论文标题
使用最佳传输连接Covid-19动力学和人类移动性的聚类模式
Clustering patterns connecting COVID-19 dynamics and Human mobility using optimal transport
论文作者
论文摘要
众所周知，社会疏远和在家居住是有效地检查大流行（例如Covid-19）在给定人群中的传播的少数措施之一。这种措施及其对疾病发病率的影响之间的依赖模式可能会动态变化，并且在不同的人群中可能会有所不同。我们描述了一个新的计算框架，以衡量和比较在美国150多个城市中，人类流动性与共同案例的新案例之间的时间关系，该疾病的发生率相对较高。我们使用了最佳运输的新颖应用来计算每对城市由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10个具有相似时间依赖性的城市簇，并计算了Wasserstein Barycenter来描述每个群集的总体动态模式。最后，我们使用城市特异性的社会经济协变量来分析每个集群的组成。
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.
