Author: Degano, Iván L.; Lotito, Pablo A.
Title: Analyzing spatial mobility patterns with timeâ€varying graphical lasso: Application to COVIDâ€19 spread Cord-id: wiyu7huu Document date: 2021_7_12
ID: wiyu7huu
Snippet: This work applies the timeâ€varying graphical lasso (TVGL) method, an extension of the traditional graphical lasso approach, to address learning timeâ€varying graphs from spatiotemporal measurements. Given georeferenced data, the TVGL method can estimate a timeâ€varying network where an edge represents a partial correlation between two nodes. To achieve this, we use a COVIDâ€19 data set from the Argentine province of Chaco. As an application, we use the estimated network to study the impact
Document: This work applies the timeâ€varying graphical lasso (TVGL) method, an extension of the traditional graphical lasso approach, to address learning timeâ€varying graphs from spatiotemporal measurements. Given georeferenced data, the TVGL method can estimate a timeâ€varying network where an edge represents a partial correlation between two nodes. To achieve this, we use a COVIDâ€19 data set from the Argentine province of Chaco. As an application, we use the estimated network to study the impact of COVIDâ€19 confinement measures and evaluate whether the measures produced the expected result.
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