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

Lancaster University Medical School. Centre for Health Informatics, Computing, and Statistics. Lancaster, UK / Liverpool School of Tropical Medicine. Liverpool, UK.
Federal University of Bahia. Institute of Collective Health. Salvador, BA, Brazil.
Federal University of Bahia. Institute of Collective Health. Salvador, BA, Brazil.
Federal University of Bahia. Institute of Collective Health. Salvador, BA, Brazil / Swedish University of Agricultural Sciences. Umeå, Sweden.
University of Pennsylvania. Philadelphia, PA, USA.
Instituto de Investigaciones Forestales y Agropecuarias Bariloche.San Carlos de Bariloche, Río Negro, Argentina.
Instituto de Investigaciones Forestales y Agropecuarias Bariloche.San Carlos de Bariloche, Río Negro, Argentina.
Federal University of Bahia. Institute of Collective Health. Salvador, BA, Brazil / Fundação Oswaldo Cruz. instituto Gonçalo Moniz. Salvador, BA, Brasil.
Fundação Oswaldo Cruz. instituto Gonçalo Moniz. Salvador, BA, Brasil / Yale School of Public Health. Department of Epidemiology of Microbial Diseases. New Haven, USA.
University of Liverpool. Department of Evolution, Ecology and Behaviour. Liverpool, UK.
Lancaster University Medical School. Centre for Health Informatics, Computing, and Statistics. Lancaster, UK
Federal University of Bahia. Institute of Collective Health. Salvador, BA, Brazil / Fundação Oswaldo Cruz. instituto Gonçalo Moniz. Salvador, BA, Brasil / Yale School of Public Health. Department of Epidemiology of Microbial Diseases. New Haven, USA.
Lancaster University Medical School. Centre for Health Informatics, Computing, and Statistics. Lancaster, UK.

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 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.

Keywords in Portuguese

Epidemiologia
Zoonoses
Doenças transmitidas por vetores
Leptospirose
Ratos

Keywords

Epidemiology
Abundance indices
Zoonotic and vector-borne diseases
Multivariate modelbased geostatistics
Leptospirosis
Norway rat

Publisher

The Royal Society [Society

Citation

EYRE, Max T. 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. Journal of the Royal Society, Interface, 2020.

DOI

10.1098/rsif.2020.0398

ISSN

1742-5689

Please use this identifier to cite or link to this item: https://www.arca.fiocruz.br/handle/icict/44075

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Copyright

Open access

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