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
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.
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The Royal Society Publishing
2020-09-02
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Subjects: | Zoonosis, Enfermedades Infecciosas, Leptospirosis, Zoonoses, Infectious Diseases, Brazil, Brasil, |
Online Access: | 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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oai:localhost:20.500.12123-79652020-09-25T11:56:55Z 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. 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 Zoonosis Enfermedades Infecciosas Leptospirosis Zoonoses Infectious Diseases Brazil Brasil 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 2020-09-25T11:48:09Z 2020-09-25T11:48:09Z 2020-09-02 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 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 eng info:eu-repo/semantics/restrictedAccess application/pdf The Royal Society Publishing Journal of the Royal Society Interface 17 (170) : 1-21 (septiembre 2020) |
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Zoonosis Enfermedades Infecciosas Leptospirosis Zoonoses Infectious Diseases Brazil Brasil Zoonosis Enfermedades Infecciosas Leptospirosis Zoonoses Infectious Diseases Brazil Brasil |
spellingShingle |
Zoonosis Enfermedades Infecciosas Leptospirosis Zoonoses Infectious Diseases Brazil Brasil Zoonosis Enfermedades Infecciosas Leptospirosis Zoonoses Infectious Diseases Brazil Brasil 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 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 |
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. |
format |
info:ar-repo/semantics/artículo |
topic_facet |
Zoonosis Enfermedades Infecciosas Leptospirosis Zoonoses Infectious Diseases Brazil Brasil |
author |
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_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_sort |
Eyre, Max T. |
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 |
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 |
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 |
publisher |
The Royal Society Publishing |
publishDate |
2020-09-02 |
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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