The antemortem diagnosis of aspergillosis in birds remains a complex challenge. A variety of diagnostic methods are currently available, including direct detection of Aspergillus components, antibody-based assays, and nonspecific markers such as serum protein electrophoresis (SPE), but their diagnostic performances remain limited. The objective of this study was to assess and compare the performances of several diagnostic approaches, specifically, galactomannan index measurement, beta-D-glucan assay, 3-hydroxybutyrate quantification, SPE, and mannoprotein detection tests. A secondary objective was to develop a predictive model of aspergillosis incorporating optimal test thresholds identified in the first phase, combined with clinical signs. A total of 101 serum and 31 plasma samples were collected from 118 aquatic bird of various species in France. Birds were classified into three categories: control individuals (n = 88), suspected aspergillosis cases (n = 7), and confirmed cases (n = 23). While individually considered tests displayed limitations in specificity, predictive modeling revealed that elevated levels of 3-hydroxybutyrate above 0.52 mmol/L (Se = 96%, Sp = 51%) combined with beta-globulins above 6.90 g/L (Se = 78%, Sp = 51%), in conjunction with concurrent respiratory clinical signs, were significantly associated with the occurrence of aspergillosis. A multivariable logistic model combining these variables achieved excellent diagnostic performance, with AUCs up to 0.98 and sensitivity above 98%. These findings suggest that these parameters, particularly when considered alongside clinical signs, may serve as more reliable indicators for detecting aspergillosis in birds.

Monitoring and improving animal health and welfare is a pressing need. Technological advances are increasing our ability to do so in a range of settings but currently, there are limited uses in experimental settings for less common model animals. Red foxes (Vulpes vulpes) are maintained by the French National Agency for Food, Environmental, and Occupational Health and Safety (ANSES) for regulatory and research purposes, primarily related to its role as a European Union and French Reference Laboratory for rabies and Echinococcus spp. In 2022, four foxes were surgically fitted with internal implants that recorded body temperature and activity levels and subsequently inoculated with rabies virus (RABV). Retrospectively, we tested the potential of these two variables to provide an early warning for the onset of rabies symptoms. We applied two anomaly detection algorithms (Shewhart and EWMA) with varying confidence levels to the data sets and compared how early the different models could detect significant changes while limiting false alarms during the calibration period. We hypothesized that body temperatures would rise significantly and foxes would significantly alter their activity levels at the beginning of infection, both at an earlier stage than is detectable through direct observation. We found that foxes significantly changed their activity during infection. We were best able to detect these changes using the EWMA algorithm, in some cases producing consecutive alarms up to two weeks before the death of an animal, while limiting false alarms. We found no evidence of fever in any of the infected foxes and body temperature did not appear to be a reliable indicator of foxes' health. While here we applied our methods to a particularly severe and rarely implemented model with a very small sample size, this proof of concept illustrates the potential of these methods for a wide range of other situations that would benefit from similar long-term monitoring, including experimental protocols with milder clinical signs and routine monitoring for unexpected declines in health or welfare. ### Competing Interest Statement The authors have declared no competing interest. VetBionet French Agency for Food, Environmental and Occupational Health and Safety (ANSES), AMI 2024 (internal grant)

This study presents a Bayesian Hidden Markov Model (HMM) that integrates continuous test results with temporal disease dynamics to improve surveillance of infectious diseases using pooled samples such as bulk tank milk (BTM). The model extends a previous HMM that relied on dichotomised results by modelling test data as mixtures of normal distributions, thereby retaining more information and improving parameter estimation. Simulations showed that the continuous HMM consistently outperformed a discrete version, with the greatest advantage in scenarios of higher infection incidence and more frequent state changes, where temporal correlation is weaker. Model performance remained robust for the estimation of dynamic parameters and diagnostic sensitivity and specificity. Applied to 2014-2020 data from the bovine viral diarrhoea virus (BVDV) surveillance programme in Brittany, France, the model estimated stable test characteristics across four départements. It confirmed the higher sensitivity of one of the two antibody ELISA tests compared the other and revealed generally low rates of transition to seropositivity and high persistence of seropositivity. Slightly higher herd-level seropositivity and distinct parameter estimates in Ille-et-Vilaine likely reflect differences in herd structure or infection dynamics. The continuous HMM provides a rigorous framework for evaluating diagnostic tests, selecting optimal thresholds, and identifying positive herds based on longitudinal data. While future improvements could include modelling covariates and addressing potential misclassification due to cross-reactions, the approach offers a robust and adaptable tool for disease surveillance and test evaluation in diverse epidemiological contexts.

Video-based livestock monitoring offers a noninvasive, cost-effective, and scalable alternative to direct human monitoring, but also to commonly used collar or ear tag devices on farms. It enables simultaneous real-time observation of multiple animals while avoiding stress and injuries from physical devices. However, single-camera systems face challenges such as blind spots and limited individual tracking, especially in barns lacking corridor layouts. These limitations can be overcome using multi-camera, multi-cow tracking (MCMCT) systems that integrate deep learning and statistical techniques to enable continuous detection, identification, activity classification, and zone location of animals in the barn, under commercial conditions. This environment is characterized by high stocking density (in m2 per cow), occlusions, and variable lighting. In this study, a commercial MCMCT system was tested over 31 d (May 2025) on 3 Holstein dairy farms in western France. Herd size ranged from 70 to 250 lactating cows and used automatic milking systems (AMS), which allowed identification of all animals when milked. Individual detection performance of this MCMCT system was then validated compared with official AMS records. A dedicated hybrid confusion matrix framework was developed to jointly assess detection and identification errors in the sequential process, allowing precise calculation of recall, precision, and F1-scores at both stages. Overall, this MCMCT system achieved over 90% detection recall and 87% to 93% precision, successfully detecting continuously more than 9 out of 10 cows daily. Identification was more challenging, with recall varying from 69% to 78% and precision above 83%, resulting in F1-scores of 79% to 82%. The performance of detection varied significantly between day and night in 2 out of 3 farms (H1 and H2), with recall rates dropping to 76% at night and exceeding 94% during peak daylight, underscoring the impact of lighting and activity patterns. Activity classification and zone location were robust, with F1-scores exceeding 87%, demonstrating the system's capacity to provide practical insights for herd management such as monitoring individual behaviors, identifying high-density zones around resources, and supporting daily management decisions. This work confirms the system's practical viability as a scalable, noninvasive monitoring solution effective under commercial farm complexities such as crowding, occlusion, and lighting variability. The integration of day-night performance analysis and the hybrid confusion matrix provide a rigorous and transparent framework for assessing system reliability, critical for deploying precision livestock farming technologies. Identification performance decreased under overcrowded conditions. Overcrowding is defined here as a surface area of less than 9 m2 per cow or less than one cubicle per cow, as recommended by the EFSA Panel on Animal Health and Animal Welfare in 2023. The system demonstrates significant potential to support and enhance herd management, early disease detection, and animal welfare monitoring.

Les relations entre l’élevage des animaux producteurs de denrées et les attentes de la société sont aujourd’hui complexes. Quelles que soient les directions prises pour faire évoluer les systèmes d’élevage, les éleveurs et les acteurs des filières doivent pouvoir vivre de leur métier dans un environnement stable ou, a minima, prévisible. Des affections multifactorielles dont les causes mêlent agents pathogènes, évolutions climatiques, systèmes et conduite d’élevage ont de nombreux impacts sur le bien-être des animaux et des éleveurs, les coûts de production et l’environnement. L’exemple particulier de l’élevage des vaches laitières et la problématique des maladies (notamment les mammites…) liées aux changements des méthodes d’élevage qui ont accompagné l’augmentation importante de la production laitière depuis les années 1950 illustrera toutes ces dimensions.

Le bien-être des animaux est une notion difficile à définir car se référant à un phénomène complexe, intrinsèquement liée à la perception qu’a l’individu de son environnement. Ne pouvant être mesuré directement, le bien-être est évalué à partir de la détermination et la quantification d’indicateurs spécifiques. Ces indicateurs, dont les variations sont associées à différents états de bien-être, doivent être combinés en fonction du contexte d’évaluation. Le comportement animal, reconnu comme une des clés pour l’évaluation du bien-être, peut changer face aux variations de l’environnement d’élevage, telles que l’accès au pâturage, influençant à la fois la routine et la dynamique de l’occupation de l’espace des animaux. L'analyse de ces changements comportementaux permet de définir de nouveaux indicateurs, facilitant l’évaluation de l’impact positif ou négatif de ces modifications environnementales sur le bien-être des animaux. L’intégration des technologies de capteurs, de modèles mathématiques et de l’intelligence artificielle ouvre de nouvelles perspectives pour un suivi longitudinal des activités, des dynamiques spatiales et d’autres paramètres d’intérêt tout au long du cycle de vie des animaux. Par exemple, les algorithmes de classification supervisée ont permis d’associer les données brutes fournies par des capteurs aux comportements d’intérêt, tandis que les algorithmes non supervisés devraient révéler de nouveaux indicateurs en lien avec le bien-être des animaux. Cet article met en lumière les opportunités offertes par les technologies numériques émergentes. Nous nous concentrons sur l’évaluation comportementale et son rôle crucial dans l’évaluation du bien-être, en présentant trois études de cas : 1) pour distinguer les problèmes liés à la santé, au stress thermique et à la reproduction chez les vaches laitières, 2) pour prévoir la boiterie chez la vache laitière et 3) pour étudier des émotions chez les porcs. Enfin, nous soulignons l’importance d’une collaboration interdisciplinaire étroite entre éthologistes, physiologistes, mathématicien(ne)s et informaticien(ne)s pour favoriser le développement de ce domaine émergent que nous désignons sous le terme d’« éthologie numérique ».

The welfare of farm animals is a complex concept that is intrinsically linked to the animal's perception of its environment. Although welfare cannot be measured directly, it can be assessed by identifying and quantifying specific indicators according to the context of the assessment. Animal beha- viour, widely recognized as a key welfare indicator, responds dynamically to changes in the rearing environment, such as access to pasture, which affect both the routine and spatial dynamics of the animals. The analysis of these behavioural changes allows the identification of new indicators and the negative or positive impact of these environmental changes on animal welfare. The integration of sensor technologies, mathematical models and artificial intelligence opens new avenues for longitudinal monitoring of activities, spatial dynamics and other parameters of interest throughout an animal's life cycle. For example, supervised classification algorithms have enabled the association of raw sensor data with specific behaviours, while unsupervised algorithms are expected to reveal novel indicators. This article explores the potential opportunities offered by digital technologies. We highlight the role of behavioural assessment in welfare assessment, illustrated by three case studies : (1) discriminating pathological, reproductive or stress conditions in cows, (2) lameness prediction in dairy cows, and (3) the study of emotions in pigs. Finally, we highlight the importance of close interdisciplinary collaboration between ethologists, physiologists, mathematicians and computer scientists to advance this emerging field, which we term'digital ethology.'

A wide variety of control and surveillance programmes that are designed and implemented based on country-specific conditions exists for infectious cattle diseases that are not regulated. This heterogeneity renders difficult the comparison of probabilities of freedom from infection estimated from collected surveillance data. The objectives of this review were to outline the methodological and epidemiological considerations for the estimation of probabilities of freedom from infection from surveillance information and review state-of-the-art methods estimating the probabilities of freedom from infection from heterogeneous surveillance data. Substantiating freedom from infection consists in quantifying the evidence of absence from the absence of evidence. The quantification usually consists in estimating the probability of observing no positive test result, in a given sample, assuming that the infection is present at a chosen (low) prevalence, called the design prevalence. The usual surveillance outputs are the sensitivity of surveillance and the probability of freedom from infection. A variety of factors influencing the choice of a method are presented; disease prevalence context, performance of the tests used, risk factors of infection, structure of the surveillance programme and frequency of testing. The existing methods for estimating the probability of freedom from infection are scenario trees, Bayesian belief networks, simulation methods, Bayesian prevalence estimation methods and the STOC free model. Scenario trees analysis is the current reference method for proving freedom from infection and is widely used in countries that claim freedom. Bayesian belief networks and simulation methods are considered extensions of scenario trees. They can be applied to more complex surveillance schemes and represent complex infection dynamics. Bayesian prevalence estimation methods and the STOC free model allow freedom from infection estimation at the herd-level from longitudinal surveillance data, considering risk factor information and the structure of the population. Comparison of surveillance outputs from heterogeneous surveillance programmes for estimating the probability of freedom from infection is a difficult task. This paper is a ‘guide towards substantiating freedom from infection’ that describes both all assumptions-limitations and available methods that can be applied in different settings.

Background Scenario tree modelling is a well-known method used to evaluate the confidence of freedom from infection or to assess the sensitivity of a surveillance system in detecting an infection at a certain design prevalence. It facilitates the use of data from various sources and the inclusion of risk factors into calculations, while still obtaining quantitative estimates of surveillance sensitivity and probability of freedom. Objectives We conducted a scoping review to identify scenario tree models (STMs) applied to assess freedom from infection in veterinary medicine, characterize their use, parameterisation, reporting and potential limitations.Eligibility criteria:We included published scientific articles and grey literature that were a) neither reviews nor expert opinions, b) aimed to assess freedom from infection, provided methods to assess it, or aimed to estimate the sensitivity of a surveillance program for early detection of an infection at a design prevalence, c) targeted infection in animals and d) used scenario tree modelling. The search covered documents published between January 2006 and August 2021. Design Several search methods were used to retrieve scientific articles and grey literature relevant to the subject. The search strategy included searching in scientific databases and/or grey literature repositories, contacting experts across the world that previously worked with STMs and retrieving citations from relevant reviews. Results and discussion Four hundred twenty-four distinct documents were retrieved with our search string. After screening, data was extracted from 99 documents representing 67 projects. Forty different animal diseases were modelled with STMs, the most represented being infections with tuberculous Mycobacterium sp., Avian Influenza A virus and Brucella sp. STMs were mostly used for diseases of cattle, swine and wild mammals. Results showed that STMs were used in a large variety of studies, are very versatile and were used in disparate frameworks. However, we also found that studies are not reported in a standardized way and often lack important information. This makes results hard to interpret, compare and reproduce. Additionally, we identified common assumptions and misconceptions, the most important ones regarding sensitivity and specificity, which could have an impact on the results of the studies using STMs. Conclusion We recommend the elaboration of internationally agreed guidelines about how to report results from STMs in a uniform manner. Such guidelines should include information on the study setting, procedures and analyses, but also on how the results could be interpreted concerning freedom from infection.

There is growing concern about climate change and its impact on human health. Specifically, global warming could increase the probability of emerging infectious diseases, notably because of changes in the geographical and seasonal distributions of disease vectors such as mosquitoes and ticks. For example, the range of Ixodes ricinus, the most common and widespread tick species in Europe, is currently expanding northward and at higher altitudes. However, little is known about the seasonal variation in tick abundance in different climates. Seasonality of I. ricinus is often based on expert opinions while field surveys are usually limited in time. Our objective was to describe seasonal variations in I. ricinus abundance under different climates. To this end, a seven-year longitudinal study, with monthly collections of I. ricinus host-seeking nymphs, was carried out in France, in six locations corresponding to different climates. Tick data were log-transformed and grouped between years so as to obtain seasonal variations for a typical year. Daily average temperature was measured during the study period. Seasonal patterns of nymph abundance were established for the six different locations using linear harmonic regression. Model parameters were estimated separately for each location. Seasonal patterns appeared different depending on the climate considered. Western temperate sites showed an early spring peak, a summer minimum and a moderate autumn and winter abundance. More continental sites showed a later peak in spring, and a minimum in winter. The peak occurred in summer for the mountainous site, with an absence of ticks in winter. In all cases except the mountainous site, the timing of the spring peak could be related to the sum of degree days since the beginning of the year. Winter abundance was positively correlated to the corresponding temperature. Our results highlight clear patterns in the different sites corresponding to different climates, which allow further forecast of tick seasonality under changing climate conditions.

In the Surveillance Tool for Outcome-based Comparison of FREEdom from infection (STOC free) project (https://www.stocfree.eu), a data collection tool was constructed to facilitate standardised collection of input data, and a model was developed to allow a standardised and harmonised comparison of the outputs of different control programmes (CPs) for cattle diseases. The STOC free model can be used to evaluate the probability of freedom from infection for herds in CPs and to determine whether these CPs comply with the European Union's pre-defined output-based standards. Bovine viral diarrhoea virus (BVDV) was chosen as the case disease for this project because of the diversity in CPs in the six participating countries. Detailed BVDV CP and risk factor information was collected using the data collection tool. For inclusion of the data in the STOC free model, key aspects and default values were quantified. A Bayesian hidden Markov model was deemed appropriate, and a model was developed for BVDV CPs. The model was tested and validated using real BVDV CP data from partner countries, and corresponding computer code was made publicly available. The STOC free model focuses on herd-level data, although that animal-level data can be included after aggregation to herd level. The STOC free model is applicable to diseases that are endemic, given that it needs the presence of some infection to estimate parameters and enable convergence. In countries where infection-free status has been achieved, a scenario tree model could be a better suited tool. Further work is recommended to generalise the STOC free model to other diseases.

Persistently Infected (PI) animals play a central role in the transmission of BVDV infection between cattle herds. Thus, promoting the certification of non-PI animals is a relevant approach for improving control, as it contributes to securing the trade. The objectives of this study were: i) to assess the reliability of diverse certification criteria, and ii) to identify risk factors for erroneous certification. To do so, the proportion of animals wrongly certified as non-PI on the basis of tests performed after the certification date, was calculated for each criterion. The data used were collected in herds located in Brittany, involved in either a clearance process for those that were infected, or in a surveillance process for herds that were BVDV-free. A total of 23 criteria were defined by combining the technical characteristics of the tests (individual vs. pool; single vs. repeated; direct vs. indirect tests), and some pathogenic characteristics of BVDV infection. Overall, the rates of wrongly-certified animals were low (mean: 1.3 10-4). Direct and indirect criteria had equivalent performances. Heifers from birth, and even foetuses in the last third of gestation, are certified, provided that the herd to which they belong has been free of BVDV for more than 2.5 years. The risk for wrong certification increased in the case of PIs present in the herd or its surroundings. The simplicity of the output-based approach described here, and the excellent performance of indirect criteria relying on serological monitoring of BTM, make it particularly interesting, as its use could facilitate trade between countries.

AbstractThere is growing concern about climate change and its impact on human health. Specifically, global warming could increase the probability of emerging infectious diseases, notably because of changes in the geographical and seasonal distributions of disease vectors such as mosquitoes and ticks. For example, the range ofIxodes ricinus, the most common and widespread tick species in Europe, is currently expanding northward and at higher altitudes. However, little is known about the seasonal variation in tick abundance in different climates. Seasonality ofI. ricinusis often based on expert opinions while field surveys are usually limited in time. Our objective was to describe seasonal variations inI. ricinusabundance under different climates. To this end, a seven-year longitudinal study, with monthly collections ofI. ricinushost-seeking nymphs, was carried out in France, in six locations corresponding to different climates. Tick data were log-transformed and grouped between years so as to obtain seasonal variations for a typical year. Daily average temperature was measured during the study period. Seasonal patterns of nymph abundance were established for the six different locations using linear harmonic regression. Model parameters were estimated separately for each location. Seasonal patterns appeared different depending on the climate considered. Western temperate sites showed an early spring peak, a summer minimum and a moderate autumn and winter abundance. More continental sites showed a later peak in spring, and a minimum in winter. The peak occurred in summer for the mountainous site, with an absence of ticks in winter. In all cases except the mountainous site, the timing of the spring peak could be related to the sum of degree days since the beginning of the year. Winter abundance was positively correlated to the corresponding temperature. Our results highlight clear patterns in the different sites corresponding to different climates, which allow further forecast of tick seasonality under changing climate conditions.

A recommendation of: Friggens, N. C., Adriaens, I., Boré, R., Cozzi, G., Jurquet, J., Kamphuis, C., Leiber, F., Lora, I., Sakowski, T., Statham, J. and De Haas, Y. Resilience: reference measures based on longer-term consequences are needed to unlock the potential of precision livestock farming technologies for quantifying this trait https://dx.doi.org/10.5281/zenodo.5215797

The collective control programmes (CPs) that exist for many infectious diseases of farm animals rely on the application of diagnostic testing at regular time intervals for the identification of infected animals or herds. The diversity of these CPs complicates the trade of animals between regions or countries because the definition of freedom from infection differs from one CP to another. In this paper, we describe a statistical model for the prediction of herd-level probabilities of infection from longitudinal data collected as part of CPs against infectious diseases of cattle. The model was applied to data collected as part of a CP against bovine viral diarrhoea virus (BVDV) infection in Loire-Atlantique, France. The model represents infection as a herd latent status with a monthly dynamics. This latent status determines test results through test sensitivity and test specificity. The probability of becoming status positive between consecutive months is modelled as a function of risk factors (when available) using logistic regression. Modelling is performed in a Bayesian framework, using either Stan or JAGS. Prior distributions need to be provided for the sensitivities and specificities of the different tests used, for the probability of remaining status positive between months as well as for the probability of becoming positive between months. When risk factors are available, prior distributions need to be provided for the coefficients of the logistic regression, replacing the prior for the probability of becoming positive. From these prior distributions and from the longitudinal data, the model returns posterior probability distributions for being status positive for all herds on the current month. Data from the previous months are used for parameter estimation. The impact of using different prior distributions and model implementations on parameter estimation was evaluated. The main advantage of this model is its ability to predict a probability of being status positive in a month from inputs that can vary in terms of nature of test, frequency of testing and risk factor availability/presence. The main challenge in applying the model to the BVDV CP data was in identifying prior distributions, especially for test characteristics, that corresponded to the latent status of interest, i.e. herds with at least one persistently infected (PI) animal. The model is available on Github as an R package (https://github.com/AurMad/STOCfree) and can be used to carry out output-based evaluation of disease CPs.

Ixodes ricinus ticks (Acari: Ixodidae) are the most important vector for Lyme borreliosis in Europe. As climate change might affect their distributions and activities, this study aimed to determine the effects of environmental factors, i.e., meteorological, bioclimatic, and habitat characteristics on host-seeking (questing) activity of I. ricinus nymphs, an important stage in disease transmissions, across diverse climatic types in France over 8 years. Questing activity was observed using a repeated removal sampling with a cloth-dragging technique in 11 sampling sites from 7 tick observatories from 2014 to 2021 at approximately 1-month intervals, involving 631 sampling campaigns. Three phenological patterns were observed, potentially following a climatic gradient. The mixed-effects negative binomial regression revealed that observed nymph counts were driven by different interval-average meteorological variables, including 1-month moving average temperature, previous 3-to-6-month moving average temperature, and 6-month moving average minimum relative humidity. The interaction effects indicated that the phenology in colder climates peaked differently from that of warmer climates. Also, land cover characteristics that support the highest baseline abundance were moderate forest fragmentation with transition borders with agricultural areas. Finally, our model could potentially be used to predict seasonal human-tick exposure risks in France that could contribute to mitigating Lyme borreliosis risk.

In the last decade and a half, emerging vector-borne diseases have become a substantial threat to cattle across Europe. To mitigate the impact of the emergence of new diseases, outbreaks must be detected early. However, the clinical signs associated with many diseases may be nonspecific. Furthermore, there is often a delay in the development of new diagnostic tests for novel pathogens which limits the ability to detect emerging disease in the initial stages. Syndromic Surveillance has been proposed as an additional surveillance method that could augment traditional methods by detecting aberrations in non-specific disease indicators. The aim of this study was to develop a syndromic surveillance system for Irish dairy herds based on routinely collected milk recording and meteorological data. We sought to determine whether the system would have detected the 2012 Schmallenberg virus (SBV) incursion into Ireland earlier than conventional surveillance methods. Using 7,743,138 milk recordings from 730,724 cows in 7037 herds between 2007 and 2012, linear mixed-effects models were developed to predict milk yield and alarms generated with temporally clustered deviations from predicted values. Additionally, hotspot spatial analyses were conducted at corresponding time points. Using a range of thresholds, our model generated alarms throughout September 2012, between 4 and 6 weeks prior to the first laboratory confirmation of SBV in Ireland. This system for monitoring milk yield represents both a potentially useful tool for early detection of disease, and a valuable foundation for developing similar tools using other metrics.