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.; Soares de Andrade de Carvalho Pereira, Ticiana; Souza, Fabio 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, 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.
Estación Experimental Agropecuaria Bariloche
Fil: Eyre, Max T. Lancaster University Medical School. Centre for Health Informatics, Computing, and Statistics; Reino Unido
Fil: Soares de Andrade de Carvalho Pereira, Ticiana. Universidade Federal da Bahia. Instituto de Saúde Coletiva; Brasil
Fil: Souza, Fabio N. Universidade Federal da Bahia. Instituto de Saúde Coletiva; Brasil
Fil: Khalil, Hussein. Swedish University of Agricultural Sciences; Suecia
Fil: Hacker, Kathryn P. University of Pennsylvania; Estados Unidos
Fil: Serrano, Laura Soledad. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentina
Fil: Taylor, Joshua Paul. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentina
Fil: Reis, Mitermayer G. Universidade Federal da Bahia. Instituto de Saúde Coletiva; Brasil
Fil: Ko, Albert I. Brazilian Ministry of Health. Oswaldo Cruz Foundation; Brasil
Fil: Begon, Mike. University of Liverpool. Department of Evolution, Ecology and Behaviour; Reino Unido
Fil: Diggle, Peter J. Lancaster University Medical School. Centre for Health Informatics, Computing, and Statistics; Reino Unido
Fil: Costa, Federico. Universidade Federal da Bahia. Instituto de Saúde Coletiva; Brasil
Fil: Giorgi, Emanuele. Lancaster University Medical School. Centre for Health Informatics, Computing, and Statistics; Reino Unido - Fuente
- Journal of the Royal Society Interface 17 (170) : 1-21 (septiembre 2020)
- Materia
-
Zoonosis
Enfermedades Infecciosas
Leptospirosis
Zoonoses
Infectious Diseases
Brazil
Brasil - Nivel de accesibilidad
- acceso restringido
- Condiciones de uso
- Repositorio
.jpg)
- Institución
- Instituto Nacional de Tecnología Agropecuaria
- OAI Identificador
- oai:localhost:20.500.12123/7965
Ver los metadatos del registro completo
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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.Soares de Andrade de Carvalho Pereira, TicianaSouza, Fabio N.Khalil, HusseinHacker, Kathryn P.Serrano, Laura SoledadTaylor, Joshua PaulReis, Mitermayer G.Ko, Albert I.Begon, MikeDiggle, Peter J.Costa, FedericoGiorgi, EmanueleZoonosisEnfermedades InfecciosasLeptospirosisZoonosesInfectious DiseasesBrazilBrasilA 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, 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.Estación Experimental Agropecuaria BarilocheFil: Eyre, Max T. Lancaster University Medical School. Centre for Health Informatics, Computing, and Statistics; Reino UnidoFil: Soares de Andrade de Carvalho Pereira, Ticiana. Universidade Federal da Bahia. Instituto de Saúde Coletiva; BrasilFil: Souza, Fabio N. Universidade Federal da Bahia. Instituto de Saúde Coletiva; BrasilFil: Khalil, Hussein. Swedish University of Agricultural Sciences; SueciaFil: Hacker, Kathryn P. University of Pennsylvania; Estados UnidosFil: Serrano, Laura Soledad. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaFil: Taylor, Joshua Paul. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaFil: Reis, Mitermayer G. Universidade Federal da Bahia. Instituto de Saúde Coletiva; BrasilFil: Ko, Albert I. Brazilian Ministry of Health. Oswaldo Cruz Foundation; BrasilFil: Begon, Mike. University of Liverpool. Department of Evolution, Ecology and Behaviour; Reino UnidoFil: Diggle, Peter J. Lancaster University Medical School. Centre for Health Informatics, Computing, and Statistics; Reino UnidoFil: Costa, Federico. Universidade Federal da Bahia. Instituto de Saúde Coletiva; BrasilFil: Giorgi, Emanuele. Lancaster University Medical School. Centre for Health Informatics, Computing, and Statistics; Reino UnidoThe Royal Society Publishing2020-09-25T11:48:09Z2020-09-25T11:48:09Z2020-09-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/7965https://royalsocietypublishing.org/doi/10.1098/rsif.2020.03981742-56891742-5662https://doi.org/10.1098/rsif.2020.0398Journal of the Royal Society Interface 17 (170) : 1-21 (septiembre 2020)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/restrictedAccess2025-09-29T13:45:02Zoai:localhost:20.500.12123/7965instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-09-29 13:45:02.424INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse |
| 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. Zoonosis Enfermedades Infecciosas Leptospirosis Zoonoses Infectious Diseases Brazil Brasil |
| 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. Soares de Andrade de Carvalho Pereira, Ticiana Souza, Fabio 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. Soares de Andrade de Carvalho Pereira, Ticiana Souza, Fabio 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 |
Soares de Andrade de Carvalho Pereira, Ticiana Souza, Fabio 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 |
Zoonosis Enfermedades Infecciosas Leptospirosis Zoonoses Infectious Diseases Brazil Brasil |
| topic |
Zoonosis Enfermedades Infecciosas Leptospirosis Zoonoses Infectious Diseases Brazil Brasil |
| 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, 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. Estación Experimental Agropecuaria Bariloche Fil: Eyre, Max T. Lancaster University Medical School. Centre for Health Informatics, Computing, and Statistics; Reino Unido Fil: Soares de Andrade de Carvalho Pereira, Ticiana. Universidade Federal da Bahia. Instituto de Saúde Coletiva; Brasil Fil: Souza, Fabio N. Universidade Federal da Bahia. Instituto de Saúde Coletiva; Brasil Fil: Khalil, Hussein. Swedish University of Agricultural Sciences; Suecia Fil: Hacker, Kathryn P. University of Pennsylvania; Estados Unidos Fil: Serrano, Laura Soledad. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentina Fil: Taylor, Joshua Paul. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentina Fil: Reis, Mitermayer G. Universidade Federal da Bahia. Instituto de Saúde Coletiva; Brasil Fil: Ko, Albert I. Brazilian Ministry of Health. Oswaldo Cruz Foundation; Brasil Fil: Begon, Mike. University of Liverpool. Department of Evolution, Ecology and Behaviour; Reino Unido Fil: Diggle, Peter J. Lancaster University Medical School. Centre for Health Informatics, Computing, and Statistics; Reino Unido Fil: Costa, Federico. Universidade Federal da Bahia. Instituto de Saúde Coletiva; Brasil Fil: Giorgi, Emanuele. Lancaster University Medical School. Centre for Health Informatics, Computing, and Statistics; 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, 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 |
2020-09-25T11:48:09Z 2020-09-25T11:48:09Z 2020-09-02 |
| 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/20.500.12123/7965 https://royalsocietypublishing.org/doi/10.1098/rsif.2020.0398 1742-5689 1742-5662 https://doi.org/10.1098/rsif.2020.0398 |
| url |
http://hdl.handle.net/20.500.12123/7965 https://royalsocietypublishing.org/doi/10.1098/rsif.2020.0398 https://doi.org/10.1098/rsif.2020.0398 |
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1742-5689 1742-5662 |
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eng |
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eng |
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info:eu-repo/semantics/restrictedAccess |
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restrictedAccess |
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application/pdf |
| dc.publisher.none.fl_str_mv |
The Royal Society Publishing |
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The Royal Society Publishing |
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Journal of the Royal Society Interface 17 (170) : 1-21 (septiembre 2020) reponame:INTA Digital (INTA) instname:Instituto Nacional de Tecnología Agropecuaria |
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Instituto Nacional de Tecnología Agropecuaria |
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INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria |
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tripaldi.nicolas@inta.gob.ar |
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