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Objective: The primary objective was to construct a time series model for the abundance of the adult female (AF) sea lice Lepeophtheirus salmonis in Atlantic Salmon Salmo salar farms in the Bay of Fundy, New Brunswick, Canada, for the period 2016–2021 and to illustrate its short‐term predictive capabilities. Methods: Sea lice are routinely counted for monitoring purposes, and these data are recorded in the Fish‐iTrends database. A multivariable autoregressive linear mixed‐effects model (second‐order autoregressive structure) was generated with the outcome of the abundance of AF sea lice and included treatments, infestation pressures (a measure that represents the dose of exposure of sea louse parasitic stages to potential fish hosts) within sites (internal) and among sites (external), and other predictors. The treatments were categorized by duration and type. Result: The effect of mechanical treatments decreased with increasing sea surface temperature. In‐sample predictions had good accuracy. A one‐standard‐deviation increase in the external infestation pressures (EIPAF) produced a significant relative increase in the abundance of AF sea lice by 5% when other model predictors were kept constant. Sites separated by short seaway distances had stronger EIPAF than sites with more considerable distances. Conclusion: This model may be helpful for managers and farmers in implementing sea lice mitigation strategies on salmon farms in the Bay of Fundy. Impact statementIn the Bay of Fundy Atlantic Salmon farms, infestation pressures of sea lice within and among farms play an important role in the abundance of adult female sea lice. The predictive model provides a framework for short‐term predictions of sea lice. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

An incursion of infectious salmon anaemia virus (ISAV) was detected in 2020 in southern Newfoundland, Canada. This resulted in an outbreak affecting four marine farms stocking Atlantic salmon (Salmo salar L.) vaccinated against ISAV. This study provides the first description of epidemiologic characteristics of an ISAV outbreak in 2020 and 2021, and detected ISAV variants at the population level. Fish kidneys were screened for ISAV by real‐time RT‐PCR and non‐negative samples were submitted for genotyping and further diagnostic testing. Nine distinct ISAV variants were identified: five European and three North American (NA) HPRΔ ISAV, and one NA‐HPR0 ISAV variant. A notable finding was the concurrent detection of both an HPR0 and an HPRΔ ISAV variant in one individual fish. In two farms, both European and NA variants were simultaneously detected, while in the other two farms either NA or European variants were identified, but not both together. Generally, mortality increases followed rises in ISAV prevalence and cycle threshold values on RT‐PCR decreased with time. Epidemiologic descriptions of ISAV outbreaks in Atlantic Canada contributes to the understanding of local disease dynamics and identification of changes thereof. Such insights are essential for the strengthening of disease management plans. [ABSTRACT FROM AUTHOR]

The objective of this study was to estimate the impact of infestation pressures on the abundance of the parasitic sea louse, Lepeophtheirus salmonis, in the Bay of Fundy, New Brunswick (NB), Canada, using the Fish‐iTrends database for the years 2009–2018. Infestation pressures were calculated as time‐lagged weighted averages of the abundance of adult female (AF) sea lice within a site (internal infestation pressure: IIP) and among sites (external infestation pressure: EIP). The EIP weights were calculated from seaway distances among sites and a Gaussian kernel density for bandwidths of 5 to 60 km. The EIP with a bandwidth of 10 km had the best fit, as determined with Akaike's information criterion, and historical AF sea lice abundance. This estimated dispersal distance of 10 km was similar to previous studies in Norway, Scotland and in New Brunswick. The infestation pressures estimated from empirical AF sea lice abundance within and among sites significantly increased the abundance of AF sea lice (p <.001). This study concludes that sea lice burdens within Atlantic salmon farms in the Bay of Fundy, NB, are affected by within site management and could be improved by synchronizing treatments between sites. [ABSTRACT FROM AUTHOR]

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.

Connectivity in an aquatic setting is determined by a combination of hydrodynamic circulation and the biology of the organisms driving linkages. These complex processes can be simulated in coupled biological-physical models. The physical model refers to an underlying circulation model defined by spatially-explicit nodes, often incorporating a particle-tracking model. The particles can then be given biological parameters or behaviors (such as maturity and/or survivability rates, diel vertical migrations, avoidance, or seeking behaviors). The output of the bio-physical models can then be used to quantify connectivity among the nodes emitting and/or receiving the particles. Here we propose a method that makes use of kernel density estimation (KDE) on the output of a particle-tracking model, to quantify the infection or infestation pressure (IP) that each node causes on the surrounding area. Because IP is the product of both exposure time and the concentration of infectious agent particles, using KDE (which also combine elements of time and space), more accurately captures IP. This method is especially useful for those interested in infectious agent networks, a situation where IP is a superior measure of connectivity than the probability of particles from each node reaching other nodes. Here we illustrate the method by modeling the connectivity of salmon farms via sea lice larvae in the Broughton Archipelago, British Columbia, Canada. Analysis revealed evidence of two sub-networks of farms connected via a single farm, and evidence that the highest IP from a given emitting farm was often tens of kilometers or more away from that farm. We also classified farms as net emitters, receivers, or balanced, based on their structural role within the network. By better understanding how these salmon farms are connected to each other via their sea lice larvae, we can effectively focus management efforts to minimize the spread of sea lice between farms, advise on future site locations and coordinated treatment efforts, and minimize any impact of farms on juvenile wild salmon. The method has wide applicability for any system where capturing infectious agent networks can provide useful guidance for management or preventative planning decisions.

The objectives of this study were to determine statewide production losses caused by diseases, identify the most incident catfish diseases affecting Alabama (AL) farm-raised catfish production in freshwater ponds and their annual pattern, and determine disease-related economic impacts. The Alabama Fish Farming Center (AFFC) conducted seven annual retrospective questionnaire surveys of AL catfish farmers since 2015 to assess the epidemiological situation of farm-raised catfish fish diseases and their economic impacts. Across the study (2015–2021), the annual number of survey respondents ranged from 64 to 74, with a total of 482 respondents. The yearly survey response rate ranged from 95.6 to 100% (mean: 98.2%). The annual survey coverage area ranged from 6410 to 7006 ha/year. About 97% of survey coverage area reported fish losses caused by a disease. Fish diseases resulted in the loss of 9 million fish/year (2.9 million kg/year; 436.7 kg/ha/year). Bacterial diseases resulted in >83% of catfish disease losses (>5 times losses to non-bacterial causes). The most incident statewide catfish disease was hypervirulent Aeromonas hydrophila (225 kg/ha/year), followed by columnaris (140 kg/ha/year), followed by Edwardsiella (52 kg/ha/year). The temporal epidemiological analysis results indicate that annual trends of proliferative gill disease and Bolbophorus disease were correlated with prolonged precipitation periods and total quantity of rainfall, respectively. Economic losses caused by fish losses to bacterial diseases, non-bacterial diseases, production losses due to reduced feeding during outbreaks, and expenditures associated with catfish diseases were responsible for 54.5%, 10.8%, 22.0%, and 12.7% of total direct economic impacts attributed to AL farm-raised catfish industry, respectively. Total disease-related financial losses in west AL were 11.1 million USD/year (1651 USD/ha/year), representing about 9.5% of food-size catfish sales in AL. The present study provides the first extensive assessment of epidemiology and health economics of farm-raised catfish disease in the freshwater pond environment for a substantial duration. More studies focusing on disease losses at the county- and farm-level would provide useful information regarding the spatial epidemiology of diseases and their underlying risk factors. • This is the first study of substantial duration which examines farm-raised catfish disease-related economic losses in U.S. • Statewide temporal epidemiological trends of bacterial and non-bacterial catfish diseases in AL, USA were determined. • Annual trends of fish losses to hypervirulent Aeromonas hydrophila (vAh) were correlated with high max. daily temperatures. • The most incident statewide catfish disease loss was vAh (225 kg/ha/year), followed by columnaris (140 kg/ha/year). • Disease-related financial losses in west AL were 1651 USD/ha/year, representing 9.5% of food-size catfish sales in AL. [ABSTRACT FROM AUTHOR]

Using Ostreid herpesvirus-1 (OsHV-1) as a case study, this paper considers a survey design methodology for an aquatic animal pathogen that incorporates the concept of biologically independent epidemiological units. Hydrodynamically-modelled epidemiological units are used to divide marine areas into sensible sampling units for detection surveys of waterborne diseases. In the aquatic environment it is difficult to manage disease at the animal level, hence management practices are often aimed at a group of animals sharing a similar risk. Using epidemiological units is a way to define these groups, based on a similar level of probability of exposure based on the modelled potential spread of a viral particle via coastal currents, that can help inform management decisions. [ABSTRACT FROM AUTHOR]

Background: In humans, Streptococcus agalactiae or group B streptococcus (GBS) is a frequent coloniser of the rectovaginal tract, a major cause of neonatal infectious disease and an emerging cause of disease in non-pregnant adults. In addition, Streptococcus agalactiae causes invasive disease in fish, compromising food security and posing a zoonotic hazard. We studied the molecular epidemiology of S. agalactiae in fish and other aquatic species to assess potential for pathogen transmission between aquatic species and humans. Methods: Isolates from fish (n = 26), seals (n = 6), a dolphin and a frog were characterized by pulsed-field gel electrophoresis, multilocus sequence typing and standardized 3-set genotyping, i.e. molecular serotyping and profiling of surface protein genes and mobile genetic elements. Results: Four subpopulations of S. agalactiae were identified among aquatic isolates. Sequence type (ST) 283 serotype III-4 and its novel single locus variant ST491 were detected in fish from Southeast Asia and shared a 3-set genotype identical to that of an emerging ST283 clone associated with invasive disease of adult humans in Asia. The human pathogenic strain ST7 serotype Ia was also detected in fish from Asia. ST23 serotype Ia, a subpopulation that is normally associated with human carriage, was found in all grey seals, suggesting that human effluent may contribute to microbial pollution of surface water and exposure of sea mammals to human pathogens. The final subpopulation consisted of non-haemolytic ST260 and ST261 serotype Ib isolates, which belong to a fish-associated clonal complex that has never been reported from humans. Conclusions: The apparent association of the four subpopulations of S. agalactiae with specific groups of host species suggests that some strains of aquatic S. agalactiae may present a zoonotic or anthroponotic hazard. Furthermore, it provides a rational framework for exploration of pathogenesis and host-associated genome content of S. agalactiae strains. [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]

Background: 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. Results: 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. Conclusions: The framework of the study could be applied for spatial studies of other infectious aquatic animal diseases. [ABSTRACT FROM AUTHOR]

Abstract: Diseases are among the greatest threats affecting the sustainability of shrimp aquaculture. In Ecuador, diseases of cultured shrimp have been quickly transmitted from one region to another. Therefore, an early detection system of impending epidemics could serve as an important management tool for the aquaculture sector. We developed a system for the early detection of shrimp epidemics for the largest shrimp zone of Ecuador based on production surveillance. The system, called Epidemiological Alert System and Aquaculture Management (SAEMA), uses a geographical information system (GIS) with an imaginary grid cartography (12,860 ha per grid) dividing the study area. A production and management index is calculated with the harvest data of each pond. A standardized deviation around the historical averages and an alert level is calculated per grid and month. Normal conditions of production and therefore the absence of disease are depicted in green and yellow. While, orange and red colours express a disease warning manifested through suboptimal production levels. As a result, a map of the study area with grid divisions is displayed, with a specific alert colour for each grid where information is available. SAEMA was developed as a Web application (http://www.saema.espol.edu.ec) that enables producers to record data via a worksheet format using any web browser. Instantaneously, the applications perform a calculation of the alert index and provide feedback to the alert levels displayed in an interactive map. A feedback process was initiated in May 2006 with 19 participating shrimp farms. The objective of this research is to develop a platform for an early detection of shrimp epidemics on a regional scale. The detection of an epidemic, expressed as suboptimal production in a specific region, can provide producers from other zones and government authorities to engage in time preventive and control measures in order to reduce the spread of diseases. [Copyright &y& Elsevier]

Most lice infestations on salmon farmed in the North Atlantic are attributed to Caligus elongatus and Lepeophtheirus salmonis. This study reports findings from time-series analysis conducted on observations of the lesser-studied C. elongatus, in four farms on the west coast of Scotland over the period 1996–2000. Least-squares and Poisson regression techniques were applied to one individual site and to the aggregated data of four sites. Models were fitted and tested for goodness of fit using appropriate statistical methods. Findings indicate that infestation levels are highly seasonal with rapidly increasing numbers after week 22 of the year followed by a steady decline from week 40. Abundance is much lower in the second year of the production cycle than the first. Neither of the models indicated that treatment application has a significant effect on C. elongatus infestations. [ABSTRACT FROM AUTHOR]

White spot disease (WSD) is caused by white spot syndrome virus (WSSV) and is an acutely fatal pandemic disease of crustaceans. It has resulted in massive losses to the shrimp-farming industry in Asia and has now spread to the Americas. This paper reports the problems and solutions associated with the design and execution of a longitudinal epidemiological study of shrimp (Penaeus monodon) health on farms practising a crop rotation of rice and shrimp in the Mekong Delta of Vietnam. The pre-sampling phase of the project involved selecting an appropriate site and sampling variables, obtaining permission and establishing the necessary laboratory and logistic facilities. At the start of the sampling phase, 40 farmers were selected and 32 of these were visited and interviewed. This resulted in the enrolment of only 17 farmers. A further seven had to be enrolled to obtain the maximum number of farmers that could be sampled by the study team. Compliance was enhanced through meetings, regular visits by senior members of the project team and ensuring that visits were punctual and that all information was treated confidentially. The production cycle began in January 1998 and lasted for approximately 5 months. An attempt was made to collect 500 post larvae (PL) before each pond was stocked to assess the health of the batch and to test for the presence of WSSV by one-step PCR. After stocking, the wild crustaceans also were sampled from the pond for PCR analyses. Information was collected on the management practices and samples of water, pond bottom, feed and shrimp collected throughout the production cycle. Water quality variables with predictable diurnal variation were sampled in the morning and afternoon, twice a week. Two months after stocking, the first outbreak of WSD occurred; subsequently, 18 farms conducted a complete emergency harvest due to the actual or perceived presence of a WSD outbreak. Detectable mortalities were reported from 19 farms, and moribund shrimps were collected from four of these for PCR and histological analyses. [Copyright &y& Elsevier]

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. • We have simulated the risk of transmission of Pancreas Disease to neighbouring farms in case of outbreaks in naïve population. • Mitigation of disease spread is dependent on immediate action; delayed stamping-out leads to disease spread. • Risk of transmission depends on the number of fish at index and neighbouring farms and seaway distance between farms. • Time and place of the outbreak has a large influence on the risk of transmission. • Realtime simulations like this can be used for control options for management of infectious diseases in marine aquaculture. [ABSTRACT FROM AUTHOR]