Bayesian shared spatial-component models to combine and borrow strength across sparse disease surveillance sources.
Biom J
; 54(3): 385-404, 2012 May.
Article
em En
| MEDLINE
| ID: mdl-22685004
ABSTRACT
When analyzing the geographical variations of disease risk, one common problem is data sparseness. In such a setting, we investigate the possibility of using Bayesian shared spatial component models to strengthen inference and correct for any spatially structured sources of bias, when distinct data sources on one or more related diseases are available. Specifically, we apply our models to analyze the spatial variation of risk of two forms of scrapie infection affecting sheep in Wales (UK) using three surveillance sources on each disease. We first model each disease separately from the combined data sources and then extend our approach to jointly analyze diseases and data sources. We assess the predictive performances of several nested joint models through pseudo cross-validatory predictive model checks.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Doença
/
Métodos Epidemiológicos
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Modelos Estatísticos
/
Geografia
Tipo de estudo:
Etiology_studies
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Prognostic_studies
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Risk_factors_studies
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Screening_studies
Limite:
Animals
País como assunto:
Europa
Idioma:
En
Ano de publicação:
2012
Tipo de documento:
Article