Abstract
Avian Influenza (AI) is an infectious viral disease transmitted by many species of birds. Some wild water birds (Anseriformes and Charadriiformes in particular) have the role of reservoir for domestic birds (poultry) and humans (zoonosis). This causes great concern in terms of high bird mortality rates and economic losses, and as potential source of human pandemic.
AI viruses are globally widespread. In Italy, many outbreaks are notified every year in the north, while sporadic outbreaks occur in other zones. The aim of this study was to map the suitability level (vulnerability maps) for AI, to address risk-based surveillance and control, often poorly supported by data-driven models. A Spatial Multi Criteria Decision Analysis (MCDA) was used. A list of possible risk factors was identified, based on literature research and 3 experts’ opinions. 11 risk factors have been considered. They can be grouped into 4 categories:
- Domestic birds farms and related facilities: domestic birds farms, backyard farms, ducks or geese farms, free-range farms, farms belonging to North Italy avian industries, manure treatment plants, poultry slaughterhouses
- Wild birds density
- rivers, lakes and other bodies of water
- roads network
All the risk factors were imported as layers in a GIS project and transformed into raster maps, with a common scale/spatial resolution (3x3 km cells). For each risk factor a weight was calculated, following the Analytical Hierarchy Process (using QGis 2.8.8 plugin “Easy AHP”) based on 3 elicited experts’ opinion. Each raster was processed based on a specified fuzzification algorithm (by “Fuzzy membership” in ArcGis® 10.8.1) before their integration in the final map. The range of medium-high risk values (0.49-0.78) covers about the 30% of the total region in Lazio, in particular the inner part of Viterbo province and the coastal area between Latina and Roma. In Toscana, high risk areas are present on the coast and an inner area between Siena and Arezzo.
MCDA can provide a possible knowledge-based approach to support specific health and surveillance measures in a scenario increasingly oriented to a One-Health perspective.
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