About: Geostatistical COVID-19 infection risk maps for Portugal

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Geostatistical COVID-19 infection risk maps for Portugal
Creator	Pereira, Maria Azevedo, Leonardo Ribeiro, Manuel Soares, Amílcar
source	Medline; PMC
abstract	The rapid spread of the SARS-CoV-2 epidemic has simultaneous time and space dynamics. This behaviour results from a complex combination of factors, including social ones, which lead to significant differences in the evolution of the spatiotemporal pattern between and within countries. Usually, spatial smoothing techniques are used to map health outcomes, and rarely uncertainty of the spatial predictions are assessed. As an alternative, we propose to apply direct block sequential simulation to model the spatial distribution of the COVID-19 infection risk in mainland Portugal. Given the daily number of infection data provided by the Portuguese Directorate-General for Health, the daily updates of infection rates are calculated by municipality and used as experimental data in the geostatistical simulation. The model considers the uncertainty/error associated with the size of each municipality’s population. The calculation of daily updates of the infection risk maps results from the median model of one ensemble of 100 geostatistical realizations of daily updates of the infection risk. The ensemble of geostatistical realizations is also used to calculate the associated spatial uncertainty of the spatial prediction using the interquartile distance. The risk maps are updated daily and show the regions with greater risks of infection and the critical dynamics related to its development over time.
has issue date	2020-07-06(xsd:dateTime)
bibo:doi	10.1186/s12942-020-00221-5
bibo:pmid	32631358
has license	cc-by
sha1sum (hex)	e230b8f299dc1d938b689257b0dfa66b08ff0f83
schema:url	https://doi.org/10.1186/s12942-020-00221-5
resource representing a document's title	Geostatistical COVID-19 infection risk maps for Portugal
has PubMed Central identifier	PMC7336093
has PubMed identifier	32631358
schema:publication	Int J Health Geogr
resource representing a document's body	covid:e230b8f299dc1d938b689257b0dfa66b08ff0f83#body_text
is schema:about of	named entity 'alternative' named entity 'geostatistical' named entity 'spatial' named entity 'number' named entity 'ensemble' named entity 'data' named entity 'COVID-19' named entity 'daily' named entity 'EPIDEMIC' named entity 'ITS' named entity 'RELATED' named entity 'TIME' named entity 'SIZE' named entity 'ASSOCIATED WITH' named entity 'CALCULATED' named entity 'USUALLY' named entity 'MAINLAND' named entity 'municipality' named entity 'prediction' named entity 'data' named entity 'spatial' named entity 'factors' named entity 'critical dynamics' named entity 'direct' named entity 'updates' named entity 'evolution' named entity 'infection' named entity 'uncertainty' named entity 'median' named entity 'size' named entity 'lead' named entity 'Portuguese' named entity 'development' named entity 'infection' named entity 'infection' named entity 'mainland Portugal' named entity 'infection' named entity 'COVID' named entity '19, 20' named entity 'covid -19' named entity 'infection' named entity 'Infection' named entity 'algorithm' named entity 'infection rate' named entity 'virus' named entity 'geostatistical' named entity 'uncorrelated' named entity 'kriging' named entity 'probability distribution function' named entity 'kriging' named entity 'regular grid' named entity 'infection' named entity 'infection' named entity 'functional data analysis' named entity 'Portugal' named entity 'choropleth maps' named entity 'variogram' named entity 'infection' named entity 'COVID-19' named entity 'data values' named entity 'random variable' named entity 'kriging' named entity 'infection' named entity 'visual perception' named entity 'Hubei Province' named entity 'kriging' named entity 'global pandemic' named entity 'human infection' named entity 'geostatistical' named entity 'Gotway' named entity 'spatial dispersion' named entity 'Geostatistical' named entity 'COVID-19' named entity 'linear regression' named entity 'high-risk' named entity 'infection' named entity 'COVID-19' named entity 'infection'

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