Evidence map›Paper›PMID 34711874›Full record

ArticleScientific reports2021

Risk evaluation at municipality level of a COVID-19 outbreak incorporating relevant geographic data: the study case of Galicia.

Alejandro Carballosa, José Balsa-Barreiro, Adrián Garea, David García-Selfa, Ángel Miramontes, Alberto P Muñuzuri

Abstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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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

6 authors.

Alejandro CarballosaGroup of Nonlinear Physics, Faculty of Physics, University of Santiago de Compostela, 15782, Santiago de Compostela, Spain.
José Balsa-BarreiroGroup of Territorial Analysis, Institute IDEGA, University of Santiago de Compostela, Santiago de Compostela, Spain.
Adrián GareaGroup of Nonlinear Physics, Faculty of Physics, University of Santiago de Compostela, 15782, Santiago de Compostela, Spain.
David García-SelfaGroup of Nonlinear Physics, Faculty of Physics, University of Santiago de Compostela, 15782, Santiago de Compostela, Spain.
Ángel MiramontesGroup of Territorial Analysis, Institute IDEGA, University of Santiago de Compostela, Santiago de Compostela, Spain.
Alberto P MuñuzuriGroup of Nonlinear Physics, Faculty of Physics, University of Santiago de Compostela, 15782, Santiago de Compostela, Spain. alberto.perez.munuzuri@usc.es.

Funding

CRETUS Strategic Partnership AGRUP2015/02Ministerio de Economía y Competitividad COV20/00617Xunta de Galicia 2021-PG036
6 · The paper itself

Abstract

The COVID-19 pandemic was an inevitable outcome of a globalized world in which a highly infective disease is able to reach every country in a matter of weeks. While lockdowns and strong mobility restrictions have proven to be efficient to contain the exponential transmission of the virus, its pervasiveness has made it impossible for economies to maintain this kind of measures in time. Understanding precisely how the spread of the virus occurs from a territorial perspective is crucial not only to prevent further infections but also to help with policy design regarding human mobility. From the large spatial differences in the behavior of the virus spread we can unveil which areas have been more vulnerable to it and why, and with this information try to assess the risk that each community has to suffer a future outbreak of infection. In this work we have analyzed the geographical distribution of the cumulative incidence during the first wave of the pandemic in the region of Galicia (north western part of Spain), and developed a mathematical approach that assigns a risk factor for each of the different municipalities that compose the region. This risk factor is independent of the actual evolution of the pandemic and incorporates geographic and demographic information. The comparison with empirical information from the first pandemic wave demonstrates the validity of the method. Our results can potentially be used to design appropriate preventive policies that help to contain the virus.

Indexed as

PandemicsSARS-CoV-2Computer SimulationCOVID-19DemographyHumansIncidenceLinear ModelsModels, StatisticalRisk FactorsSpain

Identifiers

PMID34711874
PMCPMC8553869

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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.