The Interplay of Demographic Variables and Social Distancing Scores in Deep Prediction of U.S. COVID-19 Cases
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Abstract (arXiv)
With the severity of the COVID-19 outbreak, we characterize the nature of the growth trajectories of counties in the United States using a novel combination of spectral clustering and the correlation matrix. As the U.S. and the rest of the world are experiencing a severe second wave of infections, the importance of assigning growth membership to counties and understanding the determinants of the growth are increasingly evident. Subsequently, we select the demographic features that are most statistically significant in distinguishing the communities. Lastly, we effectively predict the future growth of a given county with an LSTM using three social distancing scores. This comprehensive study captures the nature of counties' growth in cases at a very micro-level using growth communities, demographic factors, and social distancing performance to help government agencies utilize known information to make appropriate decisions regarding which potential counties to target resources and funding to.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 5 of its 50 references that are in the map. First posted on arXiv 6 Jan 2021.
Builds on
5 of 50 references.
References · 5
- 2017Community Detection and Stochastic Block Modelscited by 319
- 2020COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Imagescited by 264
- 2013Consistency of spectral clustering in stochastic block modelscited by 129
- 2020Spectral Methods for Data Science: A Statistical Perspectivecited by 112
- 2019Estimating Number of Factors by Adjusted Eigenvalues Thresholdingcited by 14
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Citing papers per year: 2022: 2.
2 papers.