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Abstract In 2023, 14 Member States were affected by African swine fever (ASF), including Croatia and Sweden where ASF emerged (wild boar outbreaks only) and Greece where ASF re‐emerged after being free since 2021. The number of ASF outbreaks among domestic pigs in the EU was five times higher than in 2022, reaching a similar magnitude to that in 2019. This was predominantly driven by the introduction and subsequent spread of ASF in Croatia and its resurgence in Romania, representing 96% of the EU outbreaks. ASF outbreaks in domestic pigs were clearly seasonal in all countries, with 88% of outbreaks reported between July and October. Most of the ASF outbreaks among domestic pigs were detected through clinical suspicion (94%), followed by tracing from affected establishments (3%), and the weekly testing of at least two dead pigs in establishments (3%). In wild boar, a 10% increase in the number of notified outbreaks was observed in the EU in comparison with 2022, with considerable variations between countries. A winter peak was observed only in Poland, Slovakia and Hungary. The epidemiological situation in wild boar improved in Germany and Hungary, as suggested by the decrease in the number of outbreaks and in the proportions of PCR‐positive samples from dead wild boar. Overall, 31% of wild boar carcasses found during passive surveillance tested positive by PCR, representing 69% of the ASF outbreaks in wild boar in the EU. In contrast, 0.4% of hunted wild boar tested positive, representing 31% of the outbreaks. Despite the introduction of ASF into new countries and the increase in the number of outbreaks, the size of restricted zones in the EU remained stable, due to the highly clustered outbreaks in Croatia, and the reduction of restricted zones in Poland, Slovakia and Bulgaria (in domestic pigs), and Hungary (in wild boar).

Abstract The global spread of avian influenza A viruses in domestic birds is causing increasing socioeconomic devastation. Various mechanistic models have been developed to better understand avian influenza transmission and evaluate the effectiveness of control measures in mitigating the socioeconomic losses caused by these viruses. However, the results of models of avian influenza transmission and control have not yet been subject to a comprehensive review. Such a review could help inform policy makers and guide future modeling work. To help fill this gap, we conducted a systematic review of the mechanistic models that have been applied to field outbreaks. Our three objectives were to: (1) describe the type of models and their epidemiological context, (2) list estimates of commonly used parameters of low pathogenicity and highly pathogenic avian influenza transmission, and (3) review the characteristics of avian influenza transmission and the efficacy of control strategies according to the mechanistic models. We reviewed a total of 46 articles. Of these, 26 articles estimated parameters by fitting the model to data, one evaluated the effectiveness of control strategies, and 19 did both. Values of the between-individual reproduction number ranged widely: from 2.18 to 86 for highly pathogenic avian influenza viruses, and from 4.7 to 45.9 for low pathogenicity avian influenza viruses, depending on epidemiological settings, virus subtypes and host species. Other parameters, such as the durations of the latent and infectious periods, were often taken from the literature, limiting the models’ potential insights. Concerning control strategies, many models evaluated culling (n = 15), while vaccination received less attention (n = 6). According to the articles reviewed, optimal control strategies varied between virus subtypes and local conditions, and depended on the overall objective of the intervention. For instance, vaccination was optimal when the objective was to limit the overall number of culled flocks. In contrast, pre-emptive culling was preferred for reducing the size and duration of an epidemic. Early implementation consistently improved the overall efficacy of interventions, highlighting the need for effective surveillance and epidemic preparedness.

Abstract We analysed the interplay between palmiped farm density and the vulnerability of the poultry production system to highly pathogenic avian influenza (HPAI) H5N8. To do so, we used a spatially-explicit transmission model, which was calibrated to reproduce the observed spatio-temporal distribution of outbreaks in France during the 2016–2017 epidemic of HPAI. Six scenarios were investigated, in which the density of palmiped farms was decreased in the municipalities with the highest palmiped farm density. For each of the six scenarios, we first calculated the spatial distribution of the basic reproduction number (R 0), i.e. the expected number of farms a particular farm would be likely to infect, should all other farms be susceptible. We also ran in silico simulations of the adjusted model for each scenario to estimate epidemic sizes and time-varying effective reproduction numbers. We showed that reducing palmiped farm density in the densest municipalities decreased substantially the size of the areas with high R 0 values (> 1.5). In silico simulations suggested that reducing palmiped farm density, even slightly, in the densest municipalities was expected to decrease substantially the number of affected poultry farms and therefore provide benefits to the poultry sector as a whole. However, they also suggest that it would not have been sufficient, even in combination with the intervention measures implemented during the 2016–2017 epidemic, to completely prevent the virus from spreading. Therefore, the effectiveness of alternative structural preventive approaches now needs to be assessed, including flock size reduction and targeted vaccination.

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.

ABSTRACT The latest outbreaks of high pathogenicity avian influenza viruses (HPAIVs) were the most widespread ever seen in Europe, entailing considerable economic costs and raising public health concerns. Virological surveillance protocols involve reverse transcription quantitative polymerase chain reactions (RT-qPCR) from tracheal and cloacal swabs, which are laborious to perform on a large scale and require special skills. Environmental sampling, and especially dust collection, may represent a relevant alternative, as it is cheap, non-invasive for animals, simpler, and quicker to carry out. The main drawback of dust samples is that they may contain high amounts of organic substances that can inhibit RT-qPCR reactions. Bovine serum albumin (BSA) is a molecule known to facilitate DNA polymerization in the presence of numerous inhibitors, including those from feces, litter, or food. We tested its use on dust samples collected on 107 farms localized in areas affected by epizootics of clade 2.3.4.4b HPAIV H5N1. We used a latent class modeling approach to compare the performance of three detection protocols: (i) BSA addition to the RT-qPCR reaction mix, (ii) dilution of template RNA, and (iii) a control protocol. Our results indicate that BSA addition to the RT-qPCR reaction mix improved the sensitivity of the method, by neutralizing inhibitors’ effect. Indeed, for hemagglutinin (HA) or matrix (M) RNA detection, the sensitivity of the BSA protocol was the highest, followed by that of the control protocol, and that of the dilution protocol. Our results suggest that the use of BSA could be routinely implemented in HPAIV dust monitoring RT-qPCR protocols. IMPORTANCE With the circulation of high pathogenicity avian influenza viruses having intensified considerably in recent years, the European Union is considering the vaccination of farmed birds. A prerequisite for this vaccination is the implementation of drastic surveillance protocols. Environmental sampling is a relevant alternative to animal sampling. However, environmental samples often contain inhibitory compounds in large enough quantities to inhibit RT-qPCR reactions. As bovine serum albumin is a molecule used in many fields to overcome this inhibitory effect, we tested its use on dust samples from poultry farms in areas heavily affected by HPAIV epizootics. Our results show that its use significantly increases the sensitivity of the method.

Modeling of infectious diseases at the livestock-wildlife interface is a unique subset of mathematical modeling with many innate challenges. To ascertain the characteristics of the models used in these scenarios, a scoping review of the scientific literature was conducted. Fifty-six studies qualified for inclusion. Only 14 diseases at this interface have benefited from the utility of mathematical modeling, despite a far greater number of shared diseases. The most represented species combinations were cattle and badgers (for bovine tuberculosis, 14), and pigs and wild boar [for African (8) and classical (3) swine fever, and foot-and-mouth and disease (1)]. Assessing control strategies was the overwhelming primary research objective (27), with most studies examining control strategies applied to wildlife hosts and the effect on domestic hosts (10) or both wild and domestic hosts (5). In spatially-explicit models, while livestock species can often be represented through explicit and identifiable location data (such as farm, herd, or pasture locations), wildlife locations are often inferred using habitat suitability as a proxy. Though there are innate assumptions that may not be fully accurate when using habitat suitability to represent wildlife presence, especially for wildlife the parsimony principle plays a large role in modeling diseases at this interface, where parameters are difficult to document or require a high level of data for inference. Explaining observed transmission dynamics was another common model objective, though the relative contribution of involved species to epizootic propagation was only ascertained in a few models. More direct evidence of disease spill-over, as can be obtained through genomic approaches based on pathogen sequences, could be a useful complement to further inform such modeling. As computational and programmatic capabilities advance, the resolution of the models and data used in these models will likely be able to increase as well, with a potential goal being the linking of modern complex ecological models with the depth of dynamics responsible for pathogen transmission. Controlling diseases at this interface is a critical step toward improving both livestock and wildlife health, and mechanistic models are becoming increasingly used to explore the strategies needed to confront these diseases.

Avian influenza A(H5N8) virus has caused major epizootics in Europe since 2016. We conducted virologic analysis of aerosol and dust collected on poultry farms in France during 2020–2021. Our results suggest dust contributes to viral dispersal, even early in an outbreak, and could be a valuable surveillance tool.

Abstract This report presents the epidemiological analysis of African swine fever (ASF) during 2022 based on the surveillance and pig population data submitted by the European Union (EU) affected countries and one neighbouring country. Coinciding with regulatory changes and an important decrease in ASF outbreaks in 2022 in the EU, the number of domestic pig samples tested as part of active surveillance decreased by 80%, while the number of samples from passive surveillance almost doubled compared with 2021. Most outbreaks among domestic pigs in the EU were detected by testing clinical suspicions (93% of outbreaks), followed by tracing activities (5%) and weekly testing of the first two dead pigs per establishment (2%). Although most of the wild boar samples came from hunted animals, the probability of detecting PCR‐positive animals was much higher in wild boar found dead. The ASF outbreaks among domestic pigs in the EU decreased by 79% while a decrease of 40% in the wild boar cases was observed in comparison with 2021. This was strongly marked in Romania, Poland and Bulgaria, with a reduction of 50–80% compared with 2021. In many countries, an important decrease in the number of pig establishments was observed, especially of small establishments with fewer than 100 pigs. The regional between farm incidence and proportion of pigs lost due to ASF in the EU was in general very low (average of 1%) apart from some regions in Romania. The impact of ASF on wild boar populations was variable, with a decline in wild boar abundance observed in certain countries versus a stable or even increased population after ASF introduction. This supports the negative relationship observed in this report between the proportion of the country with restricted zones due to ASF in wild boar and wild boar hunting bags.

Abstract In late 2015, an epizootic of Highly Pathogenic Avian Influenza (H5Nx) was registered in Southwestern France, including more than 70 outbreaks in commercial poultry flocks. Phylogenetic analyses suggested local emergence of H5 viruses which differed from A/goose/Guangdong/1/1996 clade 2.3.4.4b lineage and shared a unique polybasic cleavage site in their hemagglutinin protein. The present work provides an overview of the pathobiological picture associated with this epizootic in naturally infected chickens, guinea fowls and ducks. Upon necropsy examination, selected tissues were sampled for histopathology, immunohistochemistry and quantitative Real Time Polymerase Chain Reaction. In Galliformes, HPAIVs infection manifested as severe acute systemic vasculitis and parenchymal necrosis and was associated with endothelial expression of viral antigen. In ducks, lesions were mild and infrequent, with sparse antigenic detection in respiratory and digestive mucosae and leukocytes. Tissue quantifications of viral antigen and RNA were higher in chickens and guinea fowls compared to duck. Subsequently, recombinant HA (rHA) was generated from a H5 HPAIV isolated from an infected duck to investigate its glycan-binding affinity for avian mucosae. Glycan-binding analysis revealed strong affinity of rHA for 3’Sialyl-LacNAc and low affinity for Sialyl-LewisX, consistent with a duck-adapted virus similar to A/Duck/Mongolia/54/2001 (H5N2). K222R and S227R mutations on rHA sequence shifted affinity towards Sialyl-LewisX and led to an increased affinity for chicken mucosa, confirming the involvement of these two mutations in the glycan-binding specificity of the HA. Interestingly, the rHA glycan binding pattern of guinea fowl appeared intermediate between duck and chicken. The present study presents a unique pathobiological description of the H5 HPAIVs outbreaks that occurred in 2015–2016 in Southwestern France.

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.

African swine fever (ASF) is an emerging disease currently spreading at the interface between wild boar and pig farms in Europe and Asia. Current disease control regulations, which involve massive culling with significant economic and animal welfare costs, need to be improved. Modelling enables relevant control measures to be explored, but conducting the exercise during an epidemic is extremely difficult. Modelling challenges enhance modellers’ ability to timely advice policy makers, improve their readiness when facing emerging threats, and promote international collaborations. The ASF-Challenge, which ran between August 2020 and January 2021, was the first modelling challenge in animal health. In this paper, we describe the objectives and rules of the challenge. We then demonstrate the mechanistic multi-host model that was used to mimic as accurately as possible an ASF-like epidemic, provide a detailed explanation of the surveillance and intervention strategies that generated the synthetic data, and describe the different management strategies that were assessed by the competing modelling teams. We then outline the different technical steps of the challenge as well as its environment. Finally, we synthesize the lessons we learnt along the way to guide future modelling challenges in animal health.

Robust epidemiological knowledge and predictive modelling tools are needed to address challenging objectives, such as: understanding epidemic drivers; forecasting epidemics; and prioritising control measures. Often, multiple modelling approaches can be used during an epidemic to support effective decision making in a timely manner. Modelling challenges contribute to understanding the pros and cons of different approaches and to fostering technical dialogue between modellers. In this paper, we present the results of the first modelling challenge in animal health – the ASF Challenge – which focused on a synthetic epidemic of African swine fever (ASF) on an island. The modelling approaches proposed by five independent international teams were compared. We assessed their ability to predict temporal and spatial epidemic expansion at the interface between domestic pigs and wild boar, and to prioritise a limited number of alternative interventions. We also compared their qualitative and quantitative spatio-temporal predictions over the first two one-month projection phases of the challenge. Top-performing models in predicting the ASF epidemic differed according to the challenge phase, host species, and in predicting spatial or temporal dynamics. Ensemble models built using all team-predictions outperformed any individual model in at least one phase. The ASF Challenge demonstrated that accounting for the interface between livestock and wildlife is key to increasing our effectiveness in controlling emerging animal diseases, and contributed to improving the readiness of the scientific community to face future ASF epidemics. Finally, we discuss the lessons learnt from model comparison to guide decision making.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), responsible for COVID-19 in people, has been detected in companion animals on rare occasions. A limited number of large-scale studies have investigated the exposure of companion animals to SARS-CoV-2. The objective of this prospective study was to estimate seroprevalence in privately owned dogs and cats presented in veterinary clinics in different French regions and to test the hypothesis that the occurrence of an episode of COVID-19 in the household and close contact with the owner would increase the chances of the animals being seropositive. One hundred and sixty-five dogs and 143 cats were blood-sampled between March 2020 and December 2021. Neutralizing SARS-CoV-2 antibodies were detected in 8.4% of cats (12/143) and 5.4% of dogs (9/165). Seven animals (three dogs and four cats) were seropositive in the absence of an episode of COVID-19 in the household. Despite not being statistically significant (chi-square test, p-value = 0.55), our data may suggest that the occurrence of an episode of COVID-19 in the household could increase the risk of animal seropositivity (odds ratio = 1.38; 95% confidence interval = 0.55–3.77). This survey indirectly shows that SARS-CoV-2 circulates in canine and feline populations, but its circulation appears to be too low for pets to act as a significant viral reservoir.

Diagnostic tests are used to classify individual animals’ infection statuses. However, validating test performance in wild animals without gold standard tests is extremely challenging, and the issue is further complicated in chronic conditions where measured immune parameters vary over time. Here, we demonstrate the value of combining evidence from different diagnostic approaches to aid interpretation in the absence of gold standards, large sample sizes, and controlled environments. Over a two-year period, we sampled 268 free-living meerkats (Suricata suricatta) longitudinally for Mycobacterium suricattae (a causative agent of tuberculosis), using three ante-mortem diagnostic tests based on mycobacterial culture, and antigen-specific humoral and cell-mediated immune responses, interpreting results both independently and in combination. Post-mortem cultures confirmed M. suricattae infection in 22 animals, which had prior ante-mortem information, 59% (13/22) of which were test-positive on a parallel test interpretation (PTI) of the three ante-mortem diagnostic assays (95% confidence interval: 37–79%). A similar ability to detect infection, 65.7% (95% credible interval: 42.7–84.7%), was estimated using a Bayesian approach to examine PTI. Strong evidence was found for a near doubling of the hazard of death (Hazard Ratio 1.75, CI: 1.14–2.67, p = 0.01), associated with a positive PTI result, thus demonstrating that these test results are related to disease outcomes. For individual tests, small sample sizes led to wide confidence intervals, but replication of conclusions, using different methods, increased our confidence in these results. This study demonstrates that combining multiple methodologies to evaluate diagnostic tests in free-ranging wildlife populations can be a useful approach for exploiting such valuable datasets.