Modelling the spread of infectious salmon anaemia among salmon farms based on seaway distances between farms and genetic relationships between infectious salmon anaemia virus isolates

Abstract

Infectious salmon anaemia (ISA) is an important infectious disease in Atlantic salmon farming causing recurrent epidemic outbreaks worldwide. The focus of this paper is on tracing the spread of ISA among Norwegian salmon farms. To trace transmission pathways for the ISA virus (ISAV), we use phylogenetic relationships between virus isolates in combination with spacetime data on disease occurrences. The rate of ISA infection of salmon farms is modelled stochastically, where seaway distances between farms and genetic distances between ISAV isolates from infected farms play prominent roles. The model was fitted to data covering all cohorts of farmed salmon and the history of all farms with ISA between 2003 and summer 2009. Both seaway and genetic distances were significantly associated with the rate of ISA infection. The fitted model predicts that the risk of infection from a neighbourhood infectious farm decreases with increasing seaway distance between the two farms. Furthermore, for a given infected farm with a given ISAV genotype, the source of infection is significantly more likely to be ISAV of a small genetic distance than of moderate or large genetic distances. Nearly half of the farms with ISA in the investigated period are predicted to have been infected by an infectious farm in their neighbourhood, whereas the remaining half of the infected farms had unknown sources. For many of the neighbourhood infected farms, it was possible to point out one or a few infectious farms as the most probable sources of infection. This makes it possible to map probable infection pathways.

Low virulent infectious salmon anaemia virus (ISAV-HPR0) is prevalent and geographically structured in Norwegian salmon farming

“…The spatial association between fish groups with virulent and low virulent ISAV supports the hypothesis that ISAV-HPR0 may evolve to virulent ISAV, indicating that there is a risk associated with ISAV-HPR0 infection. If virulent ISAV evolves from ISAV-HPR0 in a given fish group, then neighbouring fish groups are at elevated risk (Aldrin et al 2011. Early detection of ISA outbreaks and implementation of measures to reduce the spread of virulent ISAV thus remain important strategies for controlling ISA.…”

“…In this paper, isolated ISA outbreaks were defined as outbreaks that were geographically and/or genetically isolated from any known source of infectious fish. We used a stochastic model to identify 17 isolated ISA outbreaks from January 2006 to August 2009 (Aldrin et al 2011). For these 17 outbreaks, the estimated probability of infection originating from an adjacent ISA outbreak was less than 10% in the stochastic model, implying that the source of infection was unknown for these outbreaks.…”

“…The local clusters have been explained by horizontal spread of infection between proximate farms, whereas the isolated outbreaks have had an unknown source of infection (Aldrin et al 2011). It has been suggested that ISA may arise from spontaneous transitions of ISAV-HPR0 to virulent ISAV (Cunningham et al 2002, Mjaaland et al 2002, Nylund et al 2003 and that this may cause either solitary outbreaks or local epidemics through local transmission ).…”

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

“…The model is an extended and improved version of a model we have used previously to model both PD and other salmonid diseases (Aldrin et al, 2010(Aldrin et al, , 2011. The most important improvements in the present model are that (i) it is now a fully specified stochastic model that can be used for scenario simulations; (ii) the infection and outbreak processes are modelled separately, which allows for fish cohorts being infected without an outbreak being detected; (iii) the infection intensity, and thus infectiousness, within infected fish cohorts is allowed to vary over time according to a seasonal SIR model; and (iv) the effect of seaway distance is modelled by a two-parametric curve, as opposed to a one-parametric exponential curve that was used previously.…”

“…Several mathematical and statistical models on spatio-temporal dynamics of infectious diseases in farmed host populations have recently been developed and applied to different diseases in livestock, such as foot-and-mouth disease, swine fever, blue-tongue, infectious salmon anaemia and bovine tuberculosis (Keeling et al, 2001;Diggle, 2006;Höhle, 2009;Szmaragd et al, 2009;Aldrin et al, 2011;Brooks-Pollock et al, 2014). In these models, probabilities of infection between farms typically relate to between-farm distance.…”