Tang Francesca, Feng Yang, Chiheb Hamza, Fan Jianqing
Department of Operations Research and Financial Engineering, Princeton University, Princeton, NJ.
Department of Biostatistics, New York University, New York City, NY.
ArXiv. 2021 Jan 6:arXiv:2101.02113v1.
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.
随着新冠疫情的加剧，我们运用频谱聚类和相关矩阵的新颖组合，对美国各县增长轨迹的性质进行了刻画。由于美国和世界其他地区正在经历严重的第二波感染，为各县确定增长类别并了解增长的决定因素的重要性日益凸显。随后，我们选择了在区分不同社区方面具有最高统计显著性的人口特征。最后，我们使用三个社会疏离分数，通过长短期记忆网络（LSTM）有效地预测了特定县的未来增长情况。这项全面的研究在非常微观的层面上，利用增长社区、人口因素和社会疏离表现，捕捉了各县病例增长的性质，以帮助政府机构利用已知信息，就将资源和资金投向哪些潜在县做出适当决策。
