Clustering Patterns Connecting COVID-19 Dynamics and Human Mobility Using Optimal Transport.

Sankhya. Series B (2008) Pub Date : 2021-01-01 Epub Date: 2021-03-16 DOI:10.1007/s13571-021-00255-0
Frank Nielsen, Gautier Marti, Sumanta Ray, Saumyadipta Pyne
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Abstract

Social distancing and stay-at-home are among the few measures that are known to be effective in checking the spread of a pandemic such as COVID-19 in a given population. The patterns of dependency between such measures and their effects on disease incidence may vary dynamically and across different populations. We described a new computational framework to measure and compare the temporal relationships between human mobility and new cases of COVID-19 across more than 150 cities of the United States with relatively high incidence of the disease. We used a novel application of Optimal Transport for computing the distance between the normalized patterns induced by bivariate time series for each pair of cities. Thus, we identified 10 clusters of cities with similar temporal dependencies, and computed the Wasserstein barycenter to describe the overall dynamic pattern for each cluster. Finally, we used city-specific socioeconomic covariates to analyze the composition of each cluster.

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利用最优传输连接 COVID-19 动态和人类流动性的聚类模式。
社会疏远和足不出户是已知能有效阻止 COVID-19 等大流行病在特定人群中传播的少数措施之一。这些措施之间的依赖模式及其对疾病发病率的影响可能会因不同人群而动态变化。我们介绍了一种新的计算框架,用于测量和比较美国 150 多个 COVID-19 发病率相对较高的城市中人类流动性与新增病例之间的时间关系。我们采用了一种新颖的最优传输技术,用于计算每对城市的双变量时间序列引起的归一化模式之间的距离。因此,我们确定了具有相似时间依赖性的 10 个城市集群,并计算了瓦瑟斯坦原点,以描述每个集群的整体动态模式。最后,我们使用特定城市的社会经济协变量来分析每个集群的构成。
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