Estimating freedom from infection: Output-based evaluation of BVDV control programmes

Abstract

There has been a slight shift in animal disease surveillance from input-based standards towards output-based standards, meaning that it is not prescribed what needs to be done, but rather what must be achieved. An objective and standardized assessment of the outputs of differently designed control programmes (CPs) is needed and therefore the aim of this thesis was to develop and test a generic output-based framework to determine the probability of freedom from Bovine Viral Diarrhea virus (BVDV) infection in cattle herds. The in this project developed STOC free framework (Surveillance analysis Tool for Outcome-based Comparison of the confidence of FREEdom), consists of a data collection tool (STOC free DATA) and a model to estimate herd-level freedom from infection (STOC free MODEL). Elements of BVDV CPs in six European countries that contribute to confidence of freedom from BVDV were described and qualitatively compared. Many differences in the context and design of BVDV CPs were found. CPs were either mandatory or voluntary, resulting in variation in risks from (in)direct animal contacts. Risk factors such as cattle density and the number of imported cattle varied greatly between countries. Differences were also found in testing protocols and definitions of freedom from infection. A systematic search and meta-analysis of risk factors for the presence of BVDV in cattle herds in Europe was performed to obtain generic estimates that could be used as default input data for STOC free MODEL. Data from 18 observational studies were analyzed by a random effects meta-analysis and significant higher odds were found for dairy herds compared to beef herds, for larger herds, for herds that participated in shows or markets, for herds that introduced cattle and for herds that had contact with cattle of other herds at pasture. STOC free DATA was developed to evaluate data availability and quality and to collect actual input data required for STOC free MODEL. Initially, the tool was developed for assessment of freedom from BVDV in six Western European countries and was then further generalized to enable inclusion of data for other cattle diseases and for use throughout Europe. STOC free DATA includes a wide range of variables that could reasonably influence confidence of freedom from infection, including those relating to cattle demographics, risk factors for introduction and characteristics of CPs. STOC free MODEL, a Bayesian Hidden Markov model, was applied to BVDV field data from CPs based on ear notch samples from newborn calves in four study regions to estimate the probability of herd level freedom from BVDV. The probability of freedom from BVDV for dairy herds that are free according to each study region’s CP was predicted to be very high ranging from 0.98 to 1.00, regardless of the use of default or country-specific priors. The STOC free model can be used to evaluate and improve BVDV CPs and to determine whether they comply with output-based regulations of the EU. The tool is currently evaluated by other research groups for application to other infectious cattle diseases such as Johne’s disease and salmonella.