Data from: Can biosecurity and local network properties predict pathogen species richness in the salmonid industry?

Salmonid farming in Ireland is mostly organic, which implies limited
disease treatment options. This highlights the importance of biosecurity
for preventing the introduction and spread of infectious agents.
Similarly, the effect of local network properties on infection spread
processes has rarely been evaluated. In this paper, we characterized the
biosecurity of salmonid farms in Ireland using a survey, and then
developed a score for benchmarking the disease risk of salmonid farms. The
usefulness and validity of this score, together with farm indegree
(dichotomized as ≤ 1 or > 1), were assessed through generalized
Poisson regression models, in which the modeled outcome was pathogen
richness, defined here as the number of different diseases affecting a
farm during a year. Seawater salmon (SW salmon) farms had the highest
biosecurity scores with a median (interquartile range) of 82.3 (5.4),
followed by freshwater salmon (FW salmon) with 75.2 (8.2), and freshwater
trout (FW trout) farms with 74.8 (4.5). For FW salmon and trout farms, the
top ranked model (in terms of leave-one-out information criteria, looic)
was the null model (looic = 46.1). For SW salmon farms, the best ranking
model was the full model with both predictors and their interaction (looic
= 33.3). Farms with a higher biosecurity score were associated with lower
pathogen richness, and farms with indegree > 1 (i.e. more than one
fish supplier) were associated with increased pathogen richness. The
effect of the interaction between these variables was also important,
showing an antagonistic effect. This would indicate that biosecurity
effectiveness is achieved through a broader perspective on the subject,
which includes a minimization in the number of suppliers and hence in the
possibilities for infection to enter a farm. The work presented here could
be used to elaborate indicators of a farm's disease risk based on its
biosecurity score and indegree, to inform risk-based disease surveillance
and control strategies for private and public stakeholders.