Research in big data, informatics, and bioinformatics has grown dramatically (Andreu-Perez J, et al., 2015, IEEE Journal of Biomedical and Health Informatics 19, 1193–1208). Advances in gene sequencing technologies, surveillance systems, and electronic medical records have increased the amount of health data available. Unconventional data sources such as social media, wearable sensors, and internet search engine activity have also contributed to the influx of health data. The purpose of this study was to describe how ‘big data’, ‘informatics’, and ‘bioinformatics’ have been used in the animal health and veterinary medical literature and to map and chart publications using these terms through time. A scoping review methodology was used. A literature search of the terms ‘big data’, ‘informatics’, and ‘bioinformatics’ was conducted in the context of animal health and veterinary medicine. Relevance screening on abstract and full-text was conducted sequentially. In order for articles to be relevant, they must have used the words ‘big data’, ‘informatics’, or ‘bioinformatics’ in the title or abstract and full-text and have dealt with one of the major animal species encountered in veterinary medicine. Data items collected for all relevant articles included species, geographic region, first author affiliation, and journal of publication. The study level, study type, and data sources were collected for primary studies. After relevance screening, 1093 were classified. While there was a steady increase in ‘bioinformatics’ articles between 1995 and the end of the study period, ‘informatics’ articles reached their peak in 2012, then declined. The first ‘big data’ publication in animal health and veterinary medicine was in 2012. While few articles used the term ‘big data’ (n = 14), recent growth in ‘big data’ articles was observed. All geographic regions produced publications in ‘informatics’ and ‘bioinformatics’ while only North America, Europe, Asia, and Australia/Oceania produced publications about ‘big data’. ‘Bioinformatics’ primary studies tended to use genetic data and tended to be conducted at the genetic level. In contrast, ‘informatics’ primary studies tended to use non-genetic data sources and conducted at an organismal level. The rapidly evolving definition of ‘big data’ may lead to avoidance of the term.

Transition cow diseases can negatively impact animal welfare and reduce dairy herd profitability. Transition cow disease incidence has remained relatively stable over time despite monitoring and management efforts aimed to reduce the risk of developing diseases. Dairy cattle disease risk is monitored by assessing multiple factors, including certain biomarker test results, health records, feed intake, body condition score, and milk production. However, these factors, which are used to make herd management decisions, are often reviewed separately without considering the correlation between them. In addition, the biomarkers that are currently used for monitoring may not be representative of the complex physiological changes that occur during the transition period. Predictive modeling, which uses data to predict future or current outcomes, is a method that can be used to combine the most predictive variables and their interactions efficiently. The use of an effective predictive model with relevant predictors for transition cow diseases will result in better targeted interventions, and therefore lower disease incidence. This review will discuss predictive modeling methods and candidate variables in the context of transition cow diseases. The next step is to investigate novel biomarkers and statistical methods that are best suited for the prediction of transition cow diseases.

The current livestock management landscape is transitioning to a high-throughput digital era where large amounts of information captured by systems of electro-optical, acoustical, mechanical, and biosensors is stored and analyzed on a daily and hourly basis, and actionable decisions are made based on quantitative and qualitative analytic results. While traditional animal breeding prediction methods have been used with great success until recently, the deluge of information starts to create a computational and storage bottleneck that could lead to negative long-term impacts on herd management strategies if not handled properly. A plethora of machine learning approaches, successfully used in various industrial and scientific applications, made their way in the mainstream approaches for livestock breeding techniques, and current results show that such methods have the potential to match or surpass the traditional approaches, while most of the time they are more scalable from a computational and storage perspective. This article provides a succinct view on what traditional and novel prediction methods are currently used in the livestock breeding field, how successful they are, and how the future of the field looks in the new digital agriculture era.

Diagnosis, treatment, and prevention of vector-borne disease (VBD) in pets is one cornerstone of companion animal practices. Veterinarians are facing new challenges associated with the emergence, reemergence, and rising incidence of VBD, including heartworm disease, Lyme disease, anaplasmosis, and ehrlichiosis. Increases in the observed prevalence of these diseases have been attributed to a multitude of factors, including diagnostic tests with improved sensitivity, expanded annual testing practices, climatologic and ecological changes enhancing vector survival and expansion, emergence or recognition of novel pathogens, and increased movement of pets as travel companions. Veterinarians have the additional responsibility of providing information about zoonotic pathogen transmission from pets, especially to vulnerable human populations: the immunocompromised, children, and the elderly. Hindering efforts to protect pets and people is the dynamic and ever-changing nature of VBD prevalence and distribution. To address this deficit in understanding, the Companion Animal Parasite Council (CAPC) began efforts to annually forecast VBD prevalence in 2011. These forecasts provide veterinarians and pet owners with expected disease prevalence in advance of potential changes. This review summarizes the fidelity of VBD forecasts and illustrates the practical use of CAPC pathogen prevalence maps and forecast data in the practice of veterinary medicine and client education.

In the last several decades, avian influenza virus has caused numerous outbreaks around the world. These outbreaks pose a significant threat to the poultry industry and also to public health. When an avian influenza (AI) outbreak occurs, it is critical to make informed decisions about the potential risks, impact, and control measures. To this end, many modeling approaches have been proposed to acquire knowledge from different sources of data and perspectives to enhance decision making. Although some of these approaches have shown to be effective, they do not follow the process of knowledge discovery in databases (KDD). KDD is an iterative process, consisting of five steps, that aims at extracting unknown and useful information from the data. The present review attempts to survey AI modeling methods in the context of KDD process. We first divide the modeling techniques used in AI into two main categories: data-intensive modeling and small-data modeling. We then investigate the existing gaps in the literature and suggest several potential directions and techniques for future studies. Overall, this review provides insights into the control of AI in terms of the risk of introduction and spread of the virus.

Bovine viral diarrhea virus (BVDV) is an important infectious disease agent that causes significant reproductive and economic losses in the cattle industry worldwide. Although BVDV infection is known to cause poor fertility in cattle, a greater part of the underlying mechanisms particularly associated with early reproductive losses are not clearly understood. Previous studies reported viral compromise of reproductive function in infected bulls. In females, BVDV infection is thought to be capable of killing the oocyte, embryo or fetus directly, or to induce lesions that result in fetal abortion or malformation. BVDV infections may also induce immune dysfunction, and predispose cattle to other diseases that cause poor health and fertility. Other reports also suggested BVDV-induced disruption of the reproductive endocrine system, and a disruption of leukocyte and cytokine functions in the reproductive organs. More recent studies have provided evidence of viral-induced suppression of endometrial innate immunity that may predispose to uterine disease. Furthermore, there is new evidence that BVDV may potentially disrupt the maternal recognition of pregnancy or the immune protection of the conceptus. This review brings together the previous reports with the more recent findings, and attempts to explain some of the mechanisms linking this important virus to infertility in cattle.

Use of antimicrobial approaches at drying-off for preventing new intramammary infections (IMI) during the dry period in dairy cows could be replaced by non-antimicrobial approaches. Such approaches would be of interest not only for organic but also for conventional dairy producers. The objective of the current review was to quantify the effect of non-antimicrobial internal teat sealant (ITS)-based approaches at drying-off for treating and preventing IMI, when compared with no treatment or with an antimicrobial-based approach. The protocol for this review was published before initiating the review. A total of 18 trials from 16 articles could be used to investigate the effect of an ITS-based approach. With the available results, we conclude with a high level of confidence that non-antimicrobial ITS-based dry-off approaches are efficient for preventing new IMI during the dry period when compared with no treatment, and would reduce risk of new IMI by 52%. Moreover, we are relatively confident that a bismuth subnitrate-based ITS performed better than an antimicrobial for preventing new IMI during the dry period (a risk reduction of 23%). Similarly, we are relatively confident that an ITS-based approach would only slightly or not at all reduce the prevalence of IMI at calving compared with untreated quarters.

Paratuberculosis is a worldwide infectious disease caused by Mycobacterium avium ssp. paratuberculosis (MAP). Various ruminant species can be affected by the disease, and the diagnosis of the disease is challenging in the absence of a gold standard test. The aim of this systematic review protocol is to determine the accuracy of the direct and indirect diagnostic tests for MAP infection with a special focus on sheep and goats.