Despite the global adoption of the FAIR guiding principles, significant barriers remain in their practical implementation due to the lack of domain-specific standards for “rich metadata”. While generic metadata schemes provide basic information, they often fail to capture the complexity of data quality and the nuances of specialized scientific fields. This gap is particularly evident in veterinary epidemiology, a field characterized by complex, multi-scale data spanning various domains where detailed description of data analysis and modelling tend to be prioritized over raw data description, leading to inconsistent reporting and low data reusability. This paper introduces the first community-developed rich metadata guidelines specifically designed for veterinary epidemiology datasets, accompanied by practical templates and real-world examples. By integrating existing standards with specific guidance on domain-specific attributes and data quality, these guidelines provide a comprehensive, single-source framework accessible to researchers across academic, public, and private sectors. They aim to support researchers and stakeholders in enhancing the reusability of animal health data.

In veterinary epidemiology, using data routinely generated by stakeholders of the livestock production chains offers an opportunity for researchers to access a large amount of information that could be used to improve animal health. However, (re)using these non-scholarly data doesn’t come without challenges. This study assesses the reusability for research purposes of 30 European datasets generated by the livestock sector to meet legislative or operational needs. Information about each dataset was collected through a questionnaire survey filled by the data owner or the data user (researchers). Datasets were described, and their compliance with the FAIR principles, a data-sharing standard, and the principle of accountability defined in the General Data Protection Regulation were assessed. The study highlighted major gaps in terms of compliance with data regulations and implementation of good data management practices, specifically considering the rare use of metadata and standard vocabularies. Filling these gaps is essential to reap the full benefits offered by the rapidly growing volume of heterogeneous data available in livestock production systems.

IntroductionDigital clinical decision support (CDS) tools are of growing importance in supporting healthcare professionals in understanding complex clinical problems and arriving at decisions that improve patient outcomes. CDS tools are also increasingly used to improve antimicrobial stewardship (AMS) practices in healthcare settings. However, far fewer CDS tools are available in lowerand middle-income countries (LMICs) and in animal health settings, where their use in improving diagnostic and treatment decision-making is likely to have the greatest impact. The aim of this study was to evaluate digital CDS tools designed as a direct aid to support diagnosis and/or treatment decisionmaking, by reviewing their scope, functions, methodologies, and quality. Recommendations for the development of veterinary CDS tools in LMICs are then provided.MethodsThe review considered studies and reports published between January 2017 and October 2023 in the English language in peer-reviewed and gray literature.ResultsA total of 41 studies and reports detailing CDS tools were included in the final review, with 35 CDS tools designed for human healthcare settings and six tools for animal healthcare settings. Of the tools reviewed, the majority were deployed in high-income countries (80.5%). Support for AMS programs was a feature in 12 (29.3%) of the tools, with 10 tools in human healthcare settings. The capabilities of the CDS tools varied when reviewed against the GUIDES checklist.DiscussionWe recommend a methodological approach for the development of veterinary CDS tools in LMICs predicated on securing sufficient and sustainable funding. Employing a multidisciplinary development team is an important first step. Developing standalone CDS tools using Bayesian algorithms based on local expert knowledge will provide users with rapid and reliable access to quality guidance on diagnoses and treatments. Such tools are likely to contribute to improved disease management on farms and reduce inappropriate antimicrobial use, thus supporting AMS practices in areas of high need.

Highly pathogenic avian influenza (HPAI) poses a significant threat to both poultry and wild birds. To help tackle this challenge, an early warning system for HPAI in wild birds based on spatio-temporal risk mapping, the Bird Flu Radar, has previously been developed by EFSA. This work focuses on the expansion of the existing model to assess the risk of introduction and establishment of HPAI in poultry. First, a literature review was conducted to identify the risk factors for virus introduction from wild birds into poultry farms and the availability of associated data in Europe. Second, a theoretical modelling framework was developed to assess, on a grid of 50 x 50 km cells, the relative weekly probability of HPAI introduction in at least one domestic poultry flock because of infectious wild birds. This probability was estimated as the combination of two probabilities: the probability of HPAI entry into the flock and the probability of HPAI establishment in the domestic poultry population. The model outcomes are computed for all farms together but also for twelve types of farms separately. Farm types were defined based on their production type and poultry species kept. Italy and France were used a case study to test the model performance over one year of data (February 2023 to March 2024), comparing model predictions with outbreaks reported as primary outbreaks in the European Union (EU) Animal Disease Information System (ADIS). For Italy, the model performances were good, with all the outbreaks being detected in areas within or close to high-risk spatio-temporal units. The results obtained for France were more mixed: several outbreaks were reported in high-risk areas, but some were missed, apparently due to the high influence of some key model parameters and geographical specificity. Indeed, all the outbreaks reported in Southwest France were not predicted by the proposed model. These first results are encouraging, but future work should focus on finding ways to adjust certain model parameters and to improve the assessment of model performance considering a longer time period and/or including more robust input data.

In Indonesia, the development of the poultry industry is facing numerous challenges. Major constraints include high disease burdens, large fluctuations in farm input and output prices, and inadequate biosecurity. Timely and reliable information about animal production and health can help stakeholders at all levels of the value chain make appropriate management decisions to optimize their profitability and productivity while reducing risks to public health. This study aimed to describe the challenges in the Indonesian poultry industry, assess stakeholders' needs and capabilities in terms of generating and using poultry information for making production and health management decisions, and identify levers for improvement. Interviews were conducted with a diversity of key informants and value chain actors in five Indonesian provinces. Thematic analysis was applied with an interpretivist approach to gain an in-depth understanding of the lived experiences of various stakeholders and their opinions as to what might constitute appropriate solutions. Our findings indicate that market and political instability, ineffective management of poultry data, and limited inter-sectoral collaboration are limiting the development of the sector. Increased intersectoral cooperation is needed to implement standards for data collection and sharing across the industry, provide education and practical training on the use of information technologies for farm management, and accelerate research and innovation. Our study can contribute to the development of data-driven tools to support evidence-based decision-making at all levels of the poultry system.

Interventions to change antimicrobial use (AMU) practices can help mitigate the risk of antimicrobial resistance (AMR) development. However, changing AMU practices can be challenging due to the complex nature of the factors influencing AMU-related behaviours. This study used a qualitative approach to explore the factors that influenced decision-making on AMU by farmers and other actors in the Indonesian poultry sector. Thirty-five semi-structured interviews were conducted with farmers, technical services staff from the private sector, and representatives of associations, universities, and international organisations in Central Java, West Java, and East Java. Thematic analysis identified three patterns of influence on AMU: how farmers used information to make AMU-related decisions, the importance of farmers’ social and advisory networks, and the motivations driving changes in AMU behaviours. Key barriers identified included a lack of shared understanding around when to use antibiotics, financial pressures in the poultry sector, and a lack of engagement with government veterinary services. Potential opportunities identified included high farmer awareness of AMU, identification of private sector actors and peer networks as the stakeholders with established relationships of trust with farmers, and the importance of farmers’ conceptions of good farming practices, which could be engaged with to improve AMU practices.

Large blooms of the dinoflagellate Karenia brevis cause annual harmful algal bloom events, or "red tides" on Florida's Gulf Coast. Each year, the Clinic for the Rehabilitation of Wildlife (CROW) is presented with hundreds of cases of aquatic birds that exhibit neurologic clinical signs due to brevetoxicosis. Double-crested cormorants (Phalacrocorax auratus) are the most common species seen, and typically present with a combination of ataxia, head tremors, knuckling, and/or lagophthalmos. Blood lactate levels are known to increase in mammals for a variety of reasons, including stress, hypoxia, sepsis, and trauma, but there is limited literature on blood lactate values in avian species. The objective of this study was to determine the prognostic value of blood lactate concentration on successful rehabilitation and release of birds presenting with clinical signs consistent with brevetoxicosis. Blood lactate levels were collected on intake, the morning after presentation and initial therapy, and prior to disposition (release or euthanasia) from 194 birds (including 98 cormorants) representing 17 species during the 2020-2021 red tide season. Overall, mean blood lactate at intake, the morning after intake, and predisposition was 2.9, 2.8, and 3.2 mmol/L, respectively, for released birds across all species (2.9, 2.9, and 3.2 mmol/L for released cormorants); 3.4, 3.4, and 6.5 mmol/L for birds that died (4.0, 3.5, and 7.9 mmol/L for cormorants that died); and 3.1, 3.5, and 4.7 mmol/L for birds that were euthanized (3.5, 4.7, and 4.9 mmol/L for cormorants that were euthanized). On average, birds that died or were euthanized had an elevated lactate at all time points as compared to those that were released, but these results were not statistically significant (P = 0.13). These results indicate that blood lactate levels do not appear to be useful as a prognostic indicator for successful release of birds, including double-crested cormorants, affected by brevetoxicosis.

Avian influenza (AI) is a highly contagious viral disease that affects primarily poultry and wild water birds. There have been continued outbreaks of highly pathogenic avian influenza (HPAI) in both poultry and wild bird flocks. We aim to capitalise on the existence of large citizen datasets on abundance and distribution of 12 wild bird species to assess spatial patterns in abundance and migration migratory routes to develop a prototype spatiotemporal risk assessment from HPAI outbreaks in wild bird populations. We undertake an initial analysis of wild bird abundance from the EuroBirdPortal dataset and movements from the EURING Databank to parameterise a risk model that estimates the weekly probability of having at least one wild bird infected with HPAI at the European Environment Agency (EEA) 50 x 50 km grid scale. The model is validated against the EMPRES-i database of disease outbreaks. We provide proof of concept that such an approach is valuable and discuss current limitations and outline how the approach might be improved.

INTRODUCTION:Electronic information systems (EIS) that implement a 'One Health' approach by integrating antimicrobial resistance (AMR) data across the human, animal and environmental health sectors, have been identified as a global priority. However, evidence on the availability, technical capacities and effectiveness of such EIS is scarce.METHODS:Through a qualitative synthesis of evidence, this systematic scoping review aims to: identify EIS for AMR surveillance that operate across human, animal and environmental health sectors; describe their technical characteristics and capabilities; and assess whether there is evidence for the effectiveness of the various EIS for AMR surveillance. Studies and reports between 1 January 2000 and 21 July 2021 from peer-reviewed and grey literature in the English language were included.RESULTS:26 studies and reports were included in the final review, of which 27 EIS were described. None of the EIS integrated AMR data in a One Health approach across all three sectors. While there was a lack of evidence of thorough evaluations of the effectiveness of the identified EIS, several surveillance system effectiveness indicators were reported for most EIS. Standardised reporting of the effectiveness of EIS is recommended for future publications. The capabilities of the EIS varied in their technical design features, in terms of usability, data display tools and desired outputs. EIS that included interactive features, and geospatial maps are increasingly relevant for future trends in AMR data analytics.CONCLUSION:No EIS for AMR surveillance was identified that was designed to integrate a broad range of AMR data from humans, animals and the environment, representing a major gap in global efforts to implement One Health approaches to address AMR.

Antimicrobial resistance (AMR) is a complex issue where microorganisms survive antimicrobial treatments, making such infections more difficult to treat. It is a global threat to public health. To increase the evidence base for AMR in the food chain, the FSA has funded several projects to collect data to monitor the trends, prevalence, emergence, spread and decline of AMR bacteria in a range of retail foods in the UK. However, this data and information from the wider literature was yet to be used to create tools to aid in the production of quantitative risk assessment to determine the risk to consumers of AMR in the food chain. To assist with this, there was a need to develop a set of modular templates of risk of AMR within foods. This sought to allow the efficient creation of reproducible risk assessments of AMR to maintain the FSA at the forefront of food safety.

Sea lice infestation is a chronic production issue for the majority of salmonid farming industries worldwide. In Chile, Caligus rogercresseyi is the main sea lice species of concern. It is associated with poor welfare, reduced productivity, high treatment and management costs, and increased susceptibility to other diseases within the salmonid aquaculture industry. In response to increasing sea lice abundance, the Servicio Nacional de Pesca y Acuicultura (`Sernapesca', the Chilean National Fisheries and Aquaculture Service) implemented an official national surveillance program for C. rogercresseyi in 2007. Epidemiological studies were conducted in the early stages of this program and described the distribution of and risk factors for lice abundance. Since that time, there have been no further studies. The performance of the program compared to long-term trends in sea lice abundance is unknown. Our study used seven years of industry-wide regulatory data held in Sernapesca's Sistema de Fiscalizacion de la Acuicultura (SIFA) and Informe Ambiental para la Acuicultura (INFA) databases to confirm that previously identified risk factors for lice abundance are still relevant to the Chilean salmonid industry, and to generate hypotheses about previously unidentified risk factors for future investigations. A total of 63,437 sea lice sampling records were analysed within 1397 production cycles for Atlantic salmon, rainbow trout, coho salmon and king salmon. We used information-theoretic approaches for risk factor analysis and post hoc modelling to generate hypotheses for future investigations. Compared to Atlantic salmon, coho salmon were significantly (p < .001) less likely to have higher levels of lice abundance (odds ratio 0.0029, 95% CI 0.0014-0.0062). Lice abundance was significantly (p = .001) lower in the most southern farming region of Chile (odds ratio < 0.001, 95% CI 0-0.0023). The influence of fish density on abundance requires further investigation; if found to be a risk factor, it could be manipulated by industry to improve health. There has been an industry-wide reduction in the abundance of C. rogercresseyi in farmed Chilean salmonids since the implementation of the program and national levels continue to decline. Further hypotheses for investigation include the assessment of whether improved lice control has resulted in reductions of other production diseases within the industry.

Background The FAIR (Findable, Accessible, Interoperable, Reusable) principles were proposed in 2016 to set a path towards reusability of research datasets. In this systematic review, we assessed the FAIRness of datasets associated with peer-reviewed articles in veterinary epidemiology research published since 2017, specifically looking at salmonids and dairy cattle. We considered the differences in practices between molecular epidemiology, the branch of epidemiology using genetic sequences of pathogens and hosts to describe disease patterns, and non-molecular epidemiology. Results A total of 152 articles were included in the assessment. Consistent with previous assessments conducted in other disciplines, our results showed that most datasets used in non-molecular epidemiological studies were not available (i.e., neither findable nor accessible). Data availability was much higher for molecular epidemiology papers, in line with a strong repository base available to scientists in this discipline. The available data objects generally scored favourably for Findable, Accessible and Reusable indicators, but Interoperability was more problematic. Conclusions None of the datasets assessed in this study met all the requirements set by the FAIR principles. Interoperability, in particular, requires specific skills in data management which may not yet be broadly available in the epidemiology community. In the discussion, we present recommendations on how veterinary research could move towards greater reusability according to FAIR principles. Overall, although many initiatives to improve data access have been started in the research community, their impact on the availability of datasets underlying published articles remains unclear to date.

West Nile virus (WNV) was first detected in Florida in July 2001, with 404 human cases reported to the Centers for Disease Control and Prevention as of February 2020. The subtropical climate of Florida is ideal for the mosquitoes that transmit WNV. We investigated the WNV seroprevalence in 3 NHP species housed outdoors at The Mannheimer Foundation in South Florida. From January to December 2016, 520 3 to 30 y old NHP were sampled at our 2 closed sites in Homestead and LaBelle: 200 rhesus macaques ( Macaca mulatta ), 212 cynomolgus macaques ( Macaca fascicularis ), and 108 hamadryas baboons ( Papio hamadryas hamadryas ). The presence of WNV IgG antibodies in these animals was determined by serum neutralization assays, which found a total seroprevalence of 14%. Seroprevalence was significantly higher in the baboons (29%) than the rhesus (11%) and cynomolgus (9%) macaques. The probability of seropositivity significantly increased with age, but sex and site did not significantly affect seroprevalence. The frequency of WNV seropositivity detected in these outdoor-housed NHP suggests that screening for WNV and other vector-borne diseases may be necessary prior to experimental use, particularly for infectious disease studies in which viremia or viral antibodies could confound results, and especially for populations housed outdoors in warm, wet climates. As no seropositive subjects demonstrated clinical signs of WNV and WNV exposure did not appear to significantly impact colony health, routine testing is likely unnecessary for most NHP colonies. However, WNV infection should still be considered as a differential diagnosis for any NHP presenting with nonspecific neurologic signs. Mosquito abatement plans and vigilant sanitation practices to further decrease mosquito and avian interaction with research NHP should also be considered.

Surveillance for acute flaccid paralysis (AFP) cases are essential for polio eradication. However, as most poliovirus infections are asymptomatic and some regions of the world are inaccessible, additional surveillance tools require development. Within England and Wales, we demonstrate how inclusion of environmental sampling (ENV) improves the sensitivity of detecting both wild and vaccine-derived polioviruses (VDPVs) when compared to current surveillance. Statistical modelling was used to estimate the spatial risk of wild and VDPV importation and circulation in England and Wales. We estimate the sensitivity of each surveillance mode to detect poliovirus and the probability of being free from poliovirus, defined as being below a pre-specified prevalence of infection. Poliovirus risk was higher within local authorities in Manchester, Birmingham, Bradford and London. The sensitivity of detecting wild poliovirus within a given month using AFP and enterovirus surveillance was estimated to be 0.096 (95% CI 0.055-0.134). Inclusion of ENV in the three highest risk local authorities and a site in London increased surveillance sensitivity to 0.192 (95% CI 0.191-0.193). The sensitivity of ENV strategies can be compared using the framework by varying sites and the frequency of sampling. The probability of being free from poliovirus slowly increased from the date of the last case in 1993. ENV within areas thought to have the highest risk improves detection of poliovirus, and has the potential to improve confidence in the polio-free status of England and Wales and detect VDPVs.

Salmonid rickettsial septicaemia (SRS) is the most important disease of farmed salmonid fish in Chile and the main driver of a high rate of antimicrobial use. This study evaluated the effectiveness of antimicrobial treatment of SRS outbreaks, using industry-generated data from 8318 cage-level production cycles stocked between 2003 and 2018. We defined SRS outbreaks by a specified level of SRS-attributed mortality over a 3-week period and calculated the mortality rate attributed to SRS and unknown causes during a follow-up period from the start of treatment until resolution of the outbreak. The post-treatment mortality rate was used as a proxy for assessing the effectiveness of the antimicrobial treatment on the magnitude of the SRS outbreak. After developing a causal diagram, we analyzed the data with generalized, mixed-effects regression models within an information-theoretic framework. For producers of Atlantic salmon, our results suggest that treatment should be provided to all infected cages on the farm, without interruption, as soon as possible after the start of the SRS outbreak. For producers of rainbow trout, our results suggest that treatment should be initiated as early as possible after the start of the SRS outbreak and with longer treatment durations if using in-feed florfenicol treatments. In the rainbow trout model, the large proportion of unexplained variance at the company and farm level indicates that lessons can be learned from the experience of other companies and farms. This study demonstrates the value of integrating aquaculture industry-generated health and management data to support applied epidemiological research.

Early detection surveillance is used for various purposes, including the early detection of non-communicable diseases (e.g. cancer screening), of unusual increases of disease frequency (e.g. influenza or pertussis outbreaks), and the first occurrence of a disease in a previously free population. This latter purpose is particularly important due to the high consequences and cost of delayed detection of a disease moving to a new population. Quantifying the sensitivity of early detection surveillance allows important aspects of the performance of different systems, approaches and authorities to be evaluated, compared and improved. While quantitative evaluation of the sensitivity of other branches of surveillance has been available for many years, development has lagged in the area of early detection, arguably one of the most important purposes of surveillance. This paper, using mostly animal health examples, develops a simple approach to quantifying the sensitivity of early detection surveillance, in terms of population coverage, temporal coverage and detection sensitivity. This approach is extended to quantify the benefits of risk-based approaches to early detection surveillance. Population-based clinical surveillance (based on either farmers and their veterinarians, or patients and their local health services) provides the best combination of sensitivity, practicality and cost-effectiveness. These systems can be significantly enhanced by removing disincentives to reporting, for instance by implementing effective strategies to improve farmer awareness and engagement with health services and addressing the challenges of well-intentioned disease notification policies that inadvertently impose barriers to reporting.

Seasonal variations in COVID-19 incidence have been suggested as a potentially important factor in the future trajectory of the pandemic. Using global line-list data on COVID-19 cases reported until 17th of March 2020 and global gridded weather data, we assessed the effects of air temperature and relative humidity on the daily incidence of confirmed COVID-19 local cases at the subnational level (first-level administrative divisions). After adjusting for surveillance capacity and time since first imported case, average temperature had a statistically significant, negative association with COVID-19 incidence for temperatures of -15°C and above. However, temperature only explained a relatively modest amount of the total variation in COVID-19 cases. The effect of relative humidity was not statistically significant. These results suggest that warmer weather may modestly reduce the rate of spread of COVID-19, but anticipation of a substantial decline in transmission due to temperature alone with onset of summer in the northern hemisphere, or in tropical regions, is not warranted by these findings.

Seasonal variation in COVID-19 incidence could impact the trajectory of the pandemic. Using global line-list data on COVID-19 cases reported until 29 th February 2020 and global gridded temperature data, and after adjusting for surveillance capacity and time since first imported case, higher average temperature was strongly associated with lower COVID-19 incidence for temperatures of 1°C and higher. However, temperature explained a relatively modest amount of the total variation in COVID-19 incidence. These preliminary findings support stringent containment efforts in Europe and elsewhere.