This WP will describe spatial and temporal dynamics of fish transportation and characterize the contact network patterns among the fish holdings based on the transportations, characterize infection-inducing contact patterns, identify the highly connected sites, and elucidate implications of the contact pattern on controlling di
sease spread.
Social network analysis (SNA) will be the approach used to characterize the contact pattern on the basis of fish transportation, and then identify highly connected sites and areas to be targeted for surveillance and control programs. The degree centrality (
number of incoming and outgoing contacts that a site has) and closeness centrality (how closely connected each site is to all other sites within the network) will be estimated for each site in the contact network (Koschutzki et al., 2005; Dube et al 2009; Martinez-Lopez et al., 2009b). The centrality measures will subsequently be used to identify the sites at potential highest risk of receiving and/or transmitting infections within the network, and which sites, areas and time periods may play a key role for disease introduction and spread in Norwegian fish farming industry.
The working hypothesis is that “central” Norwegian fish farms (i.e., farms with high values for centrality measures) and network structure (i.e., relationship between different farms or groups of farms) have a strong influence on the vulnerability or risk on introduction and spread of diseases.
Additionally, we will use exponential random graph models (ERGM) to identify which node attributes and network structural properties influence the formation of an observed contact or, in other words, what is the probability that a movement between two sites occur given the properties and characteristics of those sites. This is useful for prediction of “future” contact patterns and ultimately, will allow the better prevention and control of diseases and the implementation of risk-reduction measures on a farming site.”
