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Output-based assessment of herd-level freedom from infection in endemic situations: Application of a Bayesian Hidden Markov model

van Roon, Annika and Madouasse, A. and Toft, N. and Santman-Berends, Inge M. G. A. and Gethmann, J. and Eze, J. and Humphry, R.W. and Graham, David and Guelbenzu, Maria and Nielen, M. and More, S. J. and Mercat, M. and Fourichon, C. and Sauter-Louis, C. and Frossling, J. and Frössling, Jenny and Agren, E. and Gunn, George J. and Gunn, G. J. and Henry, Madeleine K. and van Schaik, Gerdien (2022). Output-based assessment of herd-level freedom from infection in endemic situations: Application of a Bayesian Hidden Markov model. Preventive Veterinary Medicine. 204 , 105662
[Research article]

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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.

Authors/Creators:van Roon, Annika and Madouasse, A. and Toft, N. and Santman-Berends, Inge M. G. A. and Gethmann, J. and Eze, J. and Humphry, R.W. and Graham, David and Guelbenzu, Maria and Nielen, M. and More, S. J. and Mercat, M. and Fourichon, C. and Sauter-Louis, C. and Frossling, J. and Frössling, Jenny and Agren, E. and Gunn, George J. and Gunn, G. J. and Henry, Madeleine K. and van Schaik, Gerdien
Title:Output-based assessment of herd-level freedom from infection in endemic situations: Application of a Bayesian Hidden Markov model
Series Name/Journal:Preventive Veterinary Medicine
Year of publishing :2022
Volume:204
Article number:105662
Number of Pages:9
Publisher:ELSEVIER
ISSN:0167-5877
Language:English
Publication Type:Research article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 403 Veterinary Science > Clinical Science
Keywords:Freedom from infection, Output-based surveillance, Control program, Bovine viral diarrhoea virus
URN:NBN:urn:nbn:se:slu:epsilon-p-117398
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-117398
Additional ID:
Type of IDID
DOI10.1016/j.prevetmed.2022.105662
Web of Science (WoS)000799155700007
ID Code:28325
Faculty:VH - Faculty of Veterinary Medicine and Animal Science
Department:(VH) > Dept. of Animal Environment and Health
Deposited By: SLUpub Connector
Deposited On:13 Jun 2022 11:27
Metadata Last Modified:13 Jun 2022 11:31

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