Using scenario tree modelling for targeted herd sampling to substantiate freedom from diseaseReport as inadecuate




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BMC Veterinary Research

, 7:49

First Online: 16 August 2011Received: 28 December 2010Accepted: 16 August 2011

Abstract

BackgroundIn order to optimise the cost-effectiveness of active surveillance to substantiate freedom from disease, a new approach using targeted sampling of farms was developed and applied on the example of infectious bovine rhinotracheitis IBR and enzootic bovine leucosis EBL in Switzerland. Relevant risk factors RF for the introduction of IBR and EBL into Swiss cattle farms were identified and their relative risks defined based on literature review and expert opinions. A quantitative model based on the scenario tree method was subsequently used to calculate the required sample size of a targeted sampling approach TS for a given sensitivity. We compared the sample size with that of a stratified random sample sRS with regard to efficiency.

ResultsThe required sample sizes to substantiate disease freedom were 1,241 farms for IBR and 1,750 farms for EBL to detect 0.2% herd prevalence with 99% sensitivity. Using conventional sRS, the required sample sizes were 2,259 farms for IBR and 2,243 for EBL. Considering the additional administrative expenses required for the planning of TS, the risk-based approach was still more cost-effective than a sRS 40% reduction on the full survey costs for IBR and 8% for EBL due to the considerable reduction in sample size.

ConclusionsAs the model depends on RF selected through literature review and was parameterised with values estimated by experts, it is subject to some degree of uncertainty. Nevertheless, this approach provides the veterinary authorities with a promising tool for future cost-effective sampling designs.

Electronic supplementary materialThe online version of this article doi:10.1186-1746-6148-7-49 contains supplementary material, which is available to authorized users.

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Author: Sarah Blickenstorfer - Heinzpeter Schwermer - Monika Engels - Martin Reist - Marcus G Doherr - Daniela C Hadorn

Source: https://link.springer.com/







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