Open Access Research article

Risk map and spatial determinants of pancreas disease in the marine phase of Norwegian Atlantic salmon farming sites

Saraya Tavornpanich1*, Mathilde Paul2, Hildegunn Viljugrein13, David Abrial4, Daniel Jimenez1 and Edgar Brun1

Author affiliations

1 Section for Epidemiology, Department of Health Surveillance, Norwegian Veterinary Institute, Ullevålsveien 68, Pb 750 Sentrum, N-0106, Oslo, Norway

2 UR AGIRs, Centre de coopération internationale en recherche agronomique pour le développement (CIRAD), TA C22/E, Campus international de Baillarguet, 34398, Montpellier cedex 5, France

3 Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biology, University of Oslo, P.O. Box 1066, Blindern, N-0316, Oslo, Norway

4 UR 346, Institut National de la Recherche Agronomique (INRA), 63122, Saint-Genès-Champanelle, France

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Citation and License

BMC Veterinary Research 2012, 8:172  doi:10.1186/1746-6148-8-172

Published: 24 September 2012



Outbreaks of pancreas disease (PD) greatly contribute to economic losses due to high mortality, control measures, interrupted production cycles, reduced feed conversion and flesh quality in the aquaculture industries in European salmon-producing countries. The overall objective of this study was to evaluate an effect of potential factors contributing to PD occurrence accounting for spatial congruity of neighboring infected sites, and then create quantitative risk maps for predicting PD occurrence. The study population included active Atlantic salmon farming sites located in the coastal area of 6 southern counties of Norway (where most of PD outbreaks have been reported so far) from 1 January 2009 to 31 December 2010.


Using a Bayesian modeling approach, with and without spatial component, the final model included site latitude, site density, PD history, and local biomass density. Clearly, the PD infected sites were spatially clustered; however, the cluster was well explained by the covariates of the final model. Based on the final model, we produced a map presenting the predicted probability of the PD occurrence in the southern part of Norway. Subsequently, the predictive capacity of the final model was validated by comparing the predicted probabilities with the observed PD outbreaks in 2011.


The framework of the study could be applied for spatial studies of other infectious aquatic animal diseases.

Pancreas disease; Aquatic epidemiology; Spatial analysis; Disease mapping; Bayesian modeling