Evidence map›Paper›PMID 34512107›Full record

ArticleTransactions in GIS : TG2021

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

Iván L Degano, Pablo A Lotito

Abstract read
In one paragraph

Article in Transactions in GIS : TG, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Iván L DeganoCEMIM, Facultad de Ciencias Exactas y Naturales UNMdP Mar del Plata Argentina.ORCID https://orcid.org/0000-0002-6557-7500
Pablo A LotitoPLADEMA, Facultad de Ciencias Exactas UNCPBA Tandil Argentina.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Identifiers

PMID34512107
PMCPMC8420548

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.