The Interplay of Demographic Variables and Social Distancing Scores in Deep Prediction of U.S. COVID-19 Cases
人口特征与社交距离指标在美国COVID-19病例深度预测中的作用AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确构建 LSTM 并评估四天前瞻的样本外预测，分类依据是预测方法，应用对象为县级疫情。
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人口特征与社交距离指标在美国COVID-19病例深度预测中的作用
- 研究问题
- 如何识别美国各县病例增长轨迹的群组差异，考察相关人口特征，并利用社交距离指标预测未来增长？
- 方法与思路
- 作者结合谱聚类与相关矩阵划分增长群组，筛选区分群组的人口特征，再以三项社交距离指标构建 LSTM 预测。
- 材料陈述的贡献
- 摘要称，该流程识别增长差异，最佳模型四天前瞻预测的样本外 R2 中位数为0.6251，群组数与距离特征影响预测准确性。
- 阅读关注点
- 应核对增长定义、聚类与特征筛选是否仅用训练信息，以及样本外划分、预测基准和不同县间的表现差异。
- 阅读边界
- 仅据 OpenAlex 索引摘要，稿件版本未注明；预测表现限于所述数据与评估方式，不能把人口特征或社交距离关联解释为因果效应。
原文摘要
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 United States and the rest of the world are still suffering from the effects of the virus, the importance of assigning growth membership to counties and understanding the determinants of the growth is increasingly evident. For the two communities (faster versus slower growth trajectories) we cluster the counties into, the average between-group correlation is 88.4% whereas the average within-group correlations are 95.0% and 93.8%. The average growth rate for one group is 0.1589 and 0.1704 for the other, further suggesting that our methodology captures meaningful differences between the nature of the growth across various counties. Subsequently, we select the demographic features that are most statistically significant in distinguishing the communities: number of grocery stores, number of bars, Asian population, White population, median household income, number of people with the bachelor’s degrees, and population density. Lastly, we effectively predict the future growth of a given county with a long short-term memory (LSTM) recurrent neural network using three social distancing scores. The best-performing model achieves a median out-of-sample R2 of 0.6251 for a four-day ahead prediction and we find that the number of communities and social distancing features play an important role in producing a more accurate forecasting. This comprehensive study captures the nature of the 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. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.