A living lab approach to understanding dairy farmers' needs of technologies and data to improve herd health: Focus groups from 6 European countries.

For successful development and adoption of technology on dairy farms, farmers need to be included in the innovation process. However, the design of agricultural technologies usually takes a top-down approach with little involvement of end-users at the early stages. Living Labs offer a methodology that involve end-users throughout the development process and emphasize the importance of understanding users' needs. Currently, exploration of dairy farmers' needs of technologies has been limited to specific types of technology (e.g., smartphone apps) and adult cattle. The aim of this study was to use a Living Lab approach to identify dairy farmers' needs of data and technologies to improve herd health and inform innovation development. Eighteen focus groups were conducted with, in total, 80 dairy farmers from Belgium, Ireland, the Netherlands, Norway, Sweden, and the UK. Data were analyzed using Template Analysis and 6 themes were generated which represented the fundamental needs of autonomy, comfort, competence, community and relatedness, purpose, and security. Farmers favored technologies that provided them with convenience, facilitated their knowledge and understanding of problems on farm, and allowed them to be self-reliant. Issues with data sharing and accessibility, and usability of software were barriers to technology use. Furthermore, farmers were facing problems around recruitment and management of labor and needed ways to reduce stress. Controlling aspects of the barn environment, such as air quality, hygiene, and stocking density, was a particular concern in relation to youngstock management. In conclusion, the findings suggest that developers of farm technologies may want to include farmers in the design process to ensure a positive user experience and improve accessibility. The needs identified in this study can be used as a framework when designing farm technologies to strengthen need satisfaction and reduce any potential harm toward needs.

Quantification of risk factors for bovine viral diarrhea virus in cattle herds: A systematic search and meta-analysis of observational studies.

Bovine viral diarrhea virus (BVDV) is endemic in many parts of the world, and multiple countries have implemented surveillance activities for disease control or eradication. In such control programs, the disease-free status can be compromised by factors that pose risks for introduction or persistence of the virus. The aim of the present study was to gain a comprehensive overview of possible risk factors for BVDV infection in cattle herds in Europe and to assess their importance. Papers that considered risk factors for BVDV infection in cattle were identified through a systematic search. Further selection of papers eligible for quantitative analysis was performed using a predefined checklist, including (1) appropriate region (i.e., studies performed in Europe), (2) representativeness of the study population, (3) quality of statistical analysis, and (4) availability of sufficient quantitative data. In total, 18 observational studies were selected. Data were analyzed by a random-effects meta-analysis to obtain pooled estimates of the odds of BVDV infection. Meta-analyses were performed on 6 risk factors: herd type, herd size, participation in shows or markets, introduction of cattle, grazing, and contact with other cattle herds on pasture. Significant higher odds were found for dairy herds (odds ratio, OR = 1.63, 95% confidence interval, CI: 1.06-2.50) compared with beef herds, for larger herds (OR = 1.04 for every 10 extra animals in the herd, 95% CI: 1.02-1.06), for herds that participate in shows or markets (OR = 1.45, 95% CI: 1.10-1.91), for herds that introduced cattle into the herd (OR = 1.41, 95% CI: 1.18-1.69), and for herds that share pasture or have direct contact with cattle of other herds at pasture (OR = 1.32, 95% CI: 1.07-1.63). These pooled values must be interpreted with care, as there was a high level of heterogeneity between studies. However, they do give an indication of the importance of the most frequently studied risk factors and can therefore assist in the development, evaluation, and optimization of BVDV control programs.

Capacity of a Bayesian model to detect infected herds using disease dynamics and risk factor information from surveillance programmes: A simulation study.

Control programmes against non-regulated infectious diseases of farm animals are widely implemented. Different control programmes have different definitions of "freedom from infection" which can lead to difficulties when trading animals between countries. When a disease is still present, in order to identify herds that are safe to trade with, estimating herd-level probabilities of being infected when classified "free from infection" using field data is of major interest. Our objective was to evaluate the capacity of a Bayesian Hidden Markov Model, which computes a herd-level probability of being infected, to detect infected herds compared to using test results only. Herd-level risk factors, infection dynamics and associated test results were simulated in a population of herds, for a wide range of realistic infection contexts and test characteristics. The model was used to predict the infection status of each herd from longitudinal data: a simulated risk factor and a simulated test result. Two different indexes were used to categorize herds from the probability of being infected into a herd predicted status. The model predictive performances were evaluated using the simulated herd status as the gold standard. The model detected more infected herds than a single final test in 85 % of the scenarios which converged. The proportion of infected herds additionally detected by the model, compared to test results alone, varied depending on the context. It was higher in a context of a low herd test sensitivity. On average, around 20 %, for high test sensitivity scenarios, and 40 %, for low test sensitivity scenarios, of infected herds that were undetected by the test were accurately classified as infected by the model. Model convergence did not occur for 39 % of the scenarios, mainly in association with low herd test sensitivity. Detection of additional newly infected herds was always associated with an increased number of false positive herds (except for one scenario). The number of false positive herds was lower for scenarios with low herd test sensitivity and moderate to high incidence and prevalence. These results highlight the benefit of the model, in particular for control programmes with infection present at an endemic level in a population and reliance on test(s) of low sensitivity.

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

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.

Spatial and risk factor analysis of bovine viral diarrhoea (BVD) virus after the first-year compulsory phase of BVD eradication programme in Northern Ireland.

Bovine viral diarrhoea virus (BVDV) causes bovine viral diarrhoea (BVD), which is a contagious pathogen that can have a significant economic impact on cattle industries. In Northern Ireland (NI), the compulsory phase of a BVD eradication programme was implemented in 2016. The aim of this retrospective population based study was to utilize herd-level data after the first year of the compulsory phase (March 2016-March 2017) to determine the spatial distribution and variation of BVDV, to identify clusters of infection, and to quantify some risk factors associated with BVD in NI. Global spatial clustering (autocorrelation) and local spatial hot-spot analyses were used to specify the clustering areas (hot- and cold-spot). A suite of multivariable logistic analyses was performed to estimate the associations of spatial and non-spatial factors (relating to herd characteristics) with the risk of being a BVDV positive herd. Final models were compared by evaluating the model fit and the ability to account for spatial autocorrelation in the study area. There were 17,186 herds included in the analysis. The herd-level prevalence of BVDV was 11.31%. Significant spatial clustering of BVDV positive herds was presented in the central region of NI. A mixed effects logistic model, with a spatial random effect term, was considered the best model. The final model showed that a positive BVDV status during the voluntary phase prior to the compulsory phase started (OR = 2.25; CI 95% = 1.85-2.73), larger herd size (OR = 6.19; CI 95% = 5.22-7.34 for herd size > 100 animals) and a larger number of positive nearest neighbours within 4 km radius (OR = 1.24; CI 95% = 1.05-1.47 for 8-9 neighbours and OR = 1.41; CI 95% = 1.20-1.65 for 10-12 neighbours) were significantly related to the risk of a herd being tested positive for BVDV. The clear spatial pattern from the local spatial clustering analyses could be used for targeted surveillance and control measures by focusing on the central region of NI.

Modelling the variation in skin-test tuberculin reactions, post-mortem lesion counts and case pathology in tuberculosis-exposed cattle: Effects of animal characteristics, histories and co-infection.

Correctly identifying bovine tuberculosis (bTB) in cattle remains a significant problem in endemic countries. We hypothesized that animal characteristics (sex, age, breed), histories (herd effects, testing, movement) and potential exposure to other pathogens (co-infection; BVDV, liver fluke and Mycobacterium avium reactors) could significantly impact the immune responsiveness detected at skin testing and the variation in post-mortem pathology (confirmation) in bTB-exposed cattle. Three model suites were developed using a retrospective observational data set of 5,698 cattle culled during herd breakdowns in Northern Ireland. A linear regression model suggested that antemortem tuberculin reaction size (difference in purified protein derivative avium [PPDa] and bovine [PPDb] reactions) was significantly positively associated with post-mortem maximum lesion size and the number of lesions found. This indicated that reaction size could be considered a predictor of both the extent (number of lesions/tissues) and the pathological progression of infection (maximum lesion size). Tuberculin reaction size was related to age class, and younger animals (<2.85 years) displayed larger reaction sizes than older animals. Tuberculin reaction size was also associated with breed and animal movement and increased with the time between the penultimate and disclosing tests. A negative binomial random-effects model indicated a significant increase in lesion counts for animals with M. avium reactions (PPDb-PPDa < 0) relative to non-reactors (PPDb-PPDa = 0). Lesion counts were significantly increased in animals with previous positive severe interpretation skin-test results. Animals with increased movement histories, young animals and non-dairy breed animals also had significantly increased lesion counts. Animals from herds that had BVDV-positive cattle had significantly lower lesion counts than animals from herds without evidence of BVDV infection. Restricting the data set to only animals with a bTB visible lesion at slaughter (n = 2471), an ordinal regression model indicated that liver fluke-infected animals disclosed smaller lesions, relative to liver fluke-negative animals, and larger lesions were disclosed in animals with increased movement histories.

Assessment of concurrent infection with bovine viral diarrhoea virus (BVDV) and Mycobacterium bovis: A herd-level risk factor analysis from Northern Ireland.

Bovine viral diarrhoea virus (BVDV) is a significant pathogen of cattle, leading to severe economic and animal-welfare impacts. Furthermore, the pathogen has been associated with impacting the progression or spread of other pathogens (e.g. Mycobacterium bovis, the causative agent of bovine tuberculosis (bTB)). During this study we investigated (i) risk factors for BVDV at a herd-level and (ii) whether there was any association between BVDV and herd-level bTB risk. The data for this study were gathered from a voluntary BVDV control programme in Northern Ireland (2013-2015) based on the identification of virus positive animals through tissue tag testing of calves. We assigned a herd-level BVDV status to 2827 participating herds, where a herd was assumed "infected" if one or more animals tested positive for BVDV. Two model suites were developed. Firstly, we assessed risk factors for BVDV herd status using multivariable logit random-effects modelling, aggregating to the calendar year level (2013-2015; n=4828; model 1). Secondly, we aggregated data across the three years of the study to give an overall status for the whole study period (n=2827; logistic model 2). Risk factors included year, herd-type, herd size, number of births, inward trade moves, calf mortality, and region. Furthermore, the herd-level bovine tuberculosis status (based on the single intradermal comparative cervical tuberculin (SICCT) test outcomes, or confirmation at post-mortem), or the size of bTB breakdowns (number of SICCT test positive animals), of herds was also investigated to assess whether there was an association (co-infection) with herd BVDV status. The final models suggested that BVDV herd status was positively associated with increased levels of calf mortality, herd size, number of births, the number of BVDV tests undertaken and the number of animals introduced to the herd. There was a significant univariable positive association between BVDV status, and SICCT breakdown risk, breakdown size and confirmed bTB status in model 2. However, there was no evidence of significant associations between bTB status (using SICTT status, confirmed status or herd breakdown size) and BVDV status in final multivariable models when controlling for other significant confounders. These results provide information for action for the future control and eradication of BVDV in Northern Ireland, though these data provide little support for the hypothesised association between BVDV and bTB status at herd-level. Further animal-level analyses are necessary to investigate whether there is support for a BVD-bTB co-infection association, including the impact of co-infection on the severity of infection.

Initial incursion of pandemic (H1N1) 2009 influenza A virus into European pigs.

The initial incursion of pandemic (H1N1) 2009 influenza A virus (pH1N1) into a European pig population is reported. Diagnosis of swine influenza caused by pandemic virus was made during September 2009 following routine submission of samples for differential diagnosis of causative agents of respiratory disease, including influenza A virus. All four pigs (aged six weeks) submitted for investigation from a pig herd of approximately 5000 animals in Northern Ireland, experiencing acute-onset respiratory signs in finishing and growing pigs, were positive by immunofluorescence for influenza A. Follow-up analysis of lung tissue homogenates by real-time RT-PCR confirmed the presence of pH1N1. The virus was subsequently detected on two other premises in Northern Ireland; on one premises, detection followed the pre-export health certification testing of samples from pigs presumed to be subclinically infected as no clinical signs were apparent. None of the premises was linked to another epidemiologically. Sequencing of the haemagglutinin and neuraminidase genes revealed high nucleotide identity (>99.4 per cent) with other pH1N1s isolated from human beings. Genotypic analyses revealed all gene segments to be most closely related to those of contemporary pH1N1 viruses in human beings. It is concluded that all three outbreaks occurred independently, potentially as a result of transmission of the virus from human beings to pigs.