This work applies the time-varying graphical lasso (TVGL) method, an extension of the traditional graphical lasso approach, to address learning time-varying graphs from spatiotemporal measurements. Given georeferenced data, the TVGL method can estimate a time-varying network where an edge represents a partial correlation between two nodes. To achieve this, we use a COVID-19 data set from the Argentine province of Chaco. As an application, we use the estimated network to study the impact of COVID-19 confinement measures and evaluate whether the measures produced the expected result.

中文翻译：

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使用时变图形套索分析空间移动模式：在 COVID-19 传播中的应用

这项工作应用时变图形套索 (TVGL) 方法（传统图形套索方法的扩展）来解决从时空测量中学习时变图的问题。给定地理参考数据，TVGL 方法可以估计一个时变网络，其中一条边表示两个节点之间的部分相关性。为此，我们使用了来自阿根廷查科省的 COVID-19 数据集。作为一个应用程序，我们使用估计的网络来研究 COVID-19 限制措施的影响，并评估这些措施是否产生了预期的结果。