[论文解读] High-resolution Spatio-temporal Model for County-level COVID-19 Activity in the U.S

Shixiang Zhu, Alexander Bukharin+4 more|arXiv (Cornell University)|Sep 15, 2020

COVID-19 epidemiological studies参考文献 41被引用 8

一句话总结

本文提出了一种高分辨率时空模型，用于提前一周预测美国各县的每周COVID-19确诊病例和死亡人数，利用时间相关性、县之间的空间传播以及关键协变量（如出行流动性和人口统计学因素）。该模型实现了准确的样本外预测，并识别出主要大都市枢纽和出行模式为传播的关键驱动因素。

ABSTRACT

We present an interpretable high-resolution spatio-temporal model to estimate COVID-19 deaths together with confirmed cases one-week ahead of the current time, at the county-level and weekly aggregated, in the United States. A notable feature of our spatio-temporal model is that it considers the (a) temporal auto- and pairwise correlation of the two local time series (confirmed cases and death of the COVID-19), (b) dynamics between locations (propagation between counties), and (c) covariates such as local within-community mobility and social demographic factors. The within-community mobility and demographic factors, such as total population and the proportion of the elderly, are included as important predictors since they are hypothesized to be important in determining the dynamics of COVID-19. To reduce the model's high-dimensionality, we impose sparsity structures as constraints and emphasize the impact of the top ten metropolitan areas in the nation, which we refer (and treat within our models) as hubs in spreading the disease. Our retrospective out-of-sample county-level predictions were able to forecast the subsequently observed COVID-19 activity accurately. The proposed multi-variate predictive models were designed to be highly interpretable, with clear identification and quantification of the most important factors that determine the dynamics of COVID-19. Ongoing work involves incorporating more covariates, such as education and income, to improve prediction accuracy and model interpretability.

研究动机与目标

• 开发一种高分辨率、可解释的时空模型，用于预测美国各县的COVID-19活动情况。
• 整合确诊病例与死亡人数之间的时序自相关和互相关关系。
• 建模来自邻近县和全国主要大都市枢纽的空间传播效应。
• 整合本地协变量，如社区出行流动性与人口统计因素（例如老年人口比例、总人口）。
• 通过强调稀疏性和顶级城市枢纽，提升预测准确性和可解释性。

提出的方法

• 使用带时间滞后响应的多变量线性模型，以捕捉确诊病例和死亡人数的时序动态。
• 通过将每个县与相邻县及十个全国主要大都市枢纽关联的系数，引入空间依赖性。
• 应用稀疏性约束以降低模型维度并增强可解释性。
• 对出行（工作场所、娱乐、杂货、公园、公共交通、住宅）和人口统计因素（总人口、老年人口比例）等协变量进行标准化。
• 采用对数线性泊松回归框架，以确保预测值非负，并处理计数数据的不确定性。
• 使用t检验和p值评估协变量系数的统计显著性。

实验结果

研究问题

• RQ1 如何利用高分辨率时空建模，准确预测美国各县提前一周的COVID-19确诊病例和死亡人数？
• RQ2 哪些本地和区域因素——尤其是出行流动性和人口统计学因素——对县一级的COVID-19传播影响最大？
• RQ3 主要大都市枢纽如何促进美国范围内COVID-19的空间传播？
• RQ4 确诊病例与死亡人数之间的时序相关性在多大程度上提升了预测准确性？
• RQ5 稀疏性约束和针对性的枢纽建模是否能在不牺牲预测性能的前提下增强模型的可解释性？

主要发现

• 该模型在确诊病例和死亡人数的县一级实现了准确的回顾性样本外预测。
• 工作场所和娱乐出行流动性与病例增加呈显著正相关，系数分别为+9.67e+2和+1.67e+3。
• 住宅出行流动性对病例增长有显著负面影响（系数：-5.08e+3），表明减少移动可能减缓传播。
• 对于死亡人数，杂货店和药店出行流动性具有显著正向影响（系数：+4.77e+3），表明在必需零售场所存在更高的传播风险。
• 老年人口比例与死亡率显著相关（系数：-1.17e+3，p值：6.85e-5），但与病例数无关。
• 总人口系数在病例预测中最大（系数：+2.91e+4），证实人口密度是疾病传播的主导因素。