Good knowledge on the disease situation and its impact on production is a base mechanism for designing health surveillance, risk analysis and biosecurity systems. Mediterranean marine fish farming, as any aquaculture production, is affected by various infectious diseases. However, seabass and seabream, the main produced species, are not listed as susceptible host species for the notifiable pathogens listed in the current EU legislation, which generates a lack of systematic reporting. The results presented in this study come from a survey directly to fish farms (50 hatchery and ongrowing units from 10 Mediterranean countries), with data from 2015‐2017, conducted by the H2020 project MedAID. Seabass showed a higher survival rate (85%) through a production cycle than seabream (80%) in spite of equal mortality due to pathogen infections (10%). The differences in survival may be explained by mortality “of other causes”. Seabream and seabass have different disease profiles, and the profile is slightly different between geographical regions. Among the most important diseases, tenacibaculosis and vibriosis were identified in seabass and Sparicotyle chrysophrii (a gill fluke) and nodavirus in seabream. Correlating mortality data to management variables showed that increasing density, buying fingerlings from external sources, and treatments due to disease are factors that negatively influence mortality rate. Most of the surveyed farms did not keep sufficient quality data to implement good health status reports and perform detailed impact studies, which shows the necessity of updating the current legislative framework to provide the basis for better reporting of relevant pathogens in the Mediterranean basin.

A 12‐point checklist in the design and practical application of active surveillance of diseases in aquatic organisms (farmed and wild) has been developed to serve as a methodological approach and guidance for a multidisciplinary team particularly in countries where surveillance expertise is limited. The checklist is based on a review of available main aquatic surveillance references and scientific literature and was further developed based on the outcomes of several aquaculture biosecurity project‐related workshops hosted by the Food and Agriculture Organization of the United Nations. The checklist includes the following: (1) scenario setting; (2) defining surveillance objective; (3) defining the populations; (4) disease clustering; (5) case definition; (6) diagnostic testing; (7) study design and sampling; (8) data collection and management; (9) data analysis; (10) validation and quality assurance; (11) human and financial resources and logistics requirements; and (12) surveillance in the bigger picture. For a multidisciplinary approach to disease control, knowledge of fish biology, aquaculture systems and many aspects of aquaculture health management are required. Surveillance needs significant financial investment and must be supported by adequate diagnostic capability, information system management, legal framework and communication networks, with transparent reporting mechanisms to allow rapid disease response for serious diseases of aquatic organisms. It is a stepwise and pragmatic approach that offers a good starting point for addressing disease issues especially in developing countries. It can be used as a model to build targeted surveillance competency and a basic reference when implementing a surveillance programme or improving existing programmes.

Influenza A(H1N1)pdm09 virus infection in Norwegian pigs was largely subclinical. This study tested the hypothesis that the infection causes negligible impact on pigs’ growth performance in terms of feed conversion efficiency, daily feed intake, daily growth, age on reaching 100 kg bodyweight and overall feed intake. A sample of 1955 pigs originating from 43 breeding herds was classified into five infection status groups; seronegative pigs (n = 887); seropositive pigs (n = 874); pigs positive for virus at bodyweight between 33 kg and 60 kg (n = 123); pigs positive for virus at bodyweight between 61 kg and 80 kg (n = 34) and pigs positive for virus at bodyweight between 81 kg and 100 kg (n = 37). Each pig had daily recordings of feed intake and bodyweight from 33 kg to 100 kg. Marginal effects of the virus infection on the outcomes were estimated by multi-level linear regression, which accounted for known fixed effects (breed, birthdate, average daily feed intake and growth phase) and random effects (cluster effects of pig and herd). The seropositive and virus positive pigs had decreased (P value<0.05) growth performance compared to seronegative pigs even though feed intake was not decreased. Reduced feed conversion efficiency led to lower average daily growth, additional feed requirement and longer time needed to reach the 100 kg bodyweight. The effects were more marked (P value<0.03) in pigs infected at a younger age and lasted a longer period. Despite increased feed intake observed, their growth rates were lower and they took more time to reach 100 kg bodyweight compared to the seronegative pigs. Our study rejected the null hypothesis that the virus infection had negligible adverse effects on growth performance of Norwegian pigs.

In endemic areas with very low infection prevalence, the frequency and intensity of Echinococcus multilocularis can be extremely low. This necessitates efficient, specific and sensitive molecular tools. We wanted to compare the existing molecular tools, used in the Norwegian national surveillance programme, and compare these with new techniques for detection of this zoonotic pathogen in fox faeces. Here we present the results of screening samples containing a known level of E. multilocularis eggs with two highly sensitive DNA isolation and extraction methods combined with one conventional PCR and three real-time PCR methods for detection.

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.

Farming seabass (Dicentrarchus labrax) is an essential activity in the Mediterranean basin including the Aegean Sea. The main seabass producer is Turkey accounting for 155,151 tons of production in 2021. In this study, skin swabs of seabass farmed in the Aegean Sea were analysed with regard to the isolation and identification of Pseudomonas. Bacterial microbiota of skin samples (n = 96) from 12 fish farms were investigated using next-generation sequencing (NGS) and metabarcoding analysis. The results demonstrated that Proteobacteria was the dominant bacterial phylum in all samples. At the species level, Pseudomonas lundensis was identified in all samples. Pseudomonas, Shewanella, and Flavobacterium were identified using conventional methods and a total of 46 viable (48% of all NGS+) Pseudomonas were isolated in seabass swab samples. Additionally, antibiotic susceptibility was determined according to standards of the European Committee on Antimicrobial Susceptibility Testing (EUCAST) and Clinical and Laboratory Standards Institute (CLSI) in psychrotrophic Pseudomonas. Pseudomonas strains were tested for susceptibility to 11 antibiotics (piperacillin-tazobactam, gentamicin, tobramycin, amikacin, doripenem, meropenem, imipenem, levofloxacin, ciprofloxacin, norfloxacin, and tetracycline) from five different groups of antibiotics (penicillins, aminoglycosides, carbapenems, fluoroquinolones, and tetracyclines). The antibiotics chosen were not specifically linked to usage by the aquaculture industry. According to the EUCAST and CLSI, three and two Pseudomonas strains were found to be resistant to doripenem and imipenem (E-test), respectively. All strains were susceptible to piperacillin-tazobactam, amikacin, levofloxacin, and tetracycline. Our data provide insight into different bacteria that are prevalent in the skin microbiota of seabass sampled from the Aegean Sea in Turkey, and into the antibiotic resistance of psychrotrophic Pseudomonas spp.