A multivariate geostatistical framework for combining multiple indices of abundance for disease vectors and reservoirs: A case study of rattiness in a low-income urban Brazilian co...

Autores: Eyre, Max T.; Carvalho Pereira, Ticiana S. A.; Souza, Fábio N.; Khalil, Hussein; Hacker, Kathryn P.; Serrano, Laura Soledad; Taylor, Joshua Paul; Reis, Mitermayer G.; Ko, Albert I.; Begon, Mike; Diggle, Peter J.; Costa, Federico; Giorgi, Emanuele
Año de publicación: 2020
Idioma: inglés
Tipo de recurso: artículo
Estado: versión publicada
Descripción: A key requirement in studies of endemic vector-borne or zoonotic disease is an estimate of the spatial variation in vector or reservoir host abundance. For many vector species, multiple indices of abundance are available, but current approaches to choosing between or combining these indices do not fully exploit the potential inferential benefits that might accrue from modelling their joint spatial distribution. Here, we develop a class of multivariate generalized linear geostatistical models for multiple indices of abundance. We illustrate this novel methodology with a case study on Norway rats in a low-income urban Brazilian community, where rat abundance is a likely risk factor for human leptospirosis. We combine three indices of rat abundance to draw predictive inferences on a spatially continuous latent process, rattiness, that acts as a proxy for abundance. We show how to explore the association between rattiness and spatially varying environmental factors, evaluate the relative importance of each of the three contributing indices and assess the presence of residual, unexplained spatial variation, and identify rattiness hotspots. The proposed methodology is applicable more generally as a tool for understanding the role of vector or reservoir host abundance in predicting spatial variation in the risk of human disease.
Fil: Eyre, Max T.. Universidad de Lancaster. Facultad de Salud y Medicina. ; Reino Unido. University of Liverpool; Reino Unido
Fil: Carvalho Pereira, Ticiana S. A.. Universidade Federal da Bahia; Brasil
Fil: Souza, Fábio N.. Universidade Federal da Bahia; Brasil
Fil: Khalil, Hussein. Universidade Federal da Bahia; Brasil. Universidad de Ciencias Agrícolas de Suecia; Suecia
Fil: Hacker, Kathryn P.. University of Pennsylvania; Estados Unidos
Fil: Serrano, Laura Soledad. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentina
Fil: Taylor, Joshua Paul. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentina
Fil: Reis, Mitermayer G.. Fundación Oswaldo Cruz; Brasil. Universidade Federal da Bahia; Brasil
Fil: Ko, Albert I.. University of Yale; Estados Unidos. Fundación Oswaldo Cruz; Brasil
Fil: Begon, Mike. University of Liverpool; Reino Unido
Fil: Diggle, Peter J.. Universidad de Lancaster. Facultad de Salud y Medicina.; Reino Unido
Fil: Costa, Federico. University of Yale; Estados Unidos. Universidade Federal da Bahia; Brasil. Fundación Oswaldo Cruz; Brasil
Fil: Giorgi, Emanuele. Universidad de Lancaster. Facultad de Salud y Medicina.; Reino Unido
Materia: ABUNDANCE INDICES
EPIDEMIOLOGY
LEPTOSPIROSIS
MULTIVARIATE MODEL-BASED GEOSTATISTICS
NORWAY RAT
ZOONOTIC AND VECTOR-BORNE DISEASES
Nivel de accesibilidad: acceso abierto
Condiciones de uso: https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
Institución: Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador: oai:ri.conicet.gov.ar:11336/157048

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repository_id_str	3498
network_name_str	CONICET Digital (CONICET)
spelling	A multivariate geostatistical framework for combining multiple indices of abundance for disease vectors and reservoirs: A case study of rattiness in a low-income urban Brazilian communityEyre, Max T.Carvalho Pereira, Ticiana S. A.Souza, Fábio N.Khalil, HusseinHacker, Kathryn P.Serrano, Laura SoledadTaylor, Joshua PaulReis, Mitermayer G.Ko, Albert I.Begon, MikeDiggle, Peter J.Costa, FedericoGiorgi, EmanueleABUNDANCE INDICESEPIDEMIOLOGYLEPTOSPIROSISMULTIVARIATE MODEL-BASED GEOSTATISTICSNORWAY RATZOONOTIC AND VECTOR-BORNE DISEASEShttps://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1A key requirement in studies of endemic vector-borne or zoonotic disease is an estimate of the spatial variation in vector or reservoir host abundance. For many vector species, multiple indices of abundance are available, but current approaches to choosing between or combining these indices do not fully exploit the potential inferential benefits that might accrue from modelling their joint spatial distribution. Here, we develop a class of multivariate generalized linear geostatistical models for multiple indices of abundance. We illustrate this novel methodology with a case study on Norway rats in a low-income urban Brazilian community, where rat abundance is a likely risk factor for human leptospirosis. We combine three indices of rat abundance to draw predictive inferences on a spatially continuous latent process, rattiness, that acts as a proxy for abundance. We show how to explore the association between rattiness and spatially varying environmental factors, evaluate the relative importance of each of the three contributing indices and assess the presence of residual, unexplained spatial variation, and identify rattiness hotspots. The proposed methodology is applicable more generally as a tool for understanding the role of vector or reservoir host abundance in predicting spatial variation in the risk of human disease.Fil: Eyre, Max T.. Universidad de Lancaster. Facultad de Salud y Medicina. ; Reino Unido. University of Liverpool; Reino UnidoFil: Carvalho Pereira, Ticiana S. A.. Universidade Federal da Bahia; BrasilFil: Souza, Fábio N.. Universidade Federal da Bahia; BrasilFil: Khalil, Hussein. Universidade Federal da Bahia; Brasil. Universidad de Ciencias Agrícolas de Suecia; SueciaFil: Hacker, Kathryn P.. University of Pennsylvania; Estados UnidosFil: Serrano, Laura Soledad. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaFil: Taylor, Joshua Paul. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaFil: Reis, Mitermayer G.. Fundación Oswaldo Cruz; Brasil. Universidade Federal da Bahia; BrasilFil: Ko, Albert I.. University of Yale; Estados Unidos. Fundación Oswaldo Cruz; BrasilFil: Begon, Mike. University of Liverpool; Reino UnidoFil: Diggle, Peter J.. Universidad de Lancaster. Facultad de Salud y Medicina.; Reino UnidoFil: Costa, Federico. University of Yale; Estados Unidos. Universidade Federal da Bahia; Brasil. Fundación Oswaldo Cruz; BrasilFil: Giorgi, Emanuele. Universidad de Lancaster. Facultad de Salud y Medicina.; Reino UnidoThe Royal Society2020-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/157048Eyre, Max T.; Carvalho Pereira, Ticiana S. A.; Souza, Fábio N.; Khalil, Hussein; Hacker, Kathryn P.; et al.; A multivariate geostatistical framework for combining multiple indices of abundance for disease vectors and reservoirs: A case study of rattiness in a low-income urban Brazilian community; The Royal Society; Journal of the Royal Society Interface; 17; 170; 9-2020; 1-211742-56891742-5662CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1098/rsif.2020.0398info:eu-repo/semantics/altIdentifier/url/https://royalsocietypublishing.org/doi/10.1098/rsif.2020.0398info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:36:49Zoai:ri.conicet.gov.ar:11336/157048instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:36:49.843CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv	A multivariate geostatistical framework for combining multiple indices of abundance for disease vectors and reservoirs: A case study of rattiness in a low-income urban Brazilian community
title	A multivariate geostatistical framework for combining multiple indices of abundance for disease vectors and reservoirs: A case study of rattiness in a low-income urban Brazilian community
spellingShingle	A multivariate geostatistical framework for combining multiple indices of abundance for disease vectors and reservoirs: A case study of rattiness in a low-income urban Brazilian community Eyre, Max T. ABUNDANCE INDICES EPIDEMIOLOGY LEPTOSPIROSIS MULTIVARIATE MODEL-BASED GEOSTATISTICS NORWAY RAT ZOONOTIC AND VECTOR-BORNE DISEASES
title_short	A multivariate geostatistical framework for combining multiple indices of abundance for disease vectors and reservoirs: A case study of rattiness in a low-income urban Brazilian community
title_full	A multivariate geostatistical framework for combining multiple indices of abundance for disease vectors and reservoirs: A case study of rattiness in a low-income urban Brazilian community
title_fullStr	A multivariate geostatistical framework for combining multiple indices of abundance for disease vectors and reservoirs: A case study of rattiness in a low-income urban Brazilian community
title_full_unstemmed	A multivariate geostatistical framework for combining multiple indices of abundance for disease vectors and reservoirs: A case study of rattiness in a low-income urban Brazilian community
title_sort	A multivariate geostatistical framework for combining multiple indices of abundance for disease vectors and reservoirs: A case study of rattiness in a low-income urban Brazilian community
dc.creator.none.fl_str_mv	Eyre, Max T. Carvalho Pereira, Ticiana S. A. Souza, Fábio N. Khalil, Hussein Hacker, Kathryn P. Serrano, Laura Soledad Taylor, Joshua Paul Reis, Mitermayer G. Ko, Albert I. Begon, Mike Diggle, Peter J. Costa, Federico Giorgi, Emanuele
author	Eyre, Max T.
author_facet	Eyre, Max T. Carvalho Pereira, Ticiana S. A. Souza, Fábio N. Khalil, Hussein Hacker, Kathryn P. Serrano, Laura Soledad Taylor, Joshua Paul Reis, Mitermayer G. Ko, Albert I. Begon, Mike Diggle, Peter J. Costa, Federico Giorgi, Emanuele
author_role	author
author2	Carvalho Pereira, Ticiana S. A. Souza, Fábio N. Khalil, Hussein Hacker, Kathryn P. Serrano, Laura Soledad Taylor, Joshua Paul Reis, Mitermayer G. Ko, Albert I. Begon, Mike Diggle, Peter J. Costa, Federico Giorgi, Emanuele
author2_role	author author author author author author author author author author author author
dc.subject.none.fl_str_mv	ABUNDANCE INDICES EPIDEMIOLOGY LEPTOSPIROSIS MULTIVARIATE MODEL-BASED GEOSTATISTICS NORWAY RAT ZOONOTIC AND VECTOR-BORNE DISEASES
topic	ABUNDANCE INDICES EPIDEMIOLOGY LEPTOSPIROSIS MULTIVARIATE MODEL-BASED GEOSTATISTICS NORWAY RAT ZOONOTIC AND VECTOR-BORNE DISEASES
purl_subject.fl_str_mv	https://purl.org/becyt/ford/1.6 https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv	A key requirement in studies of endemic vector-borne or zoonotic disease is an estimate of the spatial variation in vector or reservoir host abundance. For many vector species, multiple indices of abundance are available, but current approaches to choosing between or combining these indices do not fully exploit the potential inferential benefits that might accrue from modelling their joint spatial distribution. Here, we develop a class of multivariate generalized linear geostatistical models for multiple indices of abundance. We illustrate this novel methodology with a case study on Norway rats in a low-income urban Brazilian community, where rat abundance is a likely risk factor for human leptospirosis. We combine three indices of rat abundance to draw predictive inferences on a spatially continuous latent process, rattiness, that acts as a proxy for abundance. We show how to explore the association between rattiness and spatially varying environmental factors, evaluate the relative importance of each of the three contributing indices and assess the presence of residual, unexplained spatial variation, and identify rattiness hotspots. The proposed methodology is applicable more generally as a tool for understanding the role of vector or reservoir host abundance in predicting spatial variation in the risk of human disease. Fil: Eyre, Max T.. Universidad de Lancaster. Facultad de Salud y Medicina. ; Reino Unido. University of Liverpool; Reino Unido Fil: Carvalho Pereira, Ticiana S. A.. Universidade Federal da Bahia; Brasil Fil: Souza, Fábio N.. Universidade Federal da Bahia; Brasil Fil: Khalil, Hussein. Universidade Federal da Bahia; Brasil. Universidad de Ciencias Agrícolas de Suecia; Suecia Fil: Hacker, Kathryn P.. University of Pennsylvania; Estados Unidos Fil: Serrano, Laura Soledad. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentina Fil: Taylor, Joshua Paul. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentina Fil: Reis, Mitermayer G.. Fundación Oswaldo Cruz; Brasil. Universidade Federal da Bahia; Brasil Fil: Ko, Albert I.. University of Yale; Estados Unidos. Fundación Oswaldo Cruz; Brasil Fil: Begon, Mike. University of Liverpool; Reino Unido Fil: Diggle, Peter J.. Universidad de Lancaster. Facultad de Salud y Medicina.; Reino Unido Fil: Costa, Federico. University of Yale; Estados Unidos. Universidade Federal da Bahia; Brasil. Fundación Oswaldo Cruz; Brasil Fil: Giorgi, Emanuele. Universidad de Lancaster. Facultad de Salud y Medicina.; Reino Unido
description	A key requirement in studies of endemic vector-borne or zoonotic disease is an estimate of the spatial variation in vector or reservoir host abundance. For many vector species, multiple indices of abundance are available, but current approaches to choosing between or combining these indices do not fully exploit the potential inferential benefits that might accrue from modelling their joint spatial distribution. Here, we develop a class of multivariate generalized linear geostatistical models for multiple indices of abundance. We illustrate this novel methodology with a case study on Norway rats in a low-income urban Brazilian community, where rat abundance is a likely risk factor for human leptospirosis. We combine three indices of rat abundance to draw predictive inferences on a spatially continuous latent process, rattiness, that acts as a proxy for abundance. We show how to explore the association between rattiness and spatially varying environmental factors, evaluate the relative importance of each of the three contributing indices and assess the presence of residual, unexplained spatial variation, and identify rattiness hotspots. The proposed methodology is applicable more generally as a tool for understanding the role of vector or reservoir host abundance in predicting spatial variation in the risk of human disease.
publishDate	2020
dc.date.none.fl_str_mv	2020-09
dc.type.none.fl_str_mv	info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo
format	article
status_str	publishedVersion
dc.identifier.none.fl_str_mv	http://hdl.handle.net/11336/157048 Eyre, Max T.; Carvalho Pereira, Ticiana S. A.; Souza, Fábio N.; Khalil, Hussein; Hacker, Kathryn P.; et al.; A multivariate geostatistical framework for combining multiple indices of abundance for disease vectors and reservoirs: A case study of rattiness in a low-income urban Brazilian community; The Royal Society; Journal of the Royal Society Interface; 17; 170; 9-2020; 1-21 1742-5689 1742-5662 CONICET Digital CONICET
url	http://hdl.handle.net/11336/157048
identifier_str_mv	Eyre, Max T.; Carvalho Pereira, Ticiana S. A.; Souza, Fábio N.; Khalil, Hussein; Hacker, Kathryn P.; et al.; A multivariate geostatistical framework for combining multiple indices of abundance for disease vectors and reservoirs: A case study of rattiness in a low-income urban Brazilian community; The Royal Society; Journal of the Royal Society Interface; 17; 170; 9-2020; 1-21 1742-5689 1742-5662 CONICET Digital CONICET
dc.language.none.fl_str_mv	eng
language	eng
dc.relation.none.fl_str_mv	info:eu-repo/semantics/altIdentifier/doi/10.1098/rsif.2020.0398 info:eu-repo/semantics/altIdentifier/url/https://royalsocietypublishing.org/doi/10.1098/rsif.2020.0398
dc.rights.none.fl_str_mv	info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv	openAccess
rights_invalid_str_mv	https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv	application/pdf application/pdf
dc.publisher.none.fl_str_mv	The Royal Society
publisher.none.fl_str_mv	The Royal Society
dc.source.none.fl_str_mv	reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str	CONICET Digital (CONICET)
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instname_str	Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv	CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv	dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score	13.070432

A multivariate geostatistical framework for combining multiple indices of abundance for disease vectors and reservoirs: A case study of rattiness in a low-income urban Brazilian co...

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