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Prev Vet Med. 2018 Jul 25. pii: S0167-5877(17)30455-5. doi: 10.1016/j.prevetmed.2018.05.019. [Epub ahead of print]

A stochastic network-based model to simulate the spread of pancreas disease (PD) in the Norwegian salmon industry based on the observed vessel movements and seaway distance between marine farms.

Author information

1
Center for Animal Disease Modeling and Surveillance (CADMS), Department of Medicine & Epidemiology, School Veterinary Medicine, University of California, Davis, CA, USA.
2
Norwegian Veterinary Institute, Oslo, Norway.
3
Center for Animal Disease Modeling and Surveillance (CADMS), Department of Medicine & Epidemiology, School Veterinary Medicine, University of California, Davis, CA, USA. Electronic address: beamartinezlopez@ucdavis.edu.

Abstract

Pancreas disease (PD) is a viral disease of economic importance affecting farmed Atlantic salmon (Salmo salar L.) and rainbow trout (Oncorhyncus mykiss (Walbaum)) in the seawater phase in Ireland, Norway and Scotland. In this study we used a stochastic network-based disease spread model to better understand the role of vessel movements and nearby seaway distance on PD spread in marine farms. We used five different edge's definitions and weights for the network construction: high-risk vessel movements, high-risk wellboat movements and high-risk nearby seaway distance at <20 km, <10 km or <5 km, respectively. Models were used to simulate PD spread in marine farms as well as to simulate the spread of marine SAV2 and SAV3 subtypes independently and results were compared with the observed PD, marine SAV2 and SAV3 cases in Norway in 2016. Results revealed that the model that provided the best fit of the observed data and, therefore, the one considered more biologically plausible, was the one using high-risk wellboat movements. The marine SAV2, SAV3 and PD models using wellboat movements were able to correctly simulate the farms status (PD positive or PD negative) with the sensitivity of 84%, 85%, 84% and Specificity of 98%, 97% and 94%, respectively. These results should contribute to inform more cost-effective prevention and control policies to mitigate PD spread and to improve the sustainability and long-term profitability of the salmon industry in Norway.

KEYWORDS:

Aquaculture; Data-based model; Epidemiology; Risk-based surveillance; Spatio-temporal dynamics

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