Bayesian shared spatial-component models to combine and borrow strength across sparse disease surveillance sources.
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.
AgroParisTech/INRA UMR, Department of Applied Mathematics and Informatics, MORSE team, Paris, France. sophie.ancelet@irsn.fr
Sophie
S
Ancelet
Juan J
JJ
Abellan
Víctor J
VJ
Del Rio Vilas
Colin
C
Birch
Sylvia
S
Richardson
D000818:Animals; D001499:Bayes Theorem; D015982:Bias; D004194:Disease; D004812:Epidemiologic Methods / Q000662:veterinary; D005843:Geography; D015233:Models, Statistical; D012306:Risk; D012307:Risk Factors; D012608:Scrapie / Q000453:epidemiology; D012756:Sheep; D014852:Wales / Q000453:epidemiology