Analyzing spatial mobility patterns with time-varying graphical lasso: Application to COVID-19 spread.

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.