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A machine learning and clustering-based approach for county-level COVID-19 analysis

Author

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  • Charles Nicholson
  • Lex Beattie
  • Matthew Beattie
  • Talayeh Razzaghi
  • Sixia Chen

Abstract

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.

Suggested Citation

  • Charles Nicholson & Lex Beattie & Matthew Beattie & Talayeh Razzaghi & Sixia Chen, 2022. "A machine learning and clustering-based approach for county-level COVID-19 analysis," PLOS ONE, Public Library of Science, vol. 17(4), pages 1-24, April.
    • Handle: RePEc:plo:pone00:0267558
    DOI: 10.1371/journal.pone.0267558
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    References listed on IDEAS

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    1. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
    2. Silva, Petrônio C.L. & Batista, Paulo V.C. & Lima, Hélder S. & Alves, Marcos A. & Guimarães, Frederico G. & Silva, Rodrigo C.P., 2020. "COVID-ABS: An agent-based model of COVID-19 epidemic to simulate health and economic effects of social distancing interventions," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    3. Xuewei Chen & Hongliang Chen, 2020. "Differences in Preventive Behaviors of COVID-19 between Urban and Rural Residents: Lessons Learned from A Cross-Sectional Study in China," IJERPH, MDPI, vol. 17(12), pages 1-14, June.
    4. Hari Vishal Lakhani & Sneha S. Pillai & Mishghan Zehra & Ishita Sharma & Komal Sodhi, 2020. "Systematic Review of Clinical Insights into Novel Coronavirus (CoVID-19) Pandemic: Persisting Challenges in U.S. Rural Population," IJERPH, MDPI, vol. 17(12), pages 1-14, June.
    5. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    6. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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