Data-Driven Network Modeling as a Framework to Evaluate the Transmission of Piscine Myocarditis Virus (PMCV) in the Irish Farmed Atlantic Salmon Population and the Impact of Different Mitigation Measures.

Cardiomyopathy syndrome (CMS) is a severe cardiac disease of Atlantic salmon caused by the piscine myocarditis virus (PMCV), which was first reported in Ireland in 2012. In this paper, we describe the use of data-driven network modeling as a framework to evaluate the transmission of PMCV in the Irish farmed Atlantic salmon population and the impact of different mitigation measures. Input data included live fish movement data from 2009 to 2017, population dynamics events and the spatial location of the farms. With these inputs, we fitted a network-based stochastic infection spread model. After assumed initial introduction of the agent in 2009, our results indicate that it took 5 years to reach a between-farm prevalence of 100% in late 2014, with older fish being most affected. Local spread accounted for only a small proportion of new infections, being more important for sustained infection in a given area. Spread via movement of subclinically infected fish was most important for explaining the observed countrywide spread of the agent. Of the targeted intervention strategies evaluated, the most effective were those that target those fish farms in Ireland that can be considered the most connected, based on the number of farm-to-farm linkages in a specific time period through outward fish movements. The application of these interventions in a proactive way (before the first reported outbreak of the disease in 2012), assuming an active testing of fish consignments to and from the top 8 ranked farms in terms of outward fish movement, would have yielded the most protection for the Irish salmon farming industry. Using this approach, the between-farm PMCV prevalence never exceeded 20% throughout the simulation time (as opposed to the simulated 100% when no interventions are applied). We argue that the Irish salmon farming industry would benefit from this approach in the future, as it would help in early detection and prevention of the spread of viral agents currently exotic to the country.

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.

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.

Can biosecurity and local network properties predict pathogen species richness in the salmonid industry?

Salmonid farming in Ireland is mostly organic, which implies limited disease treatment options. This highlights the importance of biosecurity for preventing the introduction and spread of infectious agents. Similarly, the effect of local network properties on infection spread processes has rarely been evaluated. In this paper, we characterized the biosecurity of salmonid farms in Ireland using a survey, and then developed a score for benchmarking the disease risk of salmonid farms. The usefulness and validity of this score, together with farm indegree (dichotomized as ≤ 1 or > 1), were assessed through generalized Poisson regression models, in which the modeled outcome was pathogen richness, defined here as the number of different diseases affecting a farm during a year. Seawater salmon (SW salmon) farms had the highest biosecurity scores with a median (interquartile range) of 82.3 (5.4), followed by freshwater salmon (FW salmon) with 75.2 (8.2), and freshwater trout (FW trout) farms with 74.8 (4.5). For FW salmon and trout farms, the top ranked model (in terms of leave-one-out information criteria, looic) was the null model (looic = 46.1). For SW salmon farms, the best ranking model was the full model with both predictors and their interaction (looic = 33.3). Farms with a higher biosecurity score were associated with lower pathogen richness, and farms with indegree > 1 (i.e. more than one fish supplier) were associated with increased pathogen richness. The effect of the interaction between these variables was also important, showing an antagonistic effect. This would indicate that biosecurity effectiveness is achieved through a broader perspective on the subject, which includes a minimization in the number of suppliers and hence in the possibilities for infection to enter a farm. The work presented here could be used to elaborate indicators of a farm's disease risk based on its biosecurity score and indegree, to inform risk-based disease surveillance and control strategies for private and public stakeholders.

Epidemiological description of the sea lice (Caligus rogercresseyi) situation in southern Chile in August 2007.

Salmon sea lice represent one of the most important threats to salmon farming throughout the world. Results of private monitoring efforts have shown an increase in the number of positive cages and cage-level abundance of sea lice in southern Chile since 2004. As a consequence, the Chilean Fisheries Service implemented an Official Surveillance Program in the main salmon production area of southern Chile to assess the situation of sea lice in fish farms. Results showed that the prevalence of sea lice in the fish farms was 53.4%, ranging from 3.5% in Puerto Aysén to 100% in the Seno de Reloncaví zone. The average sea lice abundance was 11.8 per fish (Geometrical mean (GM)=8.61, 95% CI (2.1-6.9)). The highest levels were found in Seno de Reloncaví (GM=24.99, 95% CI (15.9-39.2)), Hornopirén (GM=14.7, 95% CI (10.4-20.8)) and Chiloé norte (GM=9.75, 95% CI (1-1.9)), and the lowest loads were observed in Puerto Aysén (GM=1.35, 95%CI (1-1.9)) and Puerto Cisnes (GM=1.67, 95%CI (1.1-2.6)). Salmo salar and Oncorhynchus mykiss had the highest abundance levels (GM=6.93, 95% CI (5.7-8.5), and (GM=5.55, 95% CI (3.6-8.5), respectively). O. kisutch showed lower levels (GM=1.34, 95% CI (1-1.7)), apparently being more resistant to infestation. Sea lice in farmed salmon are widely distributed in different zones of southern Chile, and are becoming a serious threat to this industry. Prevalence and abundance levels were found to be generally high, decreasing in southern zones.