A framework for assessing the confidence in freedom from infection in animal disease control programmes

Gerdien van Schaik*, Aurélien Madouasse, Annika M van Roon, Simon J More, David A Graham, Jenny Frossling, Jörn Gethmann, Christine Fourichon, Mathilde Mercat, Estelle Agren, Carola Sauter-Louis, GJ Gunn, JI Eze, RW Humphry, MK Henry, Maria Guelbenzu, Mirjam Nielen, Inge MGA Santman-Berends

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

In the Surveillance Tool for Outcome-based Comparison of FREEdom from infection (STOC free) project (https://www.stocfree.eu), a data collection tool was constructed to facilitate standardised collection of input data, and a model was developed to allow a standardised and harmonised comparison of the outputs of different control programmes (CPs) for cattle diseases. The STOC free model can be used to evaluate the probability of freedom from infection for herds in CPs and to determine whether these CPs comply with the European Union's pre-defined output-based standards. Bovine viral diarrhoea virus (BVDV) was chosen as the case disease for this project because of the diversity in CPs in the six participating countries. Detailed BVDV CP and risk factor information was collected using the data collection tool. For inclusion of the data in the STOC free model, key aspects and default values were quantified. A Bayesian hidden Markov model was deemed appropriate, and a model was developed for BVDV CPs. The model was tested and validated using real BVDV CP data from partner countries, and corresponding computer code was made publicly available. The STOC free model focuses on herd-level data, although that animal-level data can be included after aggregation to herd level. The STOC free model is applicable to diseases that are endemic, given that it needs the presence of some infection to estimate parameters and enable convergence. In countries where infection-free status has been achieved, a scenario tree model could be a better suited tool. Further work is recommended to generalise the STOC free model to other diseases.

Original languageEnglish
Pages (from-to)210-217
Number of pages8
JournalOIE Revue Scientifique et Technique
Volume42
Early online date30 May 2023
DOIs
Publication statusFirst published - 30 May 2023

Keywords

  • Cattle
  • Animals
  • Bovine Virus Diarrhea-Mucosal Disease/epidemiology
  • Bayes Theorem
  • Cattle Diseases/epidemiology
  • Risk Factors
  • Diarrhea Viruses, Bovine Viral
  • Freedom
  • Bovine viral diarrhoea virus
  • Control programmes
  • Probability of freedom from infection
  • Output-based surveillance

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