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Research article - Peer-reviewed, 2022

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

van Roon, A. M.; Madouasse, A.; Toft, N.; Santman-Berends, I. M. G. A.; Gethmann, J.; Eze, J.; Humphry, R. W.; Graham, D.; Guelbenzu-Gonzalo, M.; Nielen, M.; More, S. J.; Mercat, M.; Fourichon, C.; Sauter-Louis, C.; Frossling, J.; Agren, E.; Gunn, G. J.; Henry, M. K.; van Schaik, G.

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.

Keywords

Freedom from infection; Output-based surveillance; Control program; Bovine viral diarrhoea virus

Published in

Preventive Veterinary Medicine
2022, volume: 204, article number: 105662
Publisher: ELSEVIER

Authors' information

van Roon, Annika
Utrecht University
Madouasse, A.
Nantes Universite
Toft, N.
IQinAbox ApS
Santman-Berends, Inge M. G. A.
Royal GD
Gethmann, J.
Friedrich Loeffler Institute
Eze, J.
Scotland's Rural College (SRUC)
Eze, J.
Biomathematics & Statistics Scotland
Humphry, R.W.
Scotland's Rural College (SRUC)
Graham, David
Anim Hlth Ireland
Guelbenzu, Maria
Anim Hlth Ireland
Nielen, M.
Utrecht University
More, S. J.
University College Dublin
Mercat, M.
INRAE
Fourichon, C.
Nantes Universite
Sauter-Louis, C.
Friedrich Loeffler Institute
Frossling, J. (Frössling, Jenny)
National Veterinary Institute SVA
Swedish University of Agricultural Sciences, Department of Animal Environment and Health
Agren, E.
National Veterinary Institute SVA
Gunn, George J.
Scotland's Rural College
Gunn, G. J.
University of Edinburgh

UKÄ Subject classification

Clinical Science

Publication Identifiers

DOI: https://doi.org/10.1016/j.prevetmed.2022.105662

URI (permanent link to this page)

https://res.slu.se/id/publ/117398