Space-time modelling of the spread of salmon lice between and within Norwegian marine salmon farms.

Parasitic salmon lice are potentially harmful to salmonid hosts and farm produced lice pose a threat to wild salmonids. To control salmon lice infections in Norwegian salmonid farming, numbers of lice are regularly counted and lice abundance is reported from all salmonid farms every month. We have developed a stochastic space-time model where monthly lice abundance is modelled simultaneously for all farms. The set of farms is regarded as a network where the degree of contact between farms depends on their seaway distance. The expected lice abundance at each farm is modelled as a function of i) lice abundance in previous months at the same farm, ii) at neighbourhood farms, and iii) other, unspecified sources. In addition, the model includes explanatory variables such as seawater temperature and farm-numbers of fish. The model gives insight into factors that affect salmon lice abundance and contributing sources of infection. New findings in this study were that 66% of the expected salmon lice abundance was attributed to infection within farms, 28% was attributed to infection from neighbourhood farms and 6% to non-specified sources of infection. Furthermore, we present the relative risk of infection between neighbourhood farms as a function of seaway distance, which can be viewed as a between farm transmission kernel for salmon lice. The present modelling framework lays the foundation for development of future scenario simulation tools for examining the spread and abundance of salmon lice on farmed salmonids under different control regimes.

A stochastic model for the assessment of the transmission pathways of heart and skeleton muscle inflammation, pancreas disease and infectious salmon anaemia in marine fish farms in Norway.

Salmon farming is threatened, economically and ecologically, by infectious diseases. To reduce the risk of epidemics, authorities have developed regulations. These are based on quantitative understanding of pathways of infection, representing disease specific risks. A stochastic model was fitted to historical data, to estimate risk factors associated with competing spread mechanisms. Three infectious diseases were compared, heart and skeletal muscle inflammation (HSMI), pancreas disease (PD) and infectious salmon anaemia (ISA). This study was based on space-time data, from Norway from 2003 to 2007, describing the susceptible fish cohorts and the reported infections. Particular interest was given to seaway distances between farms and their local management organisation. The parameter measuring the effect of distance to an infectious fish farm was positive and significant for all diseases, implying that the risk involved with proximate infectious fish farms increased with decreasing distance. For HSMI and PD there was a significant effect of sharing a contact network with an infectious farm. For HSMI, but not for PD or ISA, there was a significant effect of previous infected cohorts on the same farm. The relative contribution of each transmission pathway was dominated by seaway distance for PD and HSMI, while other non-defined pathways dominated for ISA. This comparative study highlights that the three diseases have different patterns of spread, with important consequences for disease prevention and management.