Certifying the French population of Crassostrea ...

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Section title Certifying the French population of Crassostrea gigas free from exotic diseases: a risk analysis approach
Section author(s) A. Thebault, F. C. J. Berthe, L. Audige
Book editor(s) C.J. Rodgers
Date 2001
Abstract Sample-size calculations in the context of surveys aimed at substantiating freedom from infection have been commonly undertaken on terrestrial animals over recent years, but not on aquatic animals. A recent model developed by Audige 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 from two exotic diseases, mikrocytosis and perkinsosis, in the French population of Crassostrea gigas. As a first approach, farmed animals were targeted without dividing the French coast into different zones, since the movement and mixing of 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 at Risk with 1000 iterations and Latin hypercube sampling for each simulation. 60 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..

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