Output-based Assessment of Herd-level Freedom From Infection in Endemic Situations: Application of a Bayesian Hidden Markov Model

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Title: Output-based Assessment of Herd-level Freedom From Infection in Endemic Situations: Application of a Bayesian Hidden Markov Model
Authors: Roon, Annika M. VanMadouasse, AurélienToft, N.More, Simon Johnet al.
Permanent link: http://hdl.handle.net/10197/13000
Date: Jul-2022
Online since: 2022-07-12T11:59:09Z
Abstract: Countries have implemented control programmes (CPs) for cattle diseases such as bovine viral diarrhoea virus (BVDV) that are tailored to each country-specific situation. Practical methods are needed to assess the output of these CPs in terms of the confidence of freedom from infection that is achieved. As part of the STOC free project, a Bayesian Hidden Markov model was developed, called STOC free model, to estimate the probability of infection at herd-level. In the current study, the STOC free model was applied to BVDV field data in four study regions, from CPs based on ear notch samples. The aim of this study was to estimate the probability of herd-level freedom from BVDV in regions that are not (yet) free. We additionally evaluated the sensitivity of the parameter estimates and predicted probabilities of freedom to the prior distributions for the different model parameters. First, default priors were used in the model to enable comparison of model outputs between study regions. Thereafter, country-specific priors based on expert opinion or historical data were used in the model, to study the influence of the priors on the results and to obtain country-specific estimates. The STOC free model calculates a posterior value for the model parameters (e.g. herd-level test sensitivity and specificity, probability of introduction of infection) and a predicted probability of infection. The probability of freedom from infection was computed as one minus the probability of infection. For dairy herds that were considered free from infection within their own CP, the predicted probabilities of freedom were very high for all study regions ranging from 0.98 to 1.00, regardless of the use of default or country-specific priors. The priors did have more influence on two of the model parameters, herd-level sensitivity and the probability of remaining infected, due to the low prevalence and incidence of BVDV in the study regions. The advantage of STOC free model compared to scenario tree modelling, the reference method, is that actual data from the CP can be used and estimates are easily updated when new data becomes available.
Funding Details: European Food Safety Authority (EFSA)
Dutch Ministry of Agriculture, Nature and Food Quality
Type of material: Journal Article
Publisher: Elsevier
Journal: Preventive Veterinary Medicine
Volume: 204
Start page: 1
End page: 9
Copyright (published version): 2022 The Authors
Keywords: Freedom from infectionOutput-based surveillanceControl programBovine viral diarrhoea virus
DOI: 10.1016/j.prevetmed.2022.105662
Language: en
Status of Item: Peer reviewed
ISSN: 0167-5877
This item is made available under a Creative Commons License: https://creativecommons.org/licenses/by/3.0/ie/
Appears in Collections:Veterinary Medicine Research Collection
CVERA Research Collection

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