美国县级COVID-19活动的高分辨率时空模型

Shixiang Zhu, Alexander W. Bukharin, Liyan Xie, M. Santillana, Shihao Yang, Yao Xie
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引用次数: 14

摘要

我们提出了一个可解释的高分辨率时空模型,用于估计美国县一级和每周汇总的COVID-19死亡人数以及当前时间前一周的确诊病例。我们的时空模型的一个显著特征是,它考虑了(1)两个本地时间序列(COVID-19确诊病例和死亡病例)的时间自相关性和两两相关性,(2)地点之间的相关性(县之间的传播),以及(3)协变量,如当地社区内流动性和社会人口因素。社区内流动性和人口因素,如总人口和老年人比例,被视为重要的预测因素,因为它们被假设为确定COVID-19动态的重要因素。为了降低模型的高维,我们将稀疏结构作为约束,并强调全国前10大城市的影响,我们将其称为(并在我们的模型中处理)疾病传播的中心。我们的回顾性样本外县级预测能够准确预测随后观察到的COVID-19活动。所提出的多变量预测模型具有高度的可解释性,能够明确识别和量化决定COVID-19动态的最重要因素。正在进行的工作包括纳入更多的协变量,如教育和收入,以提高预测的准确性和模型的可解释性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
High-Resolution Spatio-Temporal Model for County-Level COVID-19 Activity in the U.S
We present an interpretable high-resolution spatio-temporal model to estimate COVID-19 deaths together with confirmed cases 1 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 (1) temporal auto- and pairwise correlation of the two local time series (confirmed cases and deaths from the COVID-19), (2) correlation between locations (propagation between counties), and (3) 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 10 metropolitan areas in the nation, which we refer to (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 multivariate 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.
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