Space-time modelling of the spread of pancreas disease (PD) within and between Norwegian marine salmonid farms

On the prediction of SAV transmission among Norwegian aquaculture sites

Pancreas Disease (PD) is a viral disease that affects Atlantic salmon (Salmo salar) in Norwegian, Scottish and Irish aquaculture. It is caused by salmonid alphavirus (SAV) and represents a significant problem in salmonid farming. Infection with SAV leads to reduced growth, mortality, product downgrading, and has a significant financial impact for the farms. The overall aim of this study is to evaluate the effect of various factors on the transmission of SAV and to create a predictive model capable of providing an early warning system for salmon farms within the Norwegian waters. Using a combination of publicly available databases, specifically BarentsWatch, and privately held PCR analyses a feature set consisting of 11 unique features was created based on the input parameters of the databases. An ensemble model was developed based on this feature set using XG-Boost, Ada-Boost, Random Forest and a Multilayer Perceptron. It was possible to successfully predict SAV transmission with 94.4% accuracy. Moreover, it was possible to predict SAV transmission 8 weeks in advance of a 'PD registration' at individual aquaculture salmon farming sites. Important predictors included well boat movement, environmental factors, proximity to sites with a 'PD registration' and seasonality.

A Scoping Review on GIS Technologies Applied to Farmed Fish Health Management

Finfish aquaculture, one of the fastest growing intensive sectors worldwide, is threatened by numerous transmissible diseases that may have devastating impacts on its economic sustainability. This review (2010-2022) used a PRISMA extension for scoping reviews and a text mining approach to explore the extent to which geographical information systems (GIS) are used in farmed fish health management and to unveil the main GIS technologies, databases, and functions used to update the spatiotemporal data underpinning risk and predictive models in aquatic surveillance programmes. After filtering for eligibility criteria, the literature search provided 54 records, highlighting the limited use of GIS technologies for disease prevention and control, as well as the prevalence of GIS application in marine salmonid farming, especially for viruses and parasitic diseases typically associated with these species. The text mining generated five main research areas, underlining a limited range of investigated species, rearing environments, and diseases, as well as highlighting the lack of GIS-based methodologies at the core of such publications. This scoping review provides a source of information for future more detailed literature analyses and outcomes to support the development of geospatial disease spread models and expand in-field GIS technologies for the prevention and mitigation of fish disease epidemics.

Assessment of listing and categorisation of animal diseases within the framework of the Animal Health Law (Regulation (EU)2016/429): Infection with salmonid alphavirus (SAV)

Infection with salmonid alphavirus (SAV) was assessed according to the criteria of the Animal Health Law (AHL), in particular the criteria of Article 7 on disease profile and impacts, Article 5 on its eligibility to be listed, Annex IV for its categorisation according to disease prevention and control rules as laid out in Article 9 and Article 8 for listing animal species related to infection with SAV. The assessment was performed following the ad hoc method on data collection and assessment developed by AHAW Panel and already published. The outcome reported is the median of the probability ranges provided by the experts, which indicates whether each criterion is fulfilled (lower bound ≥ 66%) or not (upper bound ≤ 33%), or whether there is uncertainty about fulfilment. Reasoning points are reported for criteria with an uncertain outcome. According to the assessment, it was uncertain whether infection with salmonid alphavirus can be considered eligible to be listed for Union intervention according to Article 5 of the AHL (50-80% probability). According to the criteria in Annex IV, for the purpose of categorisation related to the level of prevention and control as in Article 9 of the AHL, the AHAW Panel concluded that infection with salmonid alphavirus does not meet the criteria in Section 1 (Category A; 5-10% probability of meeting the criteria) and it is uncertain whether it meets the criteria in Sections 2, 3, 4 and 5 (Categories B, C, D and E; 50-90%, probability of meeting the criteria). The animal species to be listed for infection with SAV according to Article 8 criteria are provided.

Towards better survival: Modeling drivers for daily mortality in Norwegian Atlantic salmon farming

Mortality in the production of farmed salmonids is a major constraint to the sustainability of this form of animal husbandry. We have developed a model for the daily mortality in salmon farming over a full production cycle from stocking to harvest, considering different environmental and production factors. These factors included sea temperature, salinity, day within year, fish weight at stocking, stocking day, four types of lice treatments and the possible occurrence of pancreas disease (PD). We considered a generalized additive model following full production cycles, allowing for non-linear descriptions of how relevant factors relate to the daily mortality. We saw a high overall mortality rate immediately after stocking, which decreased the first three months in the cycle and thereafter increased. We found that the total mortality could be reduced by 21% if avoiding all lice treatments, and similarly reduced by 20% if no PD infections occurred. If avoiding jointly PD and all lice treatments, the accumulated mortality could be reduced by 34%. A single thermal or hydrogen peroxide treatment was associated with a mortality of around 1.6% and 1.3%, respectively. This modeling approach gave a unique opportunity to model how different factors interact on the overall global mortality and can easily be extended by other factors, such as additional fish diseases.

Realtime case study simulations of transmission of Pancreas Disease (PD) in Norwegian salmonid farming for disease control purposes

Pancreas Disease (PD) is a viral disease caused by Salmonid Alphavirus (SAV). It affects farmed salmonids in the North Atlantic, and leads to reduced feed intake and increased mortality with reduced production and welfare as a consequence. In 2013, the estimated cost of an outbreak on an average salmon farm was about 6.6 mil €. In Norway, PD has been notifiable since 2008, and regulations to mitigate disease spread are in place. However, despite the regulations, 140-170 farms are affected by PD every year. The aquaculture industry is growing continuously, introducing farms in new geographical areas, and fish are moved between hydrographically separated zones for trade and slaughter. All such movements and relocations need to be approved by the competent authorities. Thus, there is a demand for support to farmers and competent authorities when making decisions on disease management and especially on the effect of moving infected fish. We have used a disease-transmission model for outbreak-simulation in real time for assessing the probability of disease transmission from a farm that gets infected with PD. We have also simulated the effects of three different control-regimes: no stamping-out, delayed stamping-out or immediate stamping-out, on the transmission of PD to surrounding farms. Simulations showed that the immediate stamping out of an infected farm led to effective containment of an outbreak. No stamping out led to up to 32.1% of farms within 100 km of the index farm to become effected. We have used real production data for the model building and the scenario simulations, and the results illustrate that a risk assessment of horizontal disease transmission must be undertaken on a case-by-case basis, because the time and place of the outbreak has a large influence on the risk of transmission.

Early detection of salmonid alphavirus in seawater from marine farm sites of Atlantic salmon Salmo salar

The traditional strategy for national surveillance of salmonid alphavirus (SAV) infection in Norwegian fish farms relies on a costly, time-consuming, and resource-demanding approach based on the monthly sampling of fish from all marine farms with salmonids. In order to develop an alternative surveillance method, a water filtration method was tested in parallel with the ongoing surveillance program at 7 Norwegian marine farm sites of Atlantic salmon Salmo salar L. with no current suspicion of SAV infection. During the period from May 2019 to January 2020, seawater samples were collected from the top layer water inside all net-pens at these 7 sites. The samples were concentrated for SAV by filtration through an MF-Millipore™ electronegative membrane filter, followed by rinsing with NucliSENS® Lysis Buffer, before RNA extraction and analysis by RT-qPCR. SAV was detected from seawater at an earlier stage compared to traditional sampling methods, at all sites where the fish tested positive for SAV. A significant negative relationship was observed at all sites between the SAV concentration found in seawater samples and the number of days until SAV was detected in the fish. This means that the fewer the SAV particles in the seawater, the more days it took until SAV was detected in the fish samples. Based on this, sampling of seawater every month for the surveillance of SAV has a great potential as an alternative method for early detection of SAV in Atlantic salmon farms.

DTU-DADS-Aqua: A simulation framework for modelling waterborne spread of highly infectious pathogens in marine aquaculture

Simulation models are useful tools to predict and elucidate the effects of factors influencing the occurrence and spread of epidemics in animal populations, evaluate the effectiveness of different control strategies and ultimately inform decision-makers about mitigations to reduce risk. There is a paucity of simulation models to study waterborne transmission of viral and bacterial pathogens in marine environments. We developed a stochastic, spatiotemporal hybrid simulation model (DTU-DADS-Aqua) that incorporates a compartmental model for infection spread within net-pens, an agent-based model for infection spread between net-pens within and between sites and uses seaway distance to inform farm-site hydroconnectivity. The model includes processes to simulate infection transmission and control over surveillance, detection and depopulation measures. Different what-if scenarios can be explored according to the input data provided and user-defined parameter values, such as daily surveillance and depopulation capacities or increased animal mortality that triggers diagnostic testing to detect infection. The latter can be easily defined in a software application, in which results are summarized after each simulation. To demonstrate capabilities of the model, we simulated the spread of infectious salmon anaemia virus (ISAv) for realistic scenarios in a transboundary population of farmed Atlantic salmon (Salmo salar L.) in New Brunswick, Canada and Maine, United States. We assessed the progression of infection in the different simulated outbreak scenarios, allowing for variation in the control strategies adopted for ISAv. Model results showed that improved disease detection, coupled with increasing surveillance visits to farm-sites and increased culling capacity for depopulation of infected net-pens reduced the number of infected net-pens and outbreak duration but the number of ISA-infected farm sites was minimally affected. DTU-DADS-Aqua is a flexible modelling framework, which can be applied to study different infectious diseases in the aquatic environment, allowing the incorporation of alternative transmission and control dynamics. The framework is open-source and available at https://github.com/upei-aqua/DTU-DADS-Aqua.

Evaluating effects of different control strategies for Infectious Salmon Anaemia (ISA) in marine salmonid farming by scenario simulation using a disease transmission model

Infectious salmon anaemia (ISA) is an important viral disease causing economic losses and reduced welfare in farmed Atlantic salmon. Here, we present a spatio-temporal stochastic model for the spread of ISA between and within marine aquaculture farms. The model is estimated on historical production data for all marine salmonid farms in Norway from 2004 to February 2019. In this time 142 outbreaks of ISA occurred. We find that transmission from infected neighbouring farms accounts for around 50% of the infections, whereas transmission from "non-specified sources" accounts for around 40%. We hypothesise that the most important of the latter are viruses mutating from the non-virulent ISAV HPR0 to the virulent ISAV HPRdel. The model is used for scenario simulation, or what-if analysis, to investigate the effects of potential strategies to combat ISA, including screening, vaccination and culling. Changing from the current strategy of culling farms with detected ISA-outbreaks to mandatory screening and culling when virus is detected will reduce the fraction of cohorts with a clinical ISA outbreak from 3.8 to 0.36%. Introducing mandatory vaccination would have approximately the same effect as the current stamping-out strategy. The scenario simulation is a useful tool for deciding on appropriate mitigation measures.

Modelling the effect of vaccination on transmission dynamics of nervous necrosis virus in grouper larvae Epinephelus coioides

Nervous necrosis virus (NNV) infection in susceptible grouper larvae has been reported to cause high mortalities, leading to great economic losses in aquaculture industry. Although the effects of NNV vaccines on grouper have been broadly investigated, vaccination strategies have not been fully established. To this end, we introduced the parsimonious epidemiological models that explored the assessment of key epidemiological parameters and how they changed when vaccinations showed the effects. We showed that the models capture the published cumulative mortality data accurately. We estimated a basic reproduction number R₀  = 2.44 for NNV transmission in grouper larvae without vaccination. To effectively control NNV transmission by vaccination, a model for disease control was also generalized to attain the goals of controlled reproduction number less than 1. Our results indicated that at least 60% of grouper population needed to be immunized for ~75 min. Our data-driven modelling approach that links the transmission dynamics of NNV and vaccination strategies for grouper has the potential to support evidence-based planning and adaptation of integrated control measures. We encourage that the epidemiology-based framework introduced here can be further implemented for establishing effective vaccination and mitigation actions aimed at controlling diseases in fish farming practices.

The economic value of R0 for selective breeding against microparasitic diseases

Background

Microparasitic diseases are caused by bacteria and viruses. Genetic improvement of resistance to microparasitic diseases in breeding programs is desirable and should aim at reducing the basic reproduction ratio \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}_{0}$$\end{document}R0. Recently, we developed a method to derive the economic value of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}_{0}$$\end{document}R0 for macroparasitic diseases. In epidemiological models for microparasitic diseases, an animal’s disease status is treated as infected or not infected, resulting in a definition of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}_{0}$$\end{document}R0 that differs from that for macroparasitic diseases. Here, we extend the method for the derivation of the economic value of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}_{0}$$\end{document}R0 to microparasitic diseases.

Methods

When \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}_{0} \le 1$$\end{document}R0≤1, the economic value of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}_{0}$$\end{document}R0 is zero because the disease is very rare. When \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}_{0}$$\end{document}R0. is higher than 1, genetic improvement of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}_{0}$$\end{document}R0 can reduce expenditures on vaccination if vaccination induces herd immunity, or it can reduce production losses due to disease. When vaccination is used to achieve herd immunity, expenditures are proportional to the critical vaccination coverage, which decreases with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}_{0}$$\end{document}R0. The effect of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}_{0}$$\end{document}R0 on losses is considered separately for epidemic and endemic disease. Losses for epidemic diseases are proportional to the probability and size of major epidemics. Losses for endemic diseases are proportional to the infected fraction of the population at the endemic equilibrium.

Results

When genetic improvement reduces expenditures on vaccination, expenditures decrease with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}_{0}$$\end{document}R0 at an increasing rate. When genetic improvement reduces losses in epidemic or endemic diseases, losses decrease with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}_{0}$$\end{document}R0 at an increasing rate. Hence, in all cases, the economic value of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}_{0}$$\end{document}R0 increases as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}_{0}$$\end{document}R0 decreases towards 1.

Discussion

\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}_{0}$$\end{document}R0 and its economic value are more informative for potential benefits of genetic improvement than heritability estimates for survival after a disease challenge. In livestock, the potential for genetic improvement is small for epidemic microparasitic diseases, where disease control measures limit possibilities for phenotyping. This is not an issue in aquaculture, where controlled challenge tests are performed in dedicated facilities. If genetic evaluations include infectivity, genetic gain in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}_{0}$$\end{document}R0 can be accelerated but this would require different testing designs.

Conclusions

When \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}_{0} \le 1$$\end{document}R0≤1, its economic value is zero. The economic value of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}_{0}$$\end{document}R0 is highest at low values of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}_{0}$$\end{document}R0 and approaches zero at high values of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}_{0}$$\end{document}R0.

Response of the Salmon Heart Transcriptome to Pancreas Disease: Differences Between High- and Low-Ranking Families for Resistance

Pancreas disease caused by salmonid alphaviruses leads to severe losses in Atlantic salmon aquaculture. The aim of our study was to gain a better understanding of the biological differences between salmon with high and low genomic breeding values (H-gEBV and L-gEBV respectively) for pancreas disease resistance. Fish from H- and L-gEBV families were challenged by intraperitoneal injection of salmonid alphavirus or co-habitation with infected fish. Mortality was higher with co-habitation than injection, and for L- than H-gEBV. Heart for RNA-seq and histopathology was collected before challenge and at four- and ten-weeks post-challenge. Heart damage was less severe in injection-challenged H- than L-gEBV fish at week 4. Viral load was lower in H- than L-gEBV salmon after co-habitant challenge. Gene expression differences between H- and L-gEBV manifested before challenge, peaked at week 4, and moderated by week 10. At week 4, H-gEBV salmon showed lower expression of innate antiviral defence genes, stimulation of B- and T-cell immune function, and weaker stress responses. Retarded resolution of the disease explains the higher expression of immune genes in L-gEBV at week 10. Results suggest earlier mobilization of acquired immunity better protects H-gEBV salmon by accelerating clearance of the virus and resolution of the disease.

Quantifying the impact of temperature variation on birnavirus transmission dynamics in hard clams Meretrix lusoria

Susceptibility of hard clams Meretrix lusoria to birnavirus (BV) infections caused by temperature variations, from a mechanistic perspective, has rarely been explored. We used a deterministic susceptible-infectious-mortality (SIM) model to derive temperature-dependent key epidemiologic parameters based on data sets of viral infections in hard clams subjected to acute temperature changes. To parameterize seasonal pattern dependence, we estimated monthly based cumulative mortality and basic reproduction numbers (R₀ ) between 1997 and 2017 by way of statistical analysis. Two alternative disease control models were also proposed to assess status of controlled temperature-mediated BV infection by using, respectively, control reproduction number (R_C )-control line criterion and removal strategy-based control measure. We showed that based on R_C -control strategy, when temperatures ranged from 15 to 26.8°C, proportion of susceptible hard clams removed should be at least 0.22%. Based on removal-control strategy, we found that by limiting pond water temperature to 25-30°C, together with increased removal rates and periods to remove hard clams, it is better to remove hard clams from June and August to reduce both mortality rate and spread of BV. Our results can be used to monitor BV transmission potential in hard clams that will contribute to government control strategy to eradicate future BV epidemics.

Effect of pancreas disease caused by SAV 2 on protein and fat digestion in Atlantic salmon

Salmonid alphavirus (SAV) causes pancreas disease (PD) in farmed Atlantic salmon (Salmo salar L.), and exocrine pancreas tissue is a primary target of the virus. Digestive enzymes secreted by the exocrine pancreas break down macromolecules in feed into smaller molecules that can be absorbed. The effect of SAV infection on digestion has been poorly studied. In this study, longitudinal observations of PD outbreaks caused by SAV subtype 2 (SAV2) in Atlantic salmon at two commercial sea sites were performed. The development of PD was assessed by measurement of SAV2 RNA load and evaluation of histopathological lesions typical of PD. Reduced digestion of both protein and fat co-varied with the severity of PD lesions and viral load. Also, the study found that during a PD outbreak, the pen population comprise several subpopulations, with different likelihoods of being sampled. The body length of sampled fish deviated from the expected increase or steady state over time, and the infection status in sampled fish deviated from the expected course of infection in the population. Both conditions indicate that disease status of the individual fish influenced the likelihood of being sampled, which may cause sampling bias in population studies.

Salmonid Alphavirus (SAV)

Salmonid alphavirus (SAV), genus Alphavirus, family Togaviridae, is a single-stranded RNA virus affecting Atlantic salmon (Salmo salar) and rainbow trout (Oncorhynchus mykiss). It is known to be responsible for pancreas disease (PD) and sleeping disease (SD) which are increasing problems, causing high fish mortality and economic losses in the European aquaculture industry. Pancreas disease was first described in Atlantic salmon in Scotland in 1976 and a similar disease caused by the closely related sleeping disease virus was first described in rainbow trout in France. There have also been reports of salmonid alphavirus infections from other European countries, including Ireland, England, Norway, Germany, Italy, and Spain. Salmonid alphaviruses have been classified into six subtypes (SAV1–6). SAV1 and SAV4–6 cause pancreas disease in Atlantic salmon in Ireland or Scotland, SAV2 is the causative agent of sleeping disease in rainbow trout, and SAV3 has been detected in Atlantic salmon in Norway. The aim of this paper was to summarise current knowledge of infections caused by salmonid alphavirus and diagnostic methods including the newest techniques, and to briefly describe prevention from SAV infections by vaccination.

The epidemiology of pancreas disease in salmonid aquaculture: a summary of the current state of knowledge

Pancreas disease (PD) is a viral disease caused by Salmonid alphavirus (SAV) that affects farmed Atlantic salmon (Salmo salar L.) and rainbow trout (Oncorhynchus mykiss (Walbaum)) in the seawater phase. Since its first description in Scotland in 1976, a large number of studies have been conducted relating to the disease itself and to factors contributing to agent spread and disease occurrence. This paper summarizes the currently available, scientific information on the epidemiology of PD and its associated mitigation and control measures. Available literature shows infected farmed salmonids to be the main reservoir of SAV. Transmission between seawater sites occurs mainly passively by water currents or actively through human activity coupled with inadequate biosecurity measures. All available information suggests that the current fallowing procedures are adequate to prevent agent survival within the environment through the fallowing period and thus that a repeated disease outbreak at the same site is due to a new agent introduction. There has been no scientific evaluation of currently used on-site biosecurity measures, and there is limited information on the impact of available mitigation measures and control strategies.