Aquaculture is one of the world’s fastest growing food production sectors. The outbreak of infectious bacterial diseases in aquaculture is a major concern in the industry. Among the pathogens causing these diseases, species are responsible for several diseases in cultured animals. Models describing the growth and survival of pathogenic spp. populations represent a promising tool for improving predictions of a outbreak. Traditionally, the control of vibriosis involved the indiscriminant use of antibiotics at high concentrations, resulting in negative effects on aquaculture ecosystems and development of antibiotic-resistant pathogens. Thus, there is an urgent need to develop alternative techniques to prevent a and conduct a risk assessment, the concentration of and a range of microbial, physical, and chemical indices were monitored every week for 12 months in a recirculating aquaculture system (RAS) containing Atlantic salmon Monte Carlo simulation was used to model the effects of environmental factorsVibrioThis allowed characterization of the likelihood of harm resulting from the pathogen. In this simulation, uncertainty about the concentration of spp. was modelled with a PERT distribution. Tornado sensitivity analysis, used to compare the relative importance of variables, was conducted in ModelRisk 4.0. (COD), and salinity, and yielded a good fit to the observed data (=0.694). A stepwise multiple regression yielded the following formula: lg( spp. abundance at the sampling site. The predicted The predicted time of peak abundance was also consistent with the observed values. model failed to estimate the concentration of model deviated significantly from the observed values. These observations suggest that the occurrence of in seawater. The Monte Carlo simulation revealed that COD was strongly correlated with the concentration of spp..suggesting that spp. in In contrast, the rank correlation output was much lower for , which indicates that the probability of pathogen outbreaks and growth was less sensitive to those variables. reachlead to an outbreak of a disease. offers an easily applied tool that can be used by aquaculture farms to identify the risk factors associated withIn the RAS used in the current study, the majority of excess nutrients are derived from feed or runoff, some of which are converted into fish and feces. The levels of COD generally undergo a dramatic change after every feeding, spp. populations. The use of multiple regression analysis made it possible to calculate the threshold value for COD and other variables on the incidence of associated pathogen events. Because of the range of requirements for cultured animals, the risk factors may vary and include factors. tion for researchers and managers of the aquaculture farm to predict the abundance of species in the system.