PDF(Pubmed)
Abstract：
The COVID-19 pandemic has had great impact on human health and social economy. Several studies examined spatial and temporal patterns of health risk factors associated with COVID-19, but population flow spillover effect has not been sufficiently considered. In this paper, a population flow-based spatial-temporal eigenvector filtering model (FLOW-ESTF) was developed to consider spatial-temporal patterns and population flow connectivity simultaneously. The proposed FLOW-ESTF method efficiently improved model prediction accuracy, which could help the government aware of the infection risk level and to make suitable control policies. The selected population flow spatial-temporal eigenvector contributed most to modeling and the visualization of corresponding eigenvector set helped to explore the underlying spatial-temporal patterns and pandemic transmission nodes. The model coefficients could reflect how health risk factors contribute the modeling of state-level COVID-19 weekly increased cases and how their influence changed through time, which could help people and government to better aware the potential health risks and to adjust control measures at different stage. The extracted population flow spatial-temporal eigenvector not only represents influence of population flow and its spillover effects but also represents some possible omitted health risk factors. This could provide an efficient path to solve the problem of spatial and temporal autocorrelation in COVID-19 modeling and an intuitive way to discover underlying spatial patterns, which will partially compensate for the problems of insufficient consideration of potential risk variables and missing data.
摘要：
COVID-19大流行对人类健康和社会经济产生了重大影响。一些研究检查了与COVID-19相关的健康风险因素的时空格局，但尚未充分考虑人口流动溢出效应。在本文中,开发了基于人口流的时空特征向量滤波模型（FLOW-ESTF），以同时考虑时空模式和人口流连通性。提出的FLOW-ESTF方法有效地提高了模型预测精度,这可以帮助政府了解感染风险水平并制定适当的控制政策。选定的人口流动时空特征向量对建模贡献最大，相应特征向量集的可视化有助于探索潜在的时空模式和大流行传播节点。模型系数可以反映健康风险因素如何有助于建立州级COVID-19每周增加病例的模型，以及它们的影响如何随时间变化，这可以帮助人们和政府更好地意识到潜在的健康风险，并在不同阶段调整控制措施。提取的人口流动时空特征向量不仅代表了人口流动的影响及其溢出效应，而且还代表了一些可能被忽略的健康风险因素。这可以为解决COVID-19建模中的空间和时间自相关问题提供有效的途径，并且可以直观地发现潜在的空间模式，这将部分弥补潜在风险变量考虑不足和数据缺失的问题。
