Predictive modeling of bovine respiratory disease outcomes in feedlot cattle: A narrative review

Comparison of physiological markers, behavior monitoring, and clinical illness scoring as indicators of an inflammatory response in beef cattle

Clinical illness (CI) scoring using visual observation is the most widely applied method of detecting respiratory disease in cattle but has limited effectiveness in practice. In contrast, body-mounted sensor technology effectively facilitates disease detection. To evaluate whether a combination of movement behavior and CI scoring is effective for disease detection, cattle were vaccinated to induce a temporary inflammatory immune response. Cattle were evaluated before and after vaccination to identify the CI variables that are most indicative of sick cattle. Respiratory rate (H2 = 43.08, P < 0.0001), nasal discharge (H2 = 8.35, P = 0.015), and ocular discharge (H2 = 16.38, P = 0.0003) increased after vaccination, and rumen fill decreased (H2 = 20.10, P < 0.0001). Locomotor activity was measured via leg-mounted sensors for the four days preceding and seven days following vaccination. A statistical model that included temperature, steps, lying time, respiratory rate, rumen fill, head position, and excess saliva was developed to distinguish between scores from before and after vaccination with a sensitivity of 0.898 and specificity of 0.915. Several clinical illness signs were difficult to measure in practice. Binoculars were required for scoring respiratory rate and eye-related metrics, and cattle had to be fitted with colored collars for individual identification. Scoring each animal took up to three minutes in a small research pen; therefore, technologies that can automate both behavior monitoring and identification of clinical illness signs are key to improving capacity for BRD detection and treatment.

Dogs' ability to detect an inflammatory immune response in cattle via olfaction

Introduction

Canine olfaction is a potential means for detection of respiratory disease in beef cattle. In a prior study, two dogs were trained to discriminate between nasal swabs from healthy cattle and cattle that developed Bovine Respiratory Disease. Dogs had some ability to identify samples from BRD-affected cattle, but results were ambiguous. The purpose of this study was to evaluate more dogs using better-controlled training and testing procedures.

Methods

Nasal and saliva swabs were collected from 96 cattle before and after administering a vaccine to induce an inflammatory immune response. Samples were stored at -80°C for up to 11 months before use, and samples from animals with an elevated body temperature at baseline were omitted. An automated olfactometer apparatus was constructed to improve blinding procedures and reduce opportunities for odor contamination. Four dogs were trained to distinguish between swabs from healthy and sickness-model cattle, including the two dogs from the previous study ("Runnels" and "Cheaps") and two inexperienced dogs ("Molokai" and "Amy"). During a seven-month training period, dogs were exposed to samples from 28 animals. Dogs were tested on 59 sets of unfamiliar samples.

Results

Performance varied among dogs (χ2 = 10.48, p = 0.02). Molokai's performance was above chance (0.73 ± 0.06, p = 0.0006), while Amy (0.44 ± 0.06, p = 0.43), Cheaps (0.53 ± 0.07, p = 0.79), and Runnels (0.56 ± 0.06, p = 0.43) did not respond correctly at a rate different from chance. Accuracy did not differ between nasal swabs (0.63 ± 0.08) and saliva swabs (0.53 ± 0.08, χ2 = 0.81, p = 0.37).

Discussion

The results of this study indicate that canine olfaction may be an effective means of detecting illness in beef cattle. However, individual dogs' aptitude for this detection task varies.

Strategies for Bovine Respiratory Disease (BRD) Diagnosis and Prognosis: A Comprehensive Overview

Despite significant advances in vaccination strategies and antibiotic therapy, bovine respiratory disease (BRD) continues to be the leading disease affecting the global cattle industry. The etiology of BRD is complex, often involving multiple microbial agents, which lead to intricate interactions between the host immune system and pathogens during various beef production stages. These interactions present environmental, social, and geographical challenges. Accurate diagnosis is essential for effective disease management. Nevertheless, correct identification of BRD cases remains a daunting challenge for animal health technicians in feedlots. In response to current regulations, there is a growing interest in refining clinical diagnoses of BRD to curb the overuse of antimicrobials. This shift marks a pivotal first step toward establishing a structured diagnostic framework for this disease. This review article provides an update on recent developments and future perspectives in clinical diagnostics and prognostic techniques for BRD, assessing their benefits and limitations. The methods discussed include the evaluation of clinical signs and animal behavior, biomarker analysis, molecular diagnostics, ultrasound imaging, and prognostic modeling. While some techniques show promise as standalone diagnostics, it is likely that a multifaceted approach-leveraging a combination of these methods-will yield the most accurate diagnosis of BRD.

Evaluation of a Respiration Rate Sensor for Recording Tidal Volume in Calves under Field Conditions

In the assessment of pulmonary function in health and disease, both respiration rate (RR) and tidal volume (Vt) are fundamental parameters of spontaneous breathing. The aim of this study was to evaluate whether an RR sensor, which was previously developed for cattle, is suitable for additional measurements of Vt in calves. This new method would offer the opportunity to measure Vt continuously in freely moving animals. To measure Vt noninvasively, the application of a Lilly-type pneumotachograph implanted in the impulse oscillometry system (IOS) was used as the gold standard method. For this purpose, we applied both measuring devices in different orders successively, for 2 days on 10 healthy calves. However, the Vt equivalent (RR sensor) could not be converted into a true volume in mL or L. For a reliable recording of the Vt equivalent, a technical revision of the RR sensor excluding artifacts is required. In conclusion, converting the pressure signal of the RR sensor into a flow equivalent, and subsequently into a volume equivalent, by a comprehensive analysis, provides the basis for further improvement of the measuring system.

Literature Review on Technological Applications to Monitor and Evaluate Calves' Health and Welfare

Precision livestock farming (PLF) research is rapidly increasing and has improved farmers' quality of life, animal welfare, and production efficiency. PLF research in dairy calves is still relatively recent but has grown in the last few years. Automatic milk feeding systems (AMFS) and 3D accelerometers have been the most extensively used technologies in dairy calves. However, other technologies have been emerging in dairy calves' research, such as infrared thermography (IRT), 3D cameras, ruminal bolus, and sound analysis systems, which have not been properly validated and reviewed in the scientific literature. Thus, with this review, we aimed to analyse the state-of-the-art of technological applications in calves, focusing on dairy calves. Most of the research is focused on technology to detect and predict calves' health problems and monitor pain indicators. Feeding and lying behaviours have sometimes been associated with health and welfare levels. However, a consensus opinion is still unclear since other factors, such as milk allowance, can affect these behaviours differently. Research that employed a multi-technology approach showed better results than research focusing on only a single technique. Integrating and automating different technologies with machine learning algorithms can offer more scientific knowledge and potentially help the farmers improve calves' health, performance, and welfare, if commercial applications are available, which, from the authors' knowledge, are not at the moment.

Technological Tools for the Early Detection of Bovine Respiratory Disease in Farms

Simple Summary

The inclusion of remote automatic systems that use continuous learning technology are of great interest in precision livestock cattle farming, since the average size of farms is increasing while time for individual observation is decreasing. Bovine respiratory disease is a main concern in both fattening and heifer rearing farms due to its impact on antibiotic use, loss of performance, mortality, and animal welfare. Much scientific literature has been published regarding technologies for continuous learning and monitoring of cattle’s behavior and accurate correlation with health status, including early detection of bovine respiratory disease. This review summarizes the up-to-date technologies for early diagnosis of bovine respiratory disease and discusses their advantages and disadvantages under practical conditions.

Abstract

Classically, the diagnosis of respiratory disease in cattle has been based on observation of clinical signs and the behavior of the animals, but this technique can be subjective, time-consuming and labor intensive. It also requires proper training of staff and lacks sensitivity (Se) and specificity (Sp). Furthermore, respiratory disease is diagnosed too late, when the animal already has severe lesions. A total of 104 papers were included in this review. The use of new advanced technologies that allow early diagnosis of diseases using real-time data analysis may be the future of cattle farms. These technologies allow continuous, remote, and objective assessment of animal behavior and diagnosis of bovine respiratory disease with improved Se and Sp. The most commonly used behavioral variables are eating behavior and physical activity. Diagnosis of bovine respiratory disease may experience a significant change with the help of big data combined with machine learning, and may even integrate metabolomics as disease markers. Advanced technologies should not be a substitute for practitioners, farmers or technicians, but could help achieve a much more accurate and earlier diagnosis of respiratory disease and, therefore, reduce the use of antibiotics, increase animal welfare and sustainability of livestock farms. This review aims to familiarize practitioners and farmers with the advantages and disadvantages of the advanced technological diagnostic tools for bovine respiratory disease and introduce recent clinical applications.

Ultrasonographic diagnosis of clinical and subclinical bovine respiratory disease in Holstein calves

Background and Aim:

Bovine respiratory disease (BRD) is the main cause of death in calves, and early BRD diagnosis saves lives. This study aimed to diagnose clinical and subclinical BRD in calves by assessing some biochemical alterations and ultrasonography (USG).

Materials and Methods:

Fifty-four Holstein dairy calves in Al-Sharqiyah Province, Egypt, were used in the study. They were divided into three groups. The first control group consisted of 10 clinically healthy calves. The second group consisted of 34 calves suffering from clinical lower respiratory tract disorders. The third group consisted of 10 subclinical BRD-affected calves. Ultrasonographic examinations of chest and thoracic ultrasound scoring were performed once per 2 weeks for each calf. Blood samples were collected for serum separation to measure albumin (ALB), total protein (TP), ALB, globulin, and haptoglobin (HP).

Results:

The USG revealed small consolidation areas within an aerated lung lobe, a hypoechoic parenchyma of the entire distal lung lobe, and a hypoechoic-circumscribed structure surrounded by an echogenic wall appeared within the lung tissue in calves that suffered from lobular pneumonia, lobar pneumonia, and lung abscess, respectively. However, subclinical cases showed a small consolidation area in the cranial aspects of the right cranial lung lobe. The ultrasound lung score (ULS) was greater in clinical than in subclinical cases. The BRD-affected calves recorded significant increases in serum TP, globulin, and HP. Meanwhile, serum ALB decreased significantly.

Conclusion:

Thoracic ultrasound had a reliable tool in the BRD diagnosis, especially in the early prediction of subclinical cases in newborn calves. In addition, the ULS appeared to be a better classifier than the clinical respiratory score (CRS) for BRD diagnosis. On the other side, it was found that regression models were very useful in assessing the prediction of biochemical blood parameters based on the ULS and CRS in diseased cases.

Using Canine Olfaction to Detect Bovine Respiratory Disease: A Pilot Study

Bovine respiratory disease (BRD) is the leading cause of morbidity and mortality in feedlot cattle and is a major welfare and economic concern. Identification of BRD-affected cattle using clinical illness scores is problematic, and speed and cost constraints limit the feasibility of many diagnostic approaches. Dogs can rapidly identify humans and animals affected by a variety of diseases based on scent. Canines' olfactory systems can distinguish between patterns of volatile organic compounds produced by diseased and healthy tissue. In this pilot study, two dogs (“Runnels” and “Cheaps”) were trained for 7 months to discriminate between nasal swabs from cattle that developed signs of BRD within 20 days of feedlot arrival and swabs from cattle that did not develop BRD signs within 3 months at the feedlot. Nasal swabs were collected during cattle processing upon arrival to the feedlot and were stored at −80°C. Dogs were presented with sets of one positive and two negative samples and were trained using positive reinforcement to hold their noses over the positive sample. The dogs performed moderately well in the final stage of training, with accuracy for Runnels of 0.817 and Cheaps of 0.647, both greater than the 0.333 expected by chance. During a double-blind detection test, dogs evaluated 123 unique and unfamiliar samples that were presented as 41 sets (3 samples per set), with both the dog handler and data recorder blinded to the positive sample location. Each dog was tested twice on each set of samples. Detection test accuracy was slightly better than chance for Cheaps at 0.451 (95% CI: 0.344–0.559) and was no better than chance for Runnels at 0.390 (95% CI: 0.285–0.496. Overall accuracy was 0.421 (95% CI: 0.345–0.496). When dogs' consensus response on each sample set was considered, accuracy was 0.537 (95% CI: 0.384–0.689). Detection accuracy also varied by sample lot. While dogs showed some ability to discriminate between BRD-affected and healthy cattle using nasal swabs, the complexity of this task suggests that more testing is needed before determining whether dogs could be effective as a screening method for BRD.

ASAS-NANP symposium: mathematical modeling in animal nutrition: the progression of data analytics and artificial intelligence in support of sustainable development in animal science

A renewed interest in data analytics and decision support systems in developing automated computer systems is facilitating the emergence of hybrid intelligent systems by combining artificial intelligence (AI) algorithms with classical modeling paradigms such as mechanistic modeling (HIMM) and agent-based models (iABM). Data analytics have evolved remarkably, and the scientific community may not yet fully grasp the power and limitations of some tools. Existing statistical assumptions might need to be re-assessed to provide a more thorough competitive advantage in animal production systems towards sustainability. This paper discussed the evolution of data analytics from a competitive advantage perspective within academia and illustrated the combination of different advanced technological systems in developing HIMM. The progress of analytical tools was divided into three stages: collect and respond, predict and prescribe, and smart learning and policy making, depending on the level of their sophistication (simple to complicated analysis). The collect and respond stage is responsible for ensuring the data is correct and free of influential data points, and it represents the data and information phases for which data are cataloged and organized. The predict and prescribe stage results in gained knowledge from the data and comprises most predictive modeling paradigms, and optimization and risk assessment tools are used to prescribe future decision-making opportunities. The third stage aims to apply the information obtained in the previous stages to foment knowledge and use it for rational decisions. This stage represents the pinnacle of acquired knowledge that leads to wisdom, and AI technology is intrinsic. Although still incipient, HIMM and iABM form the forthcoming stage of competitive advantage. HIMM may not increase our ability to understand the underlying mechanisms controlling the outcomes of a system, but it may increase the predictive ability of existing models by helping the analyst explain more of the data variation. The scientific community still has some issues to be resolved, including the lack of transparency and reporting of AI that might limit code reproducibility. It might be prudent for the scientific community to avoid the shiny object syndrome (i.e., AI) and look beyond the current knowledge to understand the mechanisms that might improve productivity and efficiency to lead agriculture towards sustainable and responsible achievements.

Predictive Models for Weekly Cattle Mortality after Arrival at a Feeding Location Using Records, Weather, and Transport Data at Time of Purchase

Feedlot mortality negatively affects animal welfare and profitability. To the best of our knowledge, there are no publications on predictive models for weekly all-cause mortality in feedlot cattle. In this study, random forest models to predict weekly mortality for cattle purchase groups (n = 14,217 purchase groups; 860,545 animals) from arrival at the feeding location (Day 1) to Day 42 and cumulative mortality from Day 43 until slaughter were built using records, weather, and transport data available at the time of purchase. Models were evaluated by calculating the root mean squared error (RMSE) and accuracy (as defined as the percent of purchase groups that had predictions within 0.25% and 0.50% of actual mortality). The models had high accuracy (>90%), but the RMSE estimates were high (range = 1.0% to 4.1%). The best predictors were maximum temperature and purchase weight, although this varied by week. The models performed well among purchase groups with low weekly mortality but performed poorly in high mortality purchase groups. Although high mortality purchase groups were not accurately predicted utilizing the models in this study, the models may potentially have utility as a screening tool for very low mortality purchase groups after arrival. Future studies should consider building iterative models that utilize the strongest predictors identified in this study.

Scoping Review on Risk Factors and Methods for the Prevention of Bovine Respiratory Disease Applicable to Cow–Calf Operations

Simple Summary

Bovine respiratory disease (BRD) is common in cattle populations and has been named as one of three diseases where antibiotics are most frequently used in cow–calf operations in California. Antibiotics are typically used to treat or prevent the disease, but recent concerns about antibiotic resistance of pathogens in animals or spreading from animals to people have led to the call for more judicious use of these drugs. The present review summarizes the English scientific literature on articles about risk factors for the disease as well as ways to prevent BRD that are applicable to cow–calf operations. Numerous management and animal factors have been identified as increasing the risk for BRD. Vaccinations, metaphylactic use of antibiotics, and feed supplements are areas of research into the prevention of BRD. Genetics have also been explored to determine the heritability of BRD resistance. While vaccinations and metaphylactic use of antibiotics have been evaluated in multiple systematic reviews and meta-analyses, these types of summaries are missing for commonly studied feed supplements, such as yeast and trace minerals, and the use of nitric oxide releasing substance to prevent BRD. Further, it would be beneficial to summarize the knowledge on management related risk factors in literature reviews.

Abstract

The presented scoping review summarizes the available research evidence and identifies gaps in knowledge for bovine respiratory disease (BRD) prevention. Published literature on BRD from 1990 to April 2021 was searched in online databases, including Medline, CAB Abstracts, Scopus, Biosis, and Searchable Proceedings of Animal Conferences. Citations were systematically evaluated in a three-stage approach using commercial software and summarized in a scoping review format. A total of 265 publications were included in this review with herd/farm management (27.9%) as the most prevalent factors studied, followed by metaphylaxis (24.5%), vaccinations (24.1%), diet formulations, and nutritional supplementations (17.7%), animal characteristics (10.2%), and less common interventions and risk factors (6.4%). A high proportion of studies under herd/farm management (73%), metaphylaxis (86%), vaccinations (70%), animal characteristics (78%), and less common interventions and risk factors (59%) showed either significant effects on reducing BRD morbidity or significant differences of BRD between treatments. However, diet and nutritional supplementations reduced BRD only in 30% of research publications. Most studies on BRD were performed in feedlot populations, and more studies in cow–calf populations are needed. We further suggest meta-analyses on the use of yeast and trace mineral supplementation, and nitric oxide-releasing solution for BRD prevention.