Modelling Infectious Hematopoietic Necrosis Virus Dispersion from Marine Salmon Farms in the Discovery Islands, British Columbia, Canada

Abstract

Finite volume ocean circulation and particle tracking models are used to simulate water-borne transmission of infectious hematopoietic necrosis virus (IHNV) among Atlantic salmon (Salmo salar) farms in the Discovery Islands region of British Columbia, Canada. Historical simulations for April and July 2010 are carried out to demonstrate the seasonal impact of river discharge, wind, ultra-violet (UV) radiation, and heat flux conditions on near-surface currents, viral dispersion and survival. Numerical particles released from infected farm fish in accordance with IHNV shedding rates estimated through laboratory experiments are dispersed by model oceanic flows. Viral particles are inactivated by ambient UV radiation levels and by the natural microbial community at rates derived through laboratory studies. Viral concentration maps showing temporal and spatial changes are produced and combined with lab-determined minimum infectious dosages to estimate the infective connectivity among farms. Results demonstrate that neighbouring naïve farms can become exposed to IHNV via water-borne transport from an IHNV diseased farm, with a higher risk in April than July, and that many events in the sequence of farm outbreaks in 2001-2002 are consistent with higher risks in our farm connectivity matrix. Applications to other diseases, transfers between farmed and wild fish, and the effect of vaccinations are also discussed.

Transmission potential of infectious hematopoietic necrosis virus in APEX-IHN®-vaccinated Atlantic salmon

“…Numbers in parentheses indicates number of fish with titer. Abbreviations as in Table 2 borne transport of IHNV from diseased sites to downstream sites can occur and can account in part for disease dispersal among neighboring farms (Foreman et al 2015). In addition, there is concern about potential viral spill-back events from farmed populations to wild salmonids, namely sockeye salmon, during their migration through an aquaculture occupied area.…”

Survival of Viruses in Water

“…Few authors have addressed the detection of real viruses in real environments over long periods [50]. Such results would be helpful in assessing the persistence of virus infectivity in the environment [51,52].…”

Lessons from sea louse and salmon epidemiology

“…The 1.5-to 2-year seawater production cycles for farmed salmon add additional variability in host density in coastal regions. [3,59] explain underlying mechanisms [37] project behaviours of systems [20,28,43] predict outcomes of interventions [15,27] raise questions and generate hypotheses [66] propose models [49] present definition functions and goals timescale find possible explanations for patterns and phenomena [18] generate hypotheses [4] specify new data needs [27] [20] regression models -statistical -descriptive/correlational -can include spatial effects GLM, GLMM, random effects, logistic regression -identifying epidemiological factors effecting sea lice abundance on salmon farms [21] -associations between aquaculture and sea louse infections on sea trout [22] survival functions and hazard functions -statistical -descriptive/correlational survival analysis -impacts of sea lice on salmon survival in the NE Atlantic [3] -effects of salinity on sea louse survival on juvenile salmon [23] stochastic processes -discrete-time or continuous-time dynamics [29] rstb.royalsocietypublishing.org Phil. Trans.…”

“…Statistical models have found spatio-temporal correlations between sea louse abundances on farmed and wild salmon up to 30 km apart [41,42]. When coupled with biological models, hydrodynamic simulation models can be used to understand the mechanisms driving these patterns and have been used to characterize transmission of salmon pancreas disease virus among salmon farms in Norway [29], and infectious hematopoietic necrosis virus between salmon farms and wild salmon in Pacific Canada [43]. The contribution of individual farms within a network of farms to regional sea louse connectivity has been quantified using graph theory [36,44] and by merging hydrodynamic modelling of dispersal with classic host-macroparasite models to calculate local and regional thresholds for transmission (table 1) [16].…”