Abstract
According to the new Animal Health Law, the surveillance scheme in fish farms, must consider the health status of the farms and the level of risk of introduction and spread of diseases. The classification of the various risk levels requires heterogeneous information including: spatial location, extent and connection to the hydrological network, and the biosecurity measures implemented. Hereby it is presented a method to rank the farms on the basis of their biosecurity, and integrate this information within the hydrological network, with the ultimate aim of informing the local health authority (LHA) of which farms are exposed to greater risk of transmission, once a disease has been detected.
Sixty-two salmonid farms located in the Autonomous Province of Trento, northeastern Italy, were included in the study. Biosecurity information was collected for each farms through checklists already provided in the National Legislation implementing the EU Directive 2006/88/CE. Although the checklist covered several topics, only information on biosecurity were considered in the study. The resulting 39 biosecurity items were scored via expert elicitation, allowing evaluating the importance of single item, while also accounting for the uncertainty potentially related to each of the 10 experts. Finally, to each farm a score derived by the presence/absence of those 39 items was assigned, permitting to rank the premises accordingly to their biosecurity. Data manipulation and elaborations were performed using the statistical software R. The hydrologic network was implemented, and fully validated, within a geo-database using PostGreSQL with PostGIS and PgRouting extensions. Location of farms and their productive data were acquired from the National Livestock Database. The location of connections between farms and rivers, likely water sources, and other points of interest were georeferenced and validated by veterinarians from the LHA. Biosecurity scores were combined with spatial information of the farms to produce thematic maps. Finally, a set of specific Geographic Information System (GIS) tools were implemented to support LHA and decision makers, combining spatial operations and biosecurity scores.
Overall the fish farming sector resulted having high biosecurity scores, with only a farms showing suboptimal of poor conditions (N=2/62, 3.23%). In addition, GIS-oriented functionalities were developed for: (i) spatial operations that consider the digital terrain model (e.g. buffers accounting for the slope); (ii) network operations for selecting farm (e.g.: upstream/downstream) and interrogating their biosecurity, (iii) rapid extraction of territorial information near the farms.
The study represent a starting point for creating of a GIS-based risk assessment tool, for identifying farms at risk of disease transmission, increasing the effectiveness of surveillance and disease response measures. Furthermore, the development dedicated GIS features might be exploited to create informative maps to support decision-making.
This work is funded by Italian Ministry in the National Research IZSVE 12-20 RC “AeGIS”.