Certifying the French population of Crassostrea gigas free from exotic diseases : a risk analysis approach
|Author(s)||Thebault Anne, Berthe Franck, Audige L.|
|Meeting||OIE Conference, Risk analysis in aquatic animal health|
|Source||Proceedings of an International Conference held, Paris, 8-10 february 2000|
|Keyword(s)||Stochastic modelling, Sensitivity modelling, Risk analysis, Oysters, Molluscs, Crassostrea gigas, Certification, Aquatic animal surveys|
|Abstract||Sample-size calculation in the context of the surveys aimed at substantiating freedom from infection have been commonly undertaken an terrestrial animals over recent years, but not on aquatic animals. A recent model developed by Audigé and Beckett in 1999 can be used to plan and assess animal health surveys. The aim of this study was to adapt that model for marine aquaculture, in particular to help in planning surveys aimed at substantiating freedom form two exotic diseases, mikrocytosis and perkinsosis, in the French population of Crassostrea gigas. As a first approach, farmed animals are so frequent that it would be very difficult to be representative of a single area or zone.
To find the most appropriate sampling scheme, the model was run using @Risk with 1,000 iterations and Latin hypercube sampling for each simulation. Sixty samples from 30 animals within animal clusters were sufficient to detect a cluster prevalence of 10% with 90% confidence, or a prevalence of 20% with more than 95% confidence. Alternatively, 100 samples from 30 animals would be enough to detect 10% of infected clusters with more than 90% confidence.
A sensitivity analysis was conducted to attempt to distinguish between parameter uncertainty and variability. Uncertainty about the sensitivity of the diagnosis test (varying between 50% and 70%) had a major influence on the testing scheme at cluster level, but not much influence at the survey level. This model was very useful in assessing different sampling strategies. However, the model also requires enhancements, such as the availability of more accurate data to confirm the various assumptions made, and being able to take into account other factors, such as the results from past surveys, exchanges and movement of animals and environmental factors.