The significant economic impacts of bovine viral diarrhoea (BVD) virus have prompted many countries worldwide to embark on regional or national BVD eradication programmes. Unlike other infectious diseases, BVD control is highly feasible in cattle production systems because the pathogenesis is well understood and there are effective tools to break the disease transmission cycle at the farm and industry levels. Coordinated control approaches typically involve directly testing populations for virus or serological screening of cattle herds to identify those with recent exposure to BVD, testing individual animals within affected herds to identify and eliminate persistently infected (PI) cattle, and implementing biosecurity measures such as double‐fencing shared farm boundaries, vaccinating susceptible breeding cattle, improving visitor and equipment hygiene practices, and maintaining closed herds to prevent further disease transmission. As highlighted by the recent DISCONTOOLS review conducted by a panel of internationally‐recognized experts, knowledge gaps in the control measures are primarily centred around the practical application of existing tools rather than the need for creation of new tools. Further research is required to: (i) determine the most‐cost effective and socially acceptable means of applying BVD control measures in different cattle production systems; (ii) identify the most effective ways to build widespread support for implementing BVD control measures from the bottom‐up through farmer engagement and from the top‐down through national policy; and (iii) to develop strategies to prevent the re‐introduction of BVD into disease‐free regions by managing the risks associated with the movements of animals, personnel, and equipment. Stronger collaboration between epidemiologists, economists, and social scientists will be essential for progressing efforts to eradicate BVD from more countries worldwide.

Individuals in human and animal populations are linked through dynamic contact networks with characteristic structural features that drive the epidemiology of directly transmissible infectious diseases. Using animal movement data from the British cattle industry as an example, this analysis explores whether disease dynamics can be altered by placing targeted restrictions on contact formation to reconfigure network topology. This was accomplished using a simple network generation algorithm that combined configuration wiring with stochastic block modelling techniques to preserve the weighted in- and out-degree of individual nodes (farms) as well as key demographic characteristics of the individual network connections (movement date, livestock market, and animal production type). We then tested a control strategy based on introducing additional constraints into the network generation algorithm to prevent farms with a high in-degree from selling cattle to farms with a high out-degree as these particular network connections are predicted to have a disproportionately strong role in spreading disease. Results from simple dynamic disease simulation models predicted significantly lower endemic disease prevalences on the trade restricted networks compared to the baseline generated networks. As expected, the relative magnitude of the predicted changes in endemic prevalence was greater for diseases with short infectious periods and low transmission probabilities. Overall, our study findings demonstrate that there is significant potential for controlling multiple infectious diseases simultaneously by manipulating networks to have more epidemiologically favourable topological configurations. Further research is needed to determine whether the economic and social benefits of controlling disease can justify the costs of restricting contact formation.

Aims: To describe the demographics of and predictors for pet ownership, reasons for visiting a veterinarian, and pet-related expenditure in pet owners in New Zealand. Methods: A cross-sectional online survey was conducted from 18–22 June 2015 using a permission-based panel of New Zealand residents aged ≥18 years. Questions included demographics of respondents, number of pets and reasons for owning or not owning pets, number of visits and reasons for visiting a veterinarian, and pet-related expenditure. Results: Of the 1,572 respondents who completed the survey, 1,013 (64.4%) owned ≥1 pet. Of these, 443 owned dogs, 696 cats, 32 horses, 103 birds, 55 rabbits, and 159 owned fish. Companionship was the most common reason for getting dogs, cats, and birds; horses were mostly owned as a hobby, rabbits to provide fun for children, and fish as a source of relaxation. The majority of dog, cat, and rabbit owners considered their pets to be family members; horse owners almost equally considered their horses a hobby or a family member. The odds of pet ownership increased for respondents from a rural region, having a higher household income, having children and being female. Overall, 711/1,013 (70.2%) pet-owning respondents had taken ≥1 animal to the veterinarian in the previous year, with the most common reasons being for vaccination or annual check-ups or health issues. Respondents who considered their pets trusted companions, had a higher income, and owned dogs or cats compared with other species, were most likely to have taken their pet to a veterinarian. The greatest pet-related expenditure for all species was food. The median yearly veterinary expenditure was $200–499 by dog owners, $100–199 by cat owners, and <$100 by horse, bird, rabbit, and fish owners. The best source of information for pet-related issues was considered to be veterinarians by 724/1,001 (72.3%) owners, and the internet by 509/1,001 (50.8%) owners. Conclusions: Among survey respondents, pet ownership was common and pets filled a variety of roles in the household. Pet owners reported spending considerable amounts of money on their pets each year, but some of them may be underutilising veterinary services despite veterinarians being considered as valuable sources of information about pet-related issues.

Over the past several decades, infectious disease modeling has become an essential tool for creating counterfactual scenarios that allow the effectiveness of different disease control policies to be evaluated prior to implementation in the real world. For livestock diseases, these models have become increasingly sophisticated as researchers have gained access to rich national livestock traceability databases, which enables inclusion of explicit spatial and temporal patterns in animal movements through network-based approaches. However, there are still many limitations in how we currently model animal disease dynamics. Critical among these is that many models make the assumption that human behaviors remain constant over time. As many studies have shown, livestock owners change their behaviors around trading, on-farm biosecurity, and disease management in response to complex factors such as increased awareness of disease risks, pressure to conform with social expectations, and the direct imposition of new national animal health regulations; all of which may significantly influence how a disease spreads within and between farms. Failing to account for these dynamics may produce a substantial layer of bias in infectious disease models, yet surprisingly little is currently known about the effects on model inferences. Here, we review the growing evidence on why these assumptions matter. We summarize the current knowledge about farmers' behavioral change in on-farm biosecurity and livestock trading practices and highlight the knowledge gaps that prohibit these behavioral changes from being incorporated into disease modeling frameworks. We suggest this knowledge gap can be filled only by more empirical longitudinal studies on farmers' behavioral change as well as theoretical modeling studies that can help to identify human behavioral changes that are important in disease transmission dynamics. Moreover, we contend it is time to shift our research approach: from modeling a single disease to modeling interactions between multiple diseases and from modeling a single farmer behavior to modeling interdependencies between multiple behaviors. In order to solve these challenges, there is a strong need for interdisciplinary collaboration across a wide range of fields including animal health, epidemiology, sociology, and animal welfare.