Bayesian estimation of diagnostic sensitivity and specificity of a Nervous Necrosis Virus antibody ELISA

Jaramillo, D; Dürr, Salome Esther; Hick, P; Whittington, R (2016). Bayesian estimation of diagnostic sensitivity and specificity of a Nervous Necrosis Virus antibody ELISA. Preventive veterinary medicine, 123, pp. 138-142. Elsevier 10.1016/j.prevetmed.2015.11.022

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Diagnosis of nervous necrosis virus (NNV) infection in susceptible fish species is mostly performed post-mortem due to the neurotropism of the causative agent and the only validated diagnostic assays require samples from brain and retinal tissue. However, a non-lethal alternative to test for exposure of fish to NNV is needed. An indirect ELISA for the detection of anti-NNV antibodies in was recently developed and evaluated to detect responses in the sera from immunized fish. For this study, we assessed the accuracy of the assay at detecting specific antibodies from naturally exposed fish using field samples from populations with differing infection status. We applied a Bayesian model, using RTqPCR as a second test. Median estimates of the diagnostic sensitivity and specificity of the VNN ELISA were 81.8% and 86.7%, respectively. We concluded that the assay was fit for the purpose of identifying animals in naturally exposed populations. With further evaluation in larger populations the test might be used to inform implementation of control measures, and for estimating infection prevalence to facilitate risk analysis. To our knowledge this is the first report on the diagnostic accuracy of an antibody ELISA for an infectious disease in finfish.

Item Type:

Journal Article (Original Article)

Division/Institute:

05 Veterinary Medicine > Research Foci > Veterinary Public Health / Herd Health Management
05 Veterinary Medicine > Department of Clinical Research and Veterinary Public Health (DCR-VPH) > Veterinary Public Health Institute
05 Veterinary Medicine > Department of Clinical Research and Veterinary Public Health (DCR-VPH)

UniBE Contributor:

Dürr, Salome Esther

Subjects:

600 Technology > 630 Agriculture

ISSN:

0167-5877

Publisher:

Elsevier

Language:

English

Submitter:

Susanne Agnes Lerch

Date Deposited:

21 Jul 2017 12:39

Last Modified:

05 Dec 2022 15:03

Publisher DOI:

10.1016/j.prevetmed.2015.11.022

PubMed ID:

26702588

BORIS DOI:

10.7892/boris.96239

URI:

https://boris.unibe.ch/id/eprint/96239

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