories or other unique factors characterizing the region. This paper employs both supervised and unsupervised methods to identify the critical county-level demographic, mobility, weather, medical capacity, and health related county-level factors for studying COVID-19 propagation prior to the widespread availability of a vaccine. We use this feature subspace to aggregate counties into meaningful clusters to support more refined disease analysis efforts. |
| format | Article |
| id | doaj-art-005993da071c4157a38b2702a263db78 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-005993da071c4157a38b2702a263db782025-01-18T05:31:03ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-01174e026755810.1371/journal.pone.0267558A machine learning and clustering-based approach for county-level COVID-19 analysis.Charles NicholsonLex BeattieMatthew BeattieTalayeh RazzaghiSixia ChenCOVID-19 is a global pandemic threatening the lives and livelihood of millions of people across the world. Due to its novelty and quick spread, scientists have had difficulty in creating accurate forecasts for this disease. In part, this is due to variation in human behavior and environmental factors that impact disease propagation. This is especially true for regionally specific predictive models due to either limited case histories or other unique factors characterizing the region. This paper employs both supervised and unsupervised methods to identify the critical county-level demographic, mobility, weather, medical capacity, and health related county-level factors for studying COVID-19 propagation prior to the widespread availability of a vaccine. We use this feature subspace to aggregate counties into meaningful clusters to support more refined disease analysis efforts.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0267558&type=printable |
| spellingShingle | Charles Nicholson Lex Beattie Matthew Beattie Talayeh Razzaghi Sixia Chen A machine learning and clustering-based approach for county-level COVID-19 analysis. PLoS ONE |
| title | A machine learning and clustering-based approach for county-level COVID-19 analysis. |
| title_full | A machine learning and clustering-based approach for county-level COVID-19 analysis. |
| title_fullStr | A machine learning and clustering-based approach for county-level COVID-19 analysis. |
| title_full_unstemmed | A machine learning and clustering-based approach for county-level COVID-19 analysis. |
| title_short | A machine learning and clustering-based approach for county-level COVID-19 analysis. |
| title_sort | machine learning and clustering based approach for county level covid 19 analysis |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0267558&type=printable |
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