Statistical methods for bivariate spatial analysis in marked points. Examples in spatial epidemiology.
Identifieur interne : 000361 ( Main/Exploration ); précédent : 000360; suivant : 000362Statistical methods for bivariate spatial analysis in marked points. Examples in spatial epidemiology.
Auteurs : Marc Souris [Thaïlande] ; Laurence BichaudSource :
- Spatial and spatio-temporal epidemiology [ 1877-5853 ] ; 2011.
Descripteurs français
- KwdFr :
- Algorithmes (MeSH), Analyse spatio-temporelle (MeSH), Animaux (MeSH), France (épidémiologie), Grippe chez les oiseaux (mortalité), Grippe chez les oiseaux (virologie), Grippe chez les oiseaux (épidémiologie), Humains (MeSH), Immunoglobuline G (sang), Informatique mathématique (MeSH), Leishmania (classification), Leishmania (immunologie), Leishmania (isolement et purification), Leishmaniose (parasitologie), Leishmaniose (sang), Leishmaniose (épidémiologie), Loi du khi-deux (MeSH), Marqueurs biologiques (sang), Méthode de Monte Carlo (MeSH), Sous-type H5N1 du virus de la grippe A (isolement et purification), Systèmes d'information géographique (statistiques et données numériques), Thaïlande (épidémiologie), Volaille (MeSH), Études par échantillonnage (MeSH).
- MESH :
- immunologie : Leishmania.
- isolement et purification : Leishmania, Sous-type H5N1 du virus de la grippe A.
- mortalité : Grippe chez les oiseaux.
- parasitologie : Leishmaniose.
- sang : Immunoglobuline G, Leishmania, Leishmaniose, Marqueurs biologiques.
- statistiques et données numériques : Systèmes d'information géographique.
- virologie : Grippe chez les oiseaux.
- épidémiologie : France, Grippe chez les oiseaux, Leishmaniose, Thaïlande.
- Algorithmes, Analyse spatio-temporelle, Animaux, Humains, Informatique mathématique, Loi du khi-deux, Méthode de Monte Carlo, Volaille, Études par échantillonnage.
- Wicri :
English descriptors
- KwdEn :
- Algorithms (MeSH), Animals (MeSH), Biomarkers (blood), Chi-Square Distribution (MeSH), France (epidemiology), Geographic Information Systems (statistics & numerical data), Humans (MeSH), Immunoglobulin G (blood), Influenza A Virus, H5N1 Subtype (isolation & purification), Influenza in Birds (epidemiology), Influenza in Birds (mortality), Influenza in Birds (virology), Leishmania (classification), Leishmania (immunology), Leishmania (isolation & purification), Leishmaniasis (blood), Leishmaniasis (epidemiology), Leishmaniasis (parasitology), Mathematical Computing (MeSH), Monte Carlo Method (MeSH), Poultry (MeSH), Sampling Studies (MeSH), Spatio-Temporal Analysis (MeSH), Thailand (epidemiology).
- MESH :
- chemical , blood : Biomarkers, Immunoglobulin G.
- geographic , epidemiology : France, Thailand.
- blood : Leishmaniasis.
- classification : Leishmania.
- epidemiology : Influenza in Birds, Leishmaniasis.
- immunology : Leishmania.
- isolation & purification : Influenza A Virus, H5N1 Subtype, Leishmania.
- mortality : Influenza in Birds.
- parasitology : Leishmaniasis.
- statistics & numerical data : Geographic Information Systems.
- virology : Influenza in Birds.
- Algorithms, Animals, Chi-Square Distribution, Humans, Mathematical Computing, Monte Carlo Method, Poultry, Sampling Studies, Spatio-Temporal Analysis.
Abstract
This article presents methods to analyze global spatial relationships between two variables in two different sets of fixed points. Analysis of spatial relationships between two phenomena is of great interest in health geography and epidemiology, especially to highlight competing interest between phenomena or evidence of a common environmental factor. Our general approach extends the Moran and Pearson indices to the bivariate case in two different sets of points. The case where the variables are Boolean is treated separately through methods using nearest neighbors distances. All tests use Monte-Carlo simulations to estimate their probability distributions, with options to distinguish spatial and no spatial correlation in the special case of identical sets analysis. Implementation in a Geographic Information System (SavGIS) and real examples are used to illustrate these spatial indices and methods in epidemiology.
DOI: 10.1016/j.sste.2011.06.001
PubMed: 22748222
Affiliations:
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xml:lang="en">This article presents methods to analyze global spatial relationships between two variables in two different sets of fixed points. Analysis of spatial relationships between two phenomena is of great interest in health geography and epidemiology, especially to highlight competing interest between phenomena or evidence of a common environmental factor. Our general approach extends the Moran and Pearson indices to the bivariate case in two different sets of points. The case where the variables are Boolean is treated separately through methods using nearest neighbors distances. All tests use Monte-Carlo simulations to estimate their probability distributions, with options to distinguish spatial and no spatial correlation in the special case of identical sets analysis. Implementation in a Geographic Information System (SavGIS) and real examples are used to illustrate these spatial indices and methods in epidemiology.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">22748222</PMID><DateCompleted><Year>2013</Year><Month>03</Month><Day>04</Day></DateCompleted><DateRevised><Year>2015</Year><Month>11</Month><Day>19</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1877-5853</ISSN><JournalIssue CitedMedium="Internet"><Volume>2</Volume><Issue>4</Issue><PubDate><Year>2011</Year><Month>Dec</Month></PubDate></JournalIssue><Title>Spatial and spatio-temporal epidemiology</Title><ISOAbbreviation>Spat Spatiotemporal Epidemiol</ISOAbbreviation></Journal><ArticleTitle>Statistical methods for bivariate spatial analysis in marked points. Examples in spatial epidemiology.</ArticleTitle><Pagination><MedlinePgn>227-34</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.sste.2011.06.001</ELocationID><Abstract><AbstractText>This article presents methods to analyze global spatial relationships between two variables in two different sets of fixed points. Analysis of spatial relationships between two phenomena is of great interest in health geography and epidemiology, especially to highlight competing interest between phenomena or evidence of a common environmental factor. Our general approach extends the Moran and Pearson indices to the bivariate case in two different sets of points. The case where the variables are Boolean is treated separately through methods using nearest neighbors distances. All tests use Monte-Carlo simulations to estimate their probability distributions, with options to distinguish spatial and no spatial correlation in the special case of identical sets analysis. Implementation in a Geographic Information System (SavGIS) and real examples are used to illustrate these spatial indices and methods in epidemiology.</AbstractText><CopyrightInformation>Copyright © 2011 Elsevier Ltd. All rights reserved.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Souris</LastName><ForeName>Marc</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Center of Excellence for Vectors and Vector-Borne Diseases, Faculty of Science, Mahidol University, Salaya, Nakhompathom 73170, Thailand. 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