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Claes G.,Avian Virology and Immunology Unit | Vangeluwe D.,Royal Belgian Institute Of Natural Sciences | Van Der Stede Y.,Coordination Center for Veterinary Diagnostics | Van Den Berg T.,Avian Virology and Immunology Unit | And 2 more authors.
Avian Diseases | Year: 2012

Wild birds that reside in aquatic environments are the major reservoir of avian influenza viruses (AIVs). Since this reservoir of AIVs forms a constant threat for poultry, many countries have engaged in AIV surveillance. More and more commercial enzyme-linked immunosorbent assays (ELISA) are available for serologic surveillance, but these tests are often developed and validated for use in domestic poultry. However, for a correct interpretation of ELISA test results from wild bird sera, more information is needed. In the present study, four ELISA test kits (ID-Vet IDScreen®, IDEXX FlockChek™ AI MultiS-Screen Ab Test Kit, Synbiotics FluDETECT™BE, and BioChek AIMSp) were compared for the serologic analysis of 172 serum samples from mallard, mute swan, and Canada goose. Samples were selected based on ID-Vet IDScreen results to obtain an approximately equal number of positive and negative samples. In addition, 92 serum samples from experimentally infected specific-pathogen-free (SPF) chickens and Pekin ducks were included in the tests for validation purposes. Cohen's kappa statistics and Spearman correlation coefficients were calculated for each combination of two tests and for each bird species. Test agreement for mallard sera varied from poor to moderate, while test results for Canada goose and swan sera agreed from fair to almost perfect. The best agreement was obtained with sera from experimentally infected SPF chickens and Pekin ducks. This study shows that some care must be taken before using nucleoprotein ELISAs for the testing of sera from wild birds and that more reliable validation studies should be considered before their use in the serologic surveillance of wild birds. Source

Welby S.,Coordination Center for Veterinary Diagnostics | Van Den Berg T.,Avian Virology and Immunology | Marche S.,Avian Virology and Immunology | Houdart P.,Federal Agency for the Safety of the Food Chain | And 2 more authors.
Avian Diseases | Year: 2010

This study was aimed at redesigning the Belgian active surveillance program for domestic birds in professional poultry holdings based on a risk analysis approach. A stochastic quantitative analysis, combining all data sources, was run to obtain sensitivity estimates for the detection of an infected bird in the different risk groups identified. An optimal number of holdings for each risk group was then estimated on the basis of the different sensitivities obtained. This study proved to be a useful tool for decision makers, providing insight on how to reallocate the total amount of samples to be taken in the coming year(s) in Belgium, thus optimizing the field resources and improving efficiency of disease surveillance such as required by the international standards. © 2010 American Association of Avian Pathologists. Source

Boone I.,Coordination Center for Veterinary Diagnostics | Boone I.,University of Liege | Van Der Stede Y.,Coordination Center for Veterinary Diagnostics | Dewulf J.,Ghent University | And 4 more authors.
Journal of Risk Research | Year: 2010

The Numeral Unit Spread Assessment Pedigree (NUSAP) system was implemented to evaluate assumptions in a quantitative microbial risk assessment (QMRA) model for Salmonella spp. in minced pork meat. This QMRA model allows the testing of mitigation strategies for the reduction of human salmonellosis and aims to serve as a basis for science-based policy making. The NUSAP method was used to assess the subjective component of assumptions in the QMRA model by a set of four pedigree criteria: 'the influence of situational limitations', 'plausibility', 'choice space' and 'the agreement among peers'. After identifying 13 key assumptions relevant for the QMRA model, a workshop was organized to assess the importance of these assumptions on the output of the QMRA. The quality of the assumptions was visualized using diagnostic and kite diagrams. The diagnostic diagram pinpointed assumptions with a high degree of subjectivity and a high 'expected influence on the model results' score. Examples of those assumptions that should be dealt with care are the assumptions regarding the concentration of Salmonella on the pig carcass at the beginning of the slaughter process and the assumptions related to the Salmonella prevalence in the slaughter process. The kite diagrams allowed a clear overview of the pedigree scores for each assumption as well as a representation of expert (dis)agreement. The evaluation of the assumptions using the NUSAP system enhanced the debate on the uncertainty and its communication in the results of a QMRA model. It highlighted the model's strong and weak points and was helpful for redesigning critical modules. Since the evaluation of assumptions allows a more critical approach of the QMRA process, it is useful for policy makers as it aims to increase the transparency and acceptance of management decisions based on a QMRA model. Source

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