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Resumen
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
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dc.contributor.author | Eyre, Max T. | |
dc.contributor.author | Soares de Andrade de Carvalho Pereira, Ticiana | |
dc.contributor.author | Souza, Fabio N. | |
dc.contributor.author | Khalil, Hussein | |
dc.contributor.author | Hacker, Kathryn P. | |
dc.contributor.author | Serrano, Laura Soledad | |
dc.contributor.author | Taylor, Joshua Paul | |
dc.contributor.author | Reis, Mitermayer G. | |
dc.contributor.author | Ko, Albert I. | |
dc.contributor.author | Begon, Mike | |
dc.contributor.author | Diggle, Peter J. | |
dc.contributor.author | Costa, Federico | |
dc.contributor.author | Giorgi, Emanuele | |
dc.date.accessioned | 2020-09-25T11:48:09Z | |
dc.date.available | 2020-09-25T11:48:09Z | |
dc.date.issued | 2020-09-02 | |
dc.identifier.issn | 1742-5689 | |
dc.identifier.issn | 1742-5662 | |
dc.identifier.other | https://doi.org/10.1098/rsif.2020.0398 | |
dc.identifier.uri | http://hdl.handle.net/20.500.12123/7965 | |
dc.identifier.uri | https://royalsocietypublishing.org/doi/10.1098/rsif.2020.0398 | |
dc.description.abstract | 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. | eng |
dc.format | application/pdf | es_AR |
dc.language.iso | eng | es_AR |
dc.publisher | The Royal Society Publishing | es_AR |
dc.rights | info:eu-repo/semantics/restrictedAccess | es_AR |
dc.source | Journal of the Royal Society Interface 17 (170) : 1-21 (septiembre 2020) | es_AR |
dc.subject | Zoonosis | es_AR |
dc.subject | Zoonoses | eng |
dc.subject | Enfermedades Infecciosas | es_AR |
dc.subject | Infectious Diseases | eng |
dc.subject | Leptospirosis | es_AR |
dc.subject | Brasil | |
dc.subject | Brazil | eng |
dc.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 | es_AR |
dc.type | info:ar-repo/semantics/artículo | es_AR |
dc.type | info:eu-repo/semantics/article | es_AR |
dc.type | info:eu-repo/semantics/publishedVersion | es_AR |
dc.description.origen | Estación Experimental Agropecuaria Bariloche | es_AR |
dc.description.fil | Fil: Eyre, Max T. Lancaster University Medical School. Centre for Health Informatics, Computing, and Statistics; Reino Unido | es_AR |
dc.description.fil | Fil: Soares de Andrade de Carvalho Pereira, Ticiana. Universidade Federal da Bahia. Instituto de Saúde Coletiva; Brasil | es_AR |
dc.description.fil | Fil: Souza, Fabio N. Universidade Federal da Bahia. Instituto de Saúde Coletiva; Brasil | es_AR |
dc.description.fil | Fil: Khalil, Hussein. Swedish University of Agricultural Sciences; Suecia | es_AR |
dc.description.fil | Fil: Hacker, Kathryn P. University of Pennsylvania; Estados Unidos | es_AR |
dc.description.fil | 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 | es_AR |
dc.description.fil | 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 | es_AR |
dc.description.fil | Fil: Reis, Mitermayer G. Universidade Federal da Bahia. Instituto de Saúde Coletiva; Brasil | es_AR |
dc.description.fil | Fil: Ko, Albert I. Brazilian Ministry of Health. Oswaldo Cruz Foundation; Brasil | es_AR |
dc.description.fil | Fil: Begon, Mike. University of Liverpool. Department of Evolution, Ecology and Behaviour; Reino Unido | es_AR |
dc.description.fil | Fil: Diggle, Peter J. Lancaster University Medical School. Centre for Health Informatics, Computing, and Statistics; Reino Unido | es_AR |
dc.description.fil | Fil: Costa, Federico. Universidade Federal da Bahia. Instituto de Saúde Coletiva; Brasil | es_AR |
dc.description.fil | Fil: Giorgi, Emanuele. Lancaster University Medical School. Centre for Health Informatics, Computing, and Statistics; Reino Unido | es_AR |
dc.subtype | cientifico |
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