Translating Big Data into Smart Data for Veterinary Epidemiology

Understanding drivers and barriers to stakeholder participation in syndromic surveillance for application in Australia

Early detection of disease is crucial for an effective and timely disease control and eradication response and requires sensitive and robust surveillance systems. The use of early warning systems based on the systematic monitoring of health data and the identification of syndromes, known as syndromic surveillance systems, has been used for early identification of human and animal health events. Despite the marked development in animal health syndromic surveillance (AHSyS) systems in the last two decades, challenges for the implementation of functional AHSyS systems continue to exist, with limited research investigating stakeholder perspectives on these systems. The current project aimed to identify key drivers and barriers of livestock industry private sector stakeholder participation in syndromic surveillance in New South Wales (NSW), Australia. To achieve this aim, a qualitative study was conducted using semi-structured interviews with seven international syndromic surveillance experts and 17 private sector stakeholders, including abattoirs, knackeries, animal health consultants/veterinarians, research institutions, livestock industries, pharmaceutical companies, private veterinary laboratories and a national animal health body. The expert consultation identified that despite the significant advancements on AHSyS in the last two decades, implementation of AHSyS systems continue to be limited, with key considerations being the lack of data standardisation, issues with data privacy, data integration and the limited consideration of stakeholder needs for supporting decision-making and benefits from participation. Strong iterative collaboration with all stakeholders with high levels of trust, appropriate resourcing, and balance between regulatory and industry needs are required for supporting system sustainability. Animal health surveillance was important for all stakeholders in the consultation, however understanding of syndromic surveillance systems was limited. A significant amount of health and production data is already being collected by stakeholders; however, the data type and data collection platforms are highly variable, confirming the complexity for standardisation and integration. The major stakeholder concerns were in relation to privacy, protection of information and the potential commercial and/or trade implications of data misuse or misrepresentation, the required additional resourcing for participating and the regulatory nature of such system. Despite these concerns, all stakeholders showed interest in being involved in further discussions on the development of an AHSyS system. A successful AHSyS system should consider representativeness and quality of the data, simplicity in data collection and processing, clear benefits and value of the outputs, and strong collaboration across all relevant stakeholders. Outcomes from this project will inform future activities for the development of AHSyS initiatives in Australia.

Developing a one health data integration framework focused on real-time pathogen surveillance and applied genomic epidemiology

BACKGROUND

The One Health approach aims to balance and optimize the health of humans, animals, and ecosystems, recognizing that shared health outcomes are interdependent. A One Health approach to disease surveillance, control, and prevention requires infrastructure for coordinating, collecting, integrating, and analyzing data across sectors, incorporating human, animal, and environmental surveillance data, as well as pathogen genomic data. However, unlike data interoperability problems faced within a single organization or sector, data coordination and integration across One Health sectors requires engagement among partners to develop shared goals and capacity at the response level. Successful examples are rare; as such, we sought to develop a framework for local One Health practitioners to utilize in support of such efforts.

METHODS

We conducted a systematic scientific and gray literature review to inform development of a One Health data integration framework. We discussed a draft framework with 17 One Health and informatics experts during semi-structured interviews. Approaches to genomic data integration were identified.

RESULTS

In total, 57 records were included in the final study, representing 13 pre-defined frameworks for health systems, One Health, or data integration. These frameworks, included articles, and expert feedback were incorporated into a novel framework for One Health data integration. Two scenarios for genomic data integration were identified in the literature and outlined.

CONCLUSIONS

Frameworks currently exist for One Health data integration and separately for general informatics processes; however, their integration and application to real-time disease surveillance raises unique considerations. The framework developed herein considers common challenges of limited resource settings, including lack of informatics support during planning, and the need to move beyond scoping and planning to system development, production, and joint analyses. Several important considerations separate this One Health framework from more generalized informatics frameworks; these include complex partner identification, requirements for engagement and co-development of system scope, complex data governance, and a requirement for joint data analysis, reporting, and interpretation across sectors for success. This framework will support operationalization of data integration at the response level, providing early warning for impending One Health events, promoting identification of novel hypotheses and insights, and allowing for integrated One Health solutions.

How do pig veterinarians view technology-assisted data utilisation for pig health and welfare management? A qualitative study in Spain, the Netherlands, and Ireland

BACKGROUND

Application of data-driven strategies may support veterinarians' decision-making, benefitting pig disease prevention and control. However, little is known about veterinarians' need for data utilisation to support their decision-making process. The current study used qualitative methods, specifically focus group discussions, to explore veterinarians' views on data utilisation and their need for data tools in relation to pig health and welfare management in Spain, the Netherlands, and Ireland.

RESULTS

Generally, veterinarians pointed out the potential benefits of using technology for pig health and welfare management, but data is not yet structurally available to support their decision-making. Veterinarians pointed out the challenge of collecting, recording, and accessing data in a consistent and timely manner. Besides, the reliability, standardisation, and the context of data were identified as important factors affecting the efficiency and effectiveness of data utilisation by veterinarians. A user-friendly, adaptable, and integrated data tool was regarded as potentially helpful for veterinarians' daily work and supporting their decision-making. Specifically, veterinarians, particularly independent veterinary practitioners, noted a need for easy access to pig information. Veterinarians such as those working for integrated companies, corporate veterinarians, and independent veterinary practitioners expressed their need for data tools that provide useful information to monitor pig health and welfare in real-time, to visualise the prevalence of endemic disease based on a shared report between farmers, veterinarians, and other professional parties, to support decision-making, and to receive early warnings for disease prevention and control.

CONCLUSIONS

It is concluded that the management of pig health and welfare may benefit from data utilisation if the quality of data can be assured, the data tools can meet veterinarians' needs for decision-making, and the collaboration of sharing data and using data between farmers, veterinarians, and other professional parties can be enhanced. Nevertheless, several notable technical and institutional barriers still exist, which need to be overcome.

Risk-Based Targeting of Animals for Ancillary Testing during a Bovine Tuberculosis Breakdown Is Associated with a Reduced Time to Test Failure: Indirect Evidence of Mycobacterium bovis Exposure?

Bovine tuberculosis (bTB) continues to have significant economic and veterinary health impacts on cattle herds where the disease remains endemic. The continual tailoring of policies to address such maintenance requires an in-depth analysis of national data, underpinning new control strategies. In Ireland, when outbreaks occur, ancillary testing of herd mates deemed to be at the highest risk of exposure to reactors is undertaken using the interferon gamma (GIF) test. This highest risk cohort was hypothesised to be of a higher future risk despite this ancillary testing. We used a dataset from Ireland to model bovine test failure to the comparative tuberculin skin test using a survival analysis (observations: 39,248). Our primary exposure of interest was whether an animal that tested negative had a GIF test after the disclosure of infection within a herd during a bTB breakdown. There was evidence that animals with a negative GIF test during a breakdown had an increased risk of failing a test relative to other animals from the same herds without this exposure. The time to failure was 48.8% (95%CI: 38.3-57.5%) shorter for the exposed group relative to the unexposed group during a two-year follow-up period (2019-2022; time ratio: 0.51; 95%CI: 0.43-0.62; p < 0.001). The results from this study suggest that animals who were GIF-tested, having been deemed to have a higher risk of exposure, subsequently had shorter time-to-test failure periods. The absolute numbers of failure are small (only 2.5% of animals go on to fail during 2-year follow-up). Importantly, however, a high proportion of these high-risk herds included in the dataset failed at least one test at the follow-up (21/54 herds), impacting breakdown duration or recurrence. Such risk-informed targeting of animals could be utilised in future control policies, though further research is warranted.

Quantification of the effect of in utero events on lifetime resilience in dairy cows

Currently, the dairy industry is facing many challenges that could affect its sustainability, including climate change and public perception of the industry. As a result, interest is increasing in the concept of identifying resilient animals, those with a long productive lifespan, as well as good reproductive performance and milk yield. There is much evidence that events in utero, that is, the developmental origins of health and disease hypothesis, alter the life-course health of offspring and we hypothesized that these could alter resilience in calves, where resilience is identified using lifetime data. The aim of this study was to quantify lifetime resilience scores (LRS) using an existing scoring system, based on longevity with secondary corrections for age at first calving and calving interval, and to quantify the effects of in utero events on the LRS using 2 datasets. The first was a large dataset of cattle on 83 farms in Great Britain born from 2006 to 2015 and the second was a smaller, more granular dataset of cattle born between 2003 and 2015 in the Langhill research herd at Scotland's Rural College. Events during dam's pregnancy included health events (lameness, mastitis, use of an antibiotic or anti-inflammatory medication), the effect of heat stress as measured by temperature-humidity index, and perturbations in milk yield and quality (somatic cell count, percentage fat, percentage protein and fat:protein ratio). Daughters born to dams that experienced higher temperature-humidity indexes while they were in utero during the first and third trimesters of pregnancy had lower LRS. Daughter LRS were also lower where milk yields or median fat percentages in the first trimester were low, and when milk yields were high in the third trimester. Dam LRS was positively associated with LRS of their offspring; however, as parity of the dam increased, LRS of their calves decreased. Similarly, in the Langhill herd, dams of a higher parity produced calves with lower LRS. Additionally, dams that recorded a high maximum locomotion score in the third trimester of pregnancy were negatively associated with lower calf LRS in the Langhill herd. Our results suggest that events that occur during pregnancy have lifelong consequences for the calf's lifetime performance. However, experience of higher temperature-humidity indexes, higher dam LRS, and mothers in higher parities explained a relatively small proportion of variation in offspring LRS, which suggests that other factors play a substantial role in determining calf LRS. Although "big data" can contain a considerable amount of noise, similar findings between the 2 datasets indicate it is likely these findings are real.

Investigating the uses of machine learning algorithms to inform risk factor analyses: The example of avian infectious bronchitis virus (IBV) in broiler chickens

Infectious bronchitis virus (IBV) is a contagious coronavirus causing respiratory and urogenital disease in chickens and is responsible for significant economic losses for both the broiler and table egg layer industries. Despite IBV being regularly monitored using standard epidemiologic surveillance practices, knowledge and evidence of risk factors associated with IBV transmission remain limited. The study objective was to compare risk factor modeling outcomes between a traditional stepwise variable selection approach and a machine learning-based random forest Boruta algorithm using routinely collected IBV antibody titer data from broiler flocks. IBV antibody sampling events (n = 1111) from 166 broiler sites between 2016 and 2021 were accessed. Ninety-two geospatial-related and poultry-density variables were obtained using a geographic information system and data sets from publicly available sources. Seventeen and 27 candidate variables were screened to potentially have an association with elevated IBV antibody titers according to the manual selection and machine learning algorithm, respectively. Selected variables from both methods were further investigated by construction of multivariable generalized mixed logistic regression models. Six variables were shortlisted by both screening methods, which included year, distance to urban areas, main roads, landcover, density of layer sites and year, however, final models for both approaches only shared year as an important predictor. Despite limited significance of clinical outcomes, this work showcases the potential of a novel explorative modeling approach in combination with often unutilized resources such as publicly available geospatial data, surveillance health data and machine learning as potential supplementary tools to investigate risk factors related to infectious diseases.

Livestock and environmental characterization of Colombian municipalities: study of vesicular stomatitis

Amid the surge in data volume generated across various fields of knowledge, there is an increasing necessity for advanced analytical methodologies to effectively process and utilize this information. Particularly in the field of animal health, this approach is pivotal for enhancing disease understanding, surveillance, and management. The main objective of the study was to conduct a comprehensive livestock and environmental characterization of Colombian municipalities and examine their relationship with the distribution of vesicular stomatitis (VS). Utilizing satellite imagery to delineate climatic and land use profiles, along with data from the Colombian Agricultural Institute (ICA) concerning animal populations and their movements, the research employed Principal Component Analysis (PCA) to explore the correlation between environmental and livestock-related variables. Additionally, municipalities were grouped through a Hierarchical Clustering process. The assessment of risk associated with VS was carried out using a Generalized Linear Model. This process resulted in the formation of four distinct clusters: three primarily characterized by climatic attributes and one predominantly defined by livestock characteristics. Cluster 1, identified as "Andino" due to its climatic and environmental features, exhibited the highest odds ratio for VS occurrence. The adopted methodology not only provides a deeper understanding of the local population and its context, but also offers valuable insights for enhancing disease surveillance and control programs.

Attributing Ethiopian animal health losses to high-level causes using expert elicitation

The Global Burden of Animal Diseases programme is currently working to estimate the burden of animal health loss in Ethiopia. As part of this work, structured expert elicitation has been trialled to attribute the proportion of animal health losses due to three independent and exhaustive high-level causes (infectious, non-infectious, and external). Separate in-person workshops were conducted with eight cattle, nine small ruminant, and eight chicken experts. Following the Investigate-Discuss-Estimate-Aggregate protocol for structured expert elicitation, estimates were obtained for the proportion of animal health loss due to high-level causes in different combinations of health loss, species, age-sex class, and production system. Three-point questions were used to inform beta-pert distributions and capture uncertainty in estimates. Individual expert estimates were aggregated by quantile mean to produce average distributions. Random samples from these average distributions estimated that infectious causes inflict the highest proportion of health loss in Ethiopia, with at least 40 % of health losses estimated to be due to infectious causes in all categories. This study provides a rapid, simple, and engaging method to attribute the burden of animal health loss at a high-level. Results are informative, however will become increasingly useful once they can be compared with results from more sophisticated, data-driven models.

When Everything Becomes Bigger: Big Data for Big Poultry Production

In future decades, the demand for poultry meat and eggs is predicted to considerably increase in pace with human population growth. Although this expansion clearly represents a remarkable opportunity for the sector, it conceals a multitude of challenges. Pollution and land erosion, competition for limited resources between animal and human nutrition, animal welfare concerns, limitations on the use of growth promoters and antimicrobial agents, and increasing risks and effects of animal infectious diseases and zoonoses are several topics that have received attention from authorities and the public. The increase in poultry production must be achieved mainly through optimization and increased efficiency. The increasing ability to generate large amounts of data ("big data") is pervasive in both modern society and the farming industry. Information accessibility-coupled with the availability of tools and computational power to store, share, integrate, and analyze data with automatic and flexible algorithms-offers an unprecedented opportunity to develop tools to maximize farm profitability, reduce socio-environmental impacts, and increase animal and human health and welfare. A detailed description of all topics and applications of big data analysis in poultry farming would be infeasible. Therefore, the present work briefly reviews the application of sensor technologies, such as optical, acoustic, and wearable sensors, as well as infrared thermal imaging and optical flow, to poultry farming. The principles and benefits of advanced statistical techniques, such as machine learning and deep learning, and their use in developing effective and reliable classification and prediction models to benefit the farming system, are also discussed. Finally, recent progress in pathogen genome sequencing and analysis is discussed, highlighting practical applications in epidemiological tracking, and reconstruction of microorganisms' population dynamics, evolution, and spread. The benefits of the objective evaluation of the effectiveness of applied control strategies are also considered. Although human-artificial intelligence collaborations in the livestock sector can be frightening because they require farmers and employees in the sector to adapt to new roles, challenges, and competencies-and because several unknowns, limitations, and open-ended questions are inevitable-their overall benefits appear to be far greater than their drawbacks. As more farms and companies connect to technology, artificial intelligence (AI) and sensing technologies will begin to play a greater role in identifying patterns and solutions to pressing problems in modern animal farming, thus providing remarkable production-based and commercial advantages. Moreover, the combination of diverse sources and types of data will also become fundamental for the development of predictive models able to anticipate, rather than merely detect, disease occurrence. The increasing availability of sensors, infrastructures, and tools for big data collection, storage, sharing, and analysis-together with the use of open standards and integration with pathogen molecular epidemiology-have the potential to address the major challenge of producing higher-quality, more healthful food on a larger scale in a more sustainable manner, thereby protecting ecosystems, preserving natural resources, and improving animal and human welfare and health.

WildHealthNet: Supporting the development of sustainable wildlife health surveillance networks in Southeast Asia

Wildlife and wildlife interfaces with people and livestock are essential surveillance targets to monitor emergent or endemic pathogens or new threats affecting wildlife, livestock, and human health. However, limitations of previous investments in scope and duration have resulted in a neglect of wildlife health surveillance (WHS) systems at national and global scales, particularly in lower and middle income countries (LMICs). Building on decades of wildlife health activities in LMICs, we demonstrate the implementation of a locally-driven multi-pronged One Health approach to establishing WHS in Cambodia, Lao PDR and Viet Nam under the WildHealthNet initiative. WildHealthNet utilizes existing local capacity in the animal, public health, and environmental sectors for event based or targeted surveillance and disease detection. To scale up surveillance systems to the national level, WildHealthNet relies on iterative field implementation and policy development, capacity bridging, improving data collection and management systems, and implementing context specific responses to wildlife health intelligence. National WHS systems piloted in Cambodia, Lao PDR, and Viet Nam engaged protected area rangers, wildlife rescue centers, community members, and livestock and human health sector staff and laboratories. Surveillance activities detected outbreaks of H5N1 highly pathogenic avian influenza in wild birds, African swine fever in wild boar (Sus scrofa), Lumpy skin disease in banteng (Bos javanicus), and other endemic zoonotic pathogens identified as surveillance priorities by local stakeholders. In Cambodia and Lao PDR, national plans for wildlife disease surveillance are being signed into legislation. Cross-sectoral and trans-disciplinary approaches are needed to implement effective WHS systems. Long-term commitment, and paralleled implementation and policy development are key to sustainable WHS networks. WildHealthNet offers a roadmap to aid in the development of locally-relevant and locally-led WHS systems that support the global objectives of the World Organization for Animal Health's Wildlife Health Framework and other international agendas.

Connecting livestock disease dynamics to human learning and biosecurity decisions

The acceleration of animal disease spread worldwide due to increased animal, feed, and human movement has driven a growing body of epidemiological research as well as a deeper interest in human behavioral studies aimed at understanding their interconnectedness. Biosecurity measures can reduce the risk of infection, but human risk tolerance can hinder biosecurity investments and compliance. Humans may learn from hardship and become more risk averse, but sometimes they instead become more risk tolerant because they forget negative experiences happened in the past or because they come to believe they are immune. We represent the complexity of the hog production system with disease threats, human decision making, and human risk attitude using an agent-based model. Our objective is to explore the role of risk tolerant behaviors and the consequences of delayed biosecurity investments. We set up experiment with Monte Carlo simulations of scenarios designed with different risk tolerance amongst the swine producers and we derive distributions and trends of biosecurity and porcine epidemic diarrhea virus (PEDv) incidence emerging in the system. The output data allowed us to examine interactions between modes of risk tolerance and timings of biosecurity response discussing consequences for disease protection in the production system. The results show that hasty and delayed biosecurity responses or slow shifts toward a biosecure culture do not guarantee control of contamination when the disease has already spread in the system. In an effort to support effective disease prevention, our model results can inform policy making to move toward more resilient and healthy production systems. The modeled dynamics of risk attitude have also the potential to improve communication strategies for nudging and establishing risk averse behaviors thereby equipping the production system in case of foreign disease incursions.

ASAS-NANP symposium: mathematical modeling in animal nutrition-Making sense of big data and machine learning: how open-source code can advance training of animal scientists

Advancements in precision livestock technology have resulted in an unprecedented amount of data being collected on individual animals. Throughout the data analysis chain, many bottlenecks occur, including processing raw sensor data, integrating multiple streams of information, incorporating data into animal growth and nutrition models, developing decision support tools for producers, and training animal science students as data scientists. To realize the promise of precision livestock management technologies, open-source tools and tutorials must be developed to reduce these bottlenecks, which are a direct result of the tremendous time and effort required to create data pipelines from scratch. Open-source programming languages (e.g., R or Python) can provide users with tools to automate many data processing steps for cleaning, aggregating, and integrating data. However, the steps from data collection to training artificial intelligence models and integrating predictions into mathematical models can be tedious for those new to statistical programming, with few examples pertaining to animal science. To address this issue, we outline how open-source code can help overcome many of the bottlenecks that occur in the era of big data and precision livestock technology, with an emphasis on how routine use and publication of open-source code can help facilitate training the next generation of animal scientists. In addition, two case studies are presented with publicly available data and code to demonstrate how open-source tutorials can be utilized to streamline data processing, train machine learning models, integrate with animal nutrition models, and facilitate learning. The National Animal Nutrition Program focuses on providing research-based data on animal performance and feeding strategies. Open-source data and code repositories with examples specific to animal science can help create a reinforcing mechanism aimed at advancing animal science research.

Australian veterinarians' perspectives on the contribution of the veterinary workforce to the Australian animal health surveillance system

This study investigated the involvement of private veterinarians in surveillance activities and the veterinary workforce's contribution to the Australian animal health surveillance system. The perception that there is overall a decreased engagement by veterinarians in surveillance outcomes at a time when there is increased need for bolstering of surveillance systems was investigated. Three key questions were considered: (1) What is the current contribution of private veterinarians to the Australian surveillance system? (2) What is the veterinary professions capacity to assume a more prominent role in surveillance? (3) What is the interest and ability of the veterinary profession in Australia to undertake this surveillance role now and into the future? Semi-structured telephone interviews were conducted with 17 private veterinarians with data analyzed qualitatively to identify key themes. Results demonstrate that private veterinarians are aware of their responsibilities and are engaged in surveillance activities at both formal and informal levels. The key challenges associated with current and future contributions were related to workload, remuneration, conflicts of interest and clarity over how responsibility for surveillance is shared amongst those involved in the system. The study has demonstrated that even amongst an engaged population, barriers do need to be addressed if private veterinarians are to be tasked with increasing their involvement in animal health surveillance activities.

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.

The Contribution of Data-Driven Technologies in Achieving the Sustainable Development Goals

The United Nations’ Sustainable Development Goals (SDGs) set out to improve the quality of life of people in developed, emerging, and developing countries by covering social and economic aspects, with a focus on environmental sustainability. At the same time, data-driven technologies influence our lives in all areas and have caused fundamental economical and societal changes. This study presents a comprehensive literature review on how data-driven approaches have enabled or inhibited the successful achievement of the 17 SDGs to date. Our findings show that data-driven analytics and tools contribute to achieving the 17 SDGs, e.g., by making information more reliable, supporting better-informed decision-making, implementing data-based policies, prioritizing actions, and optimizing the allocation of resources. Based on a qualitative content analysis, results were aggregated into a conceptual framework, including the following categories: (1) uses of data-driven methods (e.g., monitoring, measurement, mapping or modeling, forecasting, risk assessment, and planning purposes), (2) resulting positive effects, (3) arising challenges, and (4) recommendations for action to overcome these challenges. Despite positive effects and versatile applications, problems such as data gaps, data biases, high energy consumption of computational resources, ethical concerns, privacy, ownership, and security issues stand in the way of achieving the 17 SDGs.

Man's best friend in life and death: scientific perspectives and challenges of dog brain banking

Biobanking refers to the systematic collection, storage, and distribution of pre- or post-mortem biological samples derived from volunteer donors. The demand for high-quality human specimens is clearly demonstrated by the number of newly emerging biobanking facilities and large international collaborative networks. Several animal species are relevant today in medical research; therefore, similar initiatives in comparative physiology could be fruitful. Dogs, in particular, are gaining increasing attention in translational research on complex phenomena, like aging, cancer, and neurodegenerative diseases. Therefore, biobanks gathering and storing dog biological materials together with related data could play a vital role in translational and veterinary research projects. To achieve these aims, a canine biobank should meet the same standards in sample quality and data management as human biobanks and should rely on well-designed collaborative networks between different professionals and dog owners. While efforts to create dog biobanks could face similar financial and technical challenges as their human counterparts, they can widen the spectrum of successful collaborative initiatives towards a better picture of dogs’ physiology, disease, evolution, and translational potential. In this review, we provide an overview about the current state of dog biobanking and introduce the “Canine Brain and Tissue Bank” (CBTB)—a new, large-scale collaborative endeavor in the field.

Dogs as Pets and Pests: Global Patterns of Canine Abundance, Activity, and Health

Dogs (Canis familiaris) were the first domesticated species and, at an estimated population of 1 billion individuals, are globally ubiquitous today. Describing the tremendous morphometric diversity and evolutionary origins of dogs is a scientific endeavor that predates Darwin, yet our interdisciplinary understanding of the species is just beginning. Here, I present global trends in dog abundance, activity, and health. While the human-dog relationship has for millennia been close, it is also complicated. As pets, companion dogs are often treated as family members and constitute the largest sector of the ever-growing >$200 billion USD global pet care industry. As pests, free-roaming dogs are an emerging threat to native species via both predation and nonconsumptive effects (e.g., disturbance, competition for resources, and hybridization). Furthermore, I briefly discuss mounting evidence of dogs as not only infectious disease reservoirs but also as bridges for the transmission of pathogens between wild animals and humans in zoonotic spillover events, triggering intensive dog population management strategies such as culling. Dog mobility across the urban-wildland interface is an important driver for this and other adverse effects of canines on wildlife populations and is an active topic of disease ecologists and conservation biologists. Other canine scientists, including veterinary clinicians and physiologists, study more mechanistic aspects of dog mobility: the comparative kinetics, kinematics, and energetics of dog locomotor health. I outline the prevalent methodological approaches and breed-specific findings within dog activity and health research, then conclude by recognizing promising technologies that are bridging disciplinary gaps in canine science.

Data-Driven Surveillance: Effective Collection, Integration, and Interpretation of Data to Support Decision Making

The biggest change brought about by the “era of big data” to health in general, and epidemiology in particular, relates arguably not to the volume of data encountered, but to its variety. An increasing number of new data sources, including many not originally collected for health purposes, are now being used for epidemiological inference and contextualization. Combining evidence from multiple data sources presents significant challenges, but discussions around this subject often confuse issues of data access and privacy, with the actual technical challenges of data integration and interoperability. We review some of the opportunities for connecting data, generating information, and supporting decision-making across the increasingly complex “variety” dimension of data in population health, to enable data-driven surveillance to go beyond simple signal detection and support an expanded set of surveillance goals.

Research perspectives on animal health in the era of artificial intelligence

Leveraging artificial intelligence (AI) approaches in animal health (AH) makes it possible to address highly complex issues such as those encountered in quantitative and predictive epidemiology, animal/human precision-based medicine, or to study host × pathogen interactions. AI may contribute (i) to diagnosis and disease case detection, (ii) to more reliable predictions and reduced errors, (iii) to representing more realistically complex biological systems and rendering computing codes more readable to non-computer scientists, (iv) to speeding-up decisions and improving accuracy in risk analyses, and (v) to better targeted interventions and anticipated negative effects. In turn, challenges in AH may stimulate AI research due to specificity of AH systems, data, constraints, and analytical objectives. Based on a literature review of scientific papers at the interface between AI and AH covering the period 2009-2019, and interviews with French researchers positioned at this interface, the present study explains the main AH areas where various AI approaches are currently mobilised, how it may contribute to renew AH research issues and remove methodological or conceptual barriers. After presenting the possible obstacles and levers, we propose several recommendations to better grasp the challenge represented by the AH/AI interface. With the development of several recent concepts promoting a global and multisectoral perspective in the field of health, AI should contribute to defract the different disciplines in AH towards more transversal and integrative research.

One Digital Health: A Unified Framework for Future Health Ecosystems

One Digital Health is a proposed unified structure. The conceptual framework of the One Digital Health Steering Wheel is built around two keys (ie, One Health and digital health), three perspectives (ie, individual health and well-being, population and society, and ecosystem), and five dimensions (ie, citizens’ engagement, education, environment, human and veterinary health care, and Healthcare Industry 4.0). One Digital Health aims to digitally transform future health ecosystems, by implementing a systemic health and life sciences approach that takes into account broad digital technology perspectives on human health, animal health, and the management of the surrounding environment. This approach allows for the examination of how future generations of health informaticians can address the intrinsic complexity of novel health and care scenarios in digitally transformed health ecosystems. In the emerging hybrid landscape, citizens and their health data have been called to play a central role in the management of individual-level and population-level perspective data. The main challenges of One Digital Health include facilitating and improving interactions between One Health and digital health communities, to allow for efficient interactions and the delivery of near–real-time, data-driven contributions in systems medicine and systems ecology. However, digital health literacy; the capacity to understand and engage in health prevention activities; self-management; and collaboration in the prevention, control, and alleviation of potential problems are necessary in systemic, ecosystem-driven public health and data science research. Therefore, people in a healthy One Digital Health ecosystem must use an active and forceful approach to prevent and manage health crises and disasters, such as the COVID-19 pandemic.

Use of Network Analysis and Spread Models to Target Control Actions for Bovine Tuberculosis in a State from Brazil

Livestock movements create complex dynamic interactions among premises that can be represented, interpreted, and used for epidemiological purposes. These movements are a very important part of the production chain but may also contribute to the spread of infectious diseases through the transfer of infected animals over large distances. Social network analysis (SNA) can be used to characterize cattle trade patterns and to identify highly connected premises that may act as hubs in the movement network, which could be subjected to targeted control measures in order to reduce the transmission of communicable diseases such as bovine tuberculosis (TB). Here, we analyzed data on cattle movement and slaughterhouse surveillance for detection of TB-like lesions (TLL) over the 2016-2018 period in the state of Rio Grande do Sul (RS) in Brazil with the following aims: (i) to characterize cattle trade describing the static full, yearly, and monthly snapshots of the network contact trade, (ii) to identify clusters in the space and contact networks of premises from which animals with TLL originated, and (iii) to evaluate the potential of targeted control actions to decrease TB spread in the cattle population of RS using a stochastic metapopulation disease transmission model that simulated within-farm and between-farm disease spread. We found heterogeneous densities of premises and animals in the study area. The analysis of the contact network revealed a highly connected (~94%) trade network, with strong temporal trends, especially for May and November. The TLL cases were significantly clustered in space and in the contact network, suggesting the potential for both local (e.g., fence-to-fence) and movement-mediated TB transmission. According to the disease spread model, removing the top 7% connected farms based on degree and betweenness could reduce the total number of infected farms over three years by >50%. In conclusion, the characterization of the cattle network suggests that highly connected farms may play a role in TB dissemination, although being close to infected farms was also identified as a risk factor for having animals with TLL. Surveillance and control actions based on degree and betweenness could be useful to break the transmission cycle between premises in RS.

Predicting WNV Circulation in Italy Using Earth Observation Data and Extreme Gradient Boosting Model

West Nile Disease (WND) is one of the most spread zoonosis in Italy and Europe caused by a vector-borne virus. Its transmission cycle is well understood, with birds acting as the primary hosts and mosquito vectors transmitting the virus to other birds, while humans and horses are occasional dead-end hosts. Identifying suitable environmental conditions across large areas containing multiple species of potential hosts and vectors can be difficult. The recent and massive availability of Earth Observation data and the continuous development of innovative Machine Learning methods can contribute to automatically identify patterns in big datasets and to make highly accurate identification of areas at risk. In this paper, we investigated the West Nile Virus (WNV) circulation in relation to Land Surface Temperature, Normalized Difference Vegetation Index and Surface Soil Moisture collected during the 160 days before the infection took place, with the aim of evaluating the predictive capacity of lagged remotely sensed variables in the identification of areas at risk for WNV circulation. WNV detection in mosquitoes, birds and horses in 2017, 2018 and 2019, has been collected from the National Information System for Animal Disease Notification. An Extreme Gradient Boosting model was trained with data from 2017 and 2018 and tested for the 2019 epidemic, predicting the spatio-temporal WNV circulation two weeks in advance with an overall accuracy of 0.84. This work lays the basis for a future early warning system that could alert public authorities when climatic and environmental conditions become favourable to the onset and spread of WNV.

Analysis of puerperal metritis treatment records in a grazing dairy farm in Argentina

OBJECTIVE

To assess the efficacy of antibiotic usage for the treatment of puerperal metritis (PM) and its association with reproductive performance, a retrospective cohort study including a total of 9168 records of cows from a dairy farm in Argentina was run.

MATERIAL AND METHODS

Cows having a PM3 (metricheck, scale 0-3) and treated with ceftiofur (ceftiofur crystalline free acid, 6.6 mg/kg) at 0-21 days postpartum (p. p.) (n = 2688), and cows having a PM 1-2 and not treated with an antibiotic at 0-21 days p. p. (n = 6480) were included in the study. All cows were reexamined with metricheck to assess the clinical cure (vaginal discharge [VD] score 0), partial cure (VD score similar or lower than previous), no cure (VD score higher than previous). Cows with a metricheck VD1-3 after 0-21 days p. p. were diagnosed as clinical endometritis (CE) 1-3. The occurrence of PM1-3, cure rate, calving to conception interval, the hazard of pregnancy, odds for non-pregnancy, and odds for CE were analyzed using SAS software.

RESULTS

A total of 8876 PM1-3 records were included, 2435 records of PM3 treatments with ceftiofur (27.43 %), and 6441 records of PM1-2 (72.57 %) with no treatment. Cows having PM1 and PM2 became pregnant 14 and 12 days earlier than cows with PM3 (p < 0.001). The PM3 ceftiofur treated cows had a clinical cure of 24.85 % (PM0); 53.63 % had a partially cure; and 18.52 % no cure. Conversely, cows with PM1-2 had a 51.96 %, 20.70 %, and 24.53 % cure rate, respectively (p < 0.001). Cows having complete cure became pregnant 13 and 11 days earlier than cows having partial cure and no cure (p < 0.001). Cows that had PM3 during the first 21 days p. p. had twice the chances of developing CE compared to cows having PM1-2 (41.28 % vs. 24.14 %, p < 0.001). After 21 days p. p., less than 1 % of cows with clinical cure developed CE compared to 63.32 % that developed CE with partial cure, and 38.21 % with no cure (p < 0.001).

CONCLUSION AND CLINICAL RELEVANCE

After ceftiofur treatment, 78 % of cows were cured when measured by disappearance of fetid VD but only 25 % of cows had clinical cure when measured by appearance of a clear VD. The cows that remained with clinical metritis had more chances of having CE after 21 days p. p. and had more days open than cows with clear normal VD.

Prediction of seasonal patterns of porcine reproductive and respiratory syndrome virus RNA detection in the U.S. swine industry

We developed a model to predict the cyclic pattern of porcine reproductive and respiratory syndrome virus (PRRSV) RNA detection by reverse-transcription real-time PCR (RT-rtPCR) from 4 major swine-centric veterinary diagnostic laboratories (VDLs) in the United States and to use historical data to forecast the upcoming year's weekly percentage of positive submissions and issue outbreak signals when the pattern of detection was not as expected. Standardized submission data and test results were used. Historical data (2015-2017) composed of the weekly percentage of PCR-positive submissions were used to fit a cyclic robust regression model. The findings were used to forecast the expected weekly percentage of PCR-positive submissions, with a 95% confidence interval (CI), for 2018. During 2018, the proportion of PRRSV-positive submissions crossed 95% CI boundaries at week 2, 14-25, and 48. The relatively higher detection on week 2 and 48 were mostly from submissions containing samples from wean-to-market pigs, and for week 14-25 originated mostly from samples from adult/sow farms. There was a recurring yearly pattern of detection, wherein an increased proportion of PRRSV RNA detection in submissions originating from wean-to-finish farms was followed by increased detection in samples from adult/sow farms. Results from the model described herein confirm the seasonal cyclic pattern of PRRSV detection using test results consolidated from 4 VDLs. Wave crests occurred consistently during winter, and wave troughs occurred consistently during the summer months. Our model was able to correctly identify statistically significant outbreak signals in PRRSV RNA detection at 3 instances during 2018.

Automated prediction of mastitis infection patterns in dairy herds using machine learning

Mastitis in dairy cattle is extremely costly both in economic and welfare terms and is one of the most significant drivers of antimicrobial usage in dairy cattle. A critical step in the prevention of mastitis is the diagnosis of the predominant route of transmission of pathogens into either contagious (CONT) or environmental (ENV), with environmental being further subdivided as transmission during either the nonlactating “dry” period (EDP) or lactating period (EL). Using data from 1000 farms, random forest algorithms were able to replicate the complex herd level diagnoses made by specialist veterinary clinicians with a high degree of accuracy. An accuracy of 98%, positive predictive value (PPV) of 86% and negative predictive value (NPV) of 99% was achieved for the diagnosis of CONT vs ENV (with CONT as a “positive” diagnosis), and an accuracy of 78%, PPV of 76% and NPV of 81% for the diagnosis of EDP vs EL (with EDP as a “positive” diagnosis). An accurate, automated mastitis diagnosis tool has great potential to aid non-specialist veterinary clinicians to make a rapid herd level diagnosis and promptly implement appropriate control measures for an extremely damaging disease in terms of animal health, productivity, welfare and antimicrobial use.

Near Real-Time Monitoring of Clinical Events Detected in Swine Herds in Northeastern Spain

Novel techniques of data mining and time series analyses allow the development of new methods to analyze information relating to the health status of the swine population in near real-time. A swine health monitoring system based on the reporting of clinical events detected at farm level has been in operation in Northeastern Spain since 2012. This initiative was supported by swine stakeholders and veterinary practitioners of the Catalonia, Aragon, and Navarra regions. The system aims to evidence the occurrence of endemic diseases in near real-time by gathering data from practitioners that visited swine farms in these regions. Practitioners volunteered to report data on clinical events detected during their visits using a web application. The system allowed collection, transfer and storage of data on different clinical signs, analysis, and modeling of the diverse clinical events detected, and provision of reproducible reports with updated results. The information enables the industry to quantify the occurrence of endemic diseases on swine farms, better recognize their spatiotemporal distribution, determine factors that influence their presence and take more efficient prevention and control measures at region, county, and farm level. This study assesses the functionality of this monitoring tool by evaluating the target population coverage, the spatiotemporal patterns of clinical signs and presumptive diagnoses reported by practitioners over more than 6 years, and describes the information provided by this system in near real-time. Between January 2012 and March 2018, the system achieved a coverage of 33 of the 62 existing counties in the three study regions. Twenty-five percent of the target swine population farms reported one or more clinical events to the system. During the study period 10,654 clinical events comprising 14,971 clinical signs from 1,693 farms were reported. The most frequent clinical signs detected in these farms were respiratory, followed by digestive, neurological, locomotor, reproductive, and dermatological signs. Respiratory disorders were mainly associated with microorganisms of the porcine respiratory disease complex. Digestive signs were mainly related to colibacilosis and clostridiosis, neurological signs to Glässer's disease and streptococcosis, reproductive signs to PRRS, locomotor to streptococcosis and Glässer's disease, and dermatological signs to exudative epidermitis.

An animal health example of managing and analyzing a large volume of data on a PC: Modeling body weight and age of over 13 million cats for explanatory and predictive purposes

Large amounts of animal health data are available to researchers, but are often stored in different formats and information silos. Analysis of this existing information can provide new insights into the health and welfare of animals and possibly reduce the need to collect additional data. The objective of this study was to develop a method of managing and analyzing large amounts of data on a personal computer that can be run within 24 h to limit the time and resources spent deploying models on larger servers. This paper describes an overall approach that makes use of existing methods for data acquisition and modeling, but adapts and combines them in a way that allows manipulation and analysis of large volumes of data on a PC. This included a total of five steps: removing errors; removing data points outside the scope of a specific hypothesis; creating descriptive statistics; developing explanatory and/or predictive models; and assessing the fit or accuracy of the models created. The approach was developed using electronic medical records for 19,416,753 feline patients from 3972 anonymized veterinary clinics in the United States and Canada, recorded between January 1981 and June 2016. Data regarding patient signalment (age, sex, breed, reproductive status) and body weight were extracted from the records and used to create linear regression models to describe body weight in cats of different ages, breeds, genders and reproductive status. Ordinary least squares linear regression and stochastic gradient descent linear regression were compared to determine their effectiveness and suitability for creating predictive models with large datasets, using 10 fold cross validation. This approach could be used to build workflows to create models to determine exploratory and predictive properties of health parameters for animals and people. The ability to work with large datasets on a PC or equivalent technology was demonstrated. Significant interactions were present among sex, reproductive status and age. A peak in weight occurred between 6 and 9 years depending on the sex, reproductive status and breed. The predictive ability of the two models was similar, with both producing a root mean square error of 1.45 and a mean absolute error of 1.09, and mean error that was approximately zero on the validation dataset.

Unweaving tangled mortality and antibiotic consumption data to detect disease outbreaks - Peaks, growths, and foresight in swine production

As our capacity to collect and store health data is increasing, a new challenge of transforming data into meaningful information for disease monitoring and surveillance has arisen. The aim of this study was to explore the potential of using livestock mortality and antibiotic consumption data as a proxy for detecting disease outbreaks at herd level. Changes in the monthly records of mortality and antibiotic consumption were monitored in Danish swine herds that became positive for porcine reproductive and respiratory syndrome (PRRS) and porcine pleuropneumonia. Laboratory serological results were used to identify herds that changed from a negative to a positive status for the diseases. A dynamic linear model with a linear growth component was used to model the data. Alarms about state changes were raised based on forecast errors, changes in the growth component, and the values of the retrospectively smoothed values of the growth component. In all cases, the alarms were defined based on credible intervals and assessed prior and after herds got a positive disease status. The number of herds with alarms based on mortality increased by 3% in the 3 months prior to laboratory confirmation of PRRS-positive herds (Se = 0.47). A 22% rise in the number of weaner herds with alarms based on the consumption of antibiotics for respiratory diseases was found 1 month prior to these herds becoming PRRS-positive (Se = 0.22). For porcine pleuropneumonia-positive herds, a 10% increase in antibiotic consumption for respiratory diseases in sow herds was seen 1 month prior to a positive result (Se = 0.5). Monitoring changes in mortality data and antibiotic consumption showed changes at herd level prior to and in the same month as confirmation from diagnostic tests. These results also show a potential value for using these data streams as part of surveillance strategies.

A Transdisciplinary Approach Supporting the Implementation of a Big Data Project in Livestock Production: An Example From the Swiss Pig Production Industry

Big Data approaches offer potential benefits for improving animal health, but they have not been broadly implemented in livestock production systems. Privacy issues, the large number of stakeholders, and the competitive environment all make data sharing, and integration a challenge in livestock production systems. The Swiss pig production industry illustrates these and other Big Data issues. It is a highly decentralized and fragmented complex network made up of a large number of small independent actors collecting a large amount of heterogeneous data. Transdisciplinary approaches hold promise for overcoming some of the barriers to implementing Big Data approaches in livestock production systems. The purpose of our paper is to describe the use of a transdisciplinary approach in a Big Data research project in the Swiss pig industry. We provide a brief overview of the research project named “Pig Data,” describing the structure of the project, the tools developed for collaboration and knowledge transfer, the data received, and some of the challenges. Our experience provides insight and direction for researchers looking to use similar approaches in livestock production system research.

A scoping review of 'big data', 'informatics', and 'bioinformatics' in the animal health and veterinary medical literature

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.

Leveraging Available Resources and Stakeholder Involvement for Improved Productivity of African Livestock in the Era of Genomic Breeding

The African continent is home to diverse populations of livestock breeds adapted to harsh environmental conditions with more than 70% under traditional systems of management. Animal productivity is less than optimal in most cases and is faced with numerous challenges including limited access to adequate nutrition and disease management, poor institutional capacities and lack of adequate government policies and funding to develop the livestock sector. Africa is home to about 1.3 billion people and with increasing demand for animal proteins by an ever growing human population, the current state of livestock productivity creates a significant yield gap for animal products. Although a greater section of the population, especially those living in rural areas depend largely on livestock for their livelihoods; the potential of the sector remains underutilized and therefore unable to contribute significantly to economic development and social wellbeing of the people. With current advances in livestock management practices, breeding technologies and health management, and with inclusion of all stakeholders, African livestock populations can be sustainably developed to close the animal protein gap that exists in the continent. In particular, advances in gene technologies, and application of genomic breeding in many Western countries has resulted in tremendous gains in traits like milk production with the potential that, implementation of genomic selection and other improved practices (nutrition, healthcare, etc.) can lead to rapid improvement in traits of economic importance in African livestock populations. The African livestock populations in the context of this review are limited to cattle, goat, pig, poultry, and sheep, which are mainly exploited for meat, milk, and eggs. This review examines the current state of livestock productivity in Africa, the main challenges faced by the sector, the role of various stakeholders and discusses in-depth strategies that can enable the application of genomic technologies for rapid improvement of livestock traits of economic importance.

Drivers for the development of an Animal Health Surveillance Ontology (AHSO)

Comprehensive reviews of syndromic surveillance in animal health have highlighted the hindrances to integration and interoperability among systems when data emerge from different sources. Discussions with syndromic surveillance experts in the fields of animal and public health, as well as computer scientists from the field of information management, have led to the conclusion that a major component of any solution will involve the adoption of ontologies. Here we describe the advantages of such an approach, and the steps taken to set up the Animal Health Surveillance Ontological (AHSO) framework. The AHSO framework is modelled in OWL, the W3C standard Semantic Web language for representing rich and complex knowledge. We illustrate how the framework can incorporate knowledge directly from domain experts or from data-driven sources, as well as by integrating existing mature ontological components from related disciplines. The development and extent of AHSO will be community driven and the final products in the framework will be open-access.

Identifying outbreaks of Porcine Epidemic Diarrhea virus through animal movements and spatial neighborhoods

The spread of pathogens in swine populations is in part determined by movements of animals between farms. However, understanding additional characteristics that predict disease outbreaks and uncovering landscape factors related to between-farm spread are crucial steps toward risk mitigation. This study integrates animal movements with environmental risk factors to identify the occurrence of porcine epidemic diarrhea virus (PEDV) outbreaks. Using weekly farm-level incidence data from 332 sow farms, we applied machine-learning algorithms to quantify associations between risk factors and PEDV outbreaks with the ultimate goal of training predictive models and to identify the most important factors associated with PEDV occurrence. Our best algorithm was able to correctly predict whether an outbreak occurred during one-week periods with >80% accuracy. The most important predictors included pig movements into neighboring farms. Other important neighborhood attributes included hog density, environmental and weather factors such as vegetation, wind speed, temperature, and precipitation, and topographical features such as slope. Our neighborhood-based approach allowed us to simultaneously capture disease risks associated with long-distance animal movement as well as local spatial dynamics. The model presented here forms the foundation for near real-time disease mapping and will advance disease surveillance and control for endemic swine pathogens in the United States.

Global trends in infectious diseases of swine

Pork accounts for more than one-third of meat produced worldwide and is an important component of global food security, agricultural economies, and trade. Infectious diseases are among the primary constraints to swine production, and the globalization of the swine industry has contributed to the emergence and spread of pathogens. Despite the importance of infectious diseases to animal health and the stability and productivity of the global swine industry, pathogens of swine have never been reviewed at a global scale. Here, we build a holistic global picture of research on swine pathogens to enhance preparedness and understand patterns of emergence and spread. By conducting a scoping review of more than 57,000 publications across 50 years, we identify priority pathogens globally and regionally, and characterize geographic and temporal trends in research priorities. Of the 40 identified pathogens, publication rates for eight pathogens increased faster than overall trends, suggesting that these pathogens may be emerging or constitute an increasing threat. We also compared regional patterns of pathogen prioritization in the context of policy differences, history of outbreaks, and differing swine health challenges faced in regions where swine production has become more industrialized. We documented a general increasing trend in importance of zoonotic pathogens and show that structural changes in the industry related to intensive swine production shift pathogen prioritization. Multinational collaboration networks were strongly shaped by region, colonial ties, and pig trade networks. This review represents the most comprehensive overview of research on swine infectious diseases to date.

A Smartphone-Based Application Improves the Accuracy, Completeness, and Timeliness of Cattle Disease Reporting and Surveillance in Ethiopia

Accurate disease reporting, ideally in near real time, is a prerequisite to detecting disease outbreaks and implementing appropriate measures for their control. This study compared the performance of the traditional paper-based approach to animal disease reporting in Ethiopia to one using an application running on smartphones. In the traditional approach, the total number of cases for each disease or syndrome was aggregated by animal species and reported to each administrative level at monthly intervals; while in the case of the smartphone application demographic information, a detailed list of presenting signs, in addition to the putative disease diagnosis were immediately available to all administrative levels via a Cloud-based server. While the smartphone-based approach resulted in much more timely reporting, there were delays due to limited connectivity; these ranged on average from 2 days (in well-connected areas) up to 13 days (in more rural locations). We outline the challenges that would likely be associated with any widespread rollout of a smartphone-based approach such as the one described in this study but demonstrate that in the long run the approach offers significant benefits in terms of timeliness of disease reporting, improved data integrity and greatly improved animal disease surveillance.