Comparison of the confidence in freedom from infection based on different control programmes between EU member states: STOC free

• Published in: EFSA supporting publications 2022-04, Vol.19 (4), p.n/a
• Main Authors: Schaik, G., Madouasse, A., Roon, A., Frössling, J., Gethmann, J., Fourichon, C., Mercat, M., More, S., Ågren, E., Sauter‐Louis, C., Gunn, G., Eze, J., Humphry, R., Henry, M., Graham, D., Guelbenzu, M., Nielen, M., Santman‐Berends, I.
• Format: Article
• Language: English
• Subjects: Animal health; Bayesian analysis; Data collection; Diarrhea; Health risks; Infections; Life Sciences; Markov chains; Model testing; Risk analysis; Risk factors; Social factors; Socioeconomic factors; Viruses
• ISSN: 2397-8325; 2397-8325
• DOI: 10.2903/sp.efsa.2022.EN-7263

The STOC free project constructed a generic framework that allows a standardised and harmonised description of different control programmes (CP) for cattle diseases. The STOC free model can be used to determine the confidence of freedom from infection that has been achieved in disease CPs, in support of an ongoing assessment of progress towards output‐based standards as outlined in the EU Animal Health Law. With this information, and as required, further CP actions can be taken to mitigate the risks of persistence and (re‐)introduction on the probability of freedom from infection. Bovine viral diarrhoea virus (BVDV) was chosen as the case disease because of the diversity in CPs in the six participating countries. A Bayesian hidden Markov model was considered the best modelling method. Detailed BVDV CP information was collected in the participating countries and the key aspects for inclusion in the STOC free model were identified. A first version of STOC free model was developed and tested on simulated data. The risk factors for BVDV infection that needed to be included in the model were defined and default values for these risk factors were quantified. A data collection tool was finalised with which the data for the STOC free model was collected. Subsequently, the developed model was tested and validated using real BVDV CP data from partner countries. Based on the feedback, the model was finalised and the report and corresponding computer code were made publicly available. There were roughly three different BVDV situations that occurred in the partner countries: 1. Endemic situation with a CP operating at herd level, 2. Endemic situation with a CP operating at animal level and 3. BVD free situation. The STOC free model is able to include herd level data only and animal level data has to be aggregated to herd level before the model can be applied. The STOC free model is not applicable for a country that is completely BVDV free given that it needs some infections to estimate its parameters and converge. In the latter situation, a scenario tree model could be a better suited tool, and this was evaluated in the Swedish case study. Further work is needed for generalisation of the method to other diseases and expansion of the method to include socioeconomic aspects of CPs.