Epidemiological analysis of African swine fever in the European Union during 2023.

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).

Optimizing environmental viral surveillance: bovine serum albumin increases RT-qPCR sensitivity for high pathogenicity avian influenza H5Nx virus detection from dust samples.

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.

Improving the communication between farmers and veterinarians to enhance the acceptability of bovine tuberculosis eradication programmes.

France and Spain have been fighting against bovine tuberculosis (bTB) for years, even though new outbreaks continue to appear on both sides of the border, generating misconceptions about the disease and social distrust in the eradication programme and competent authorities. The perceived disease risk and the commitment of all interested parties are key factors for the successful implementation of control programmes, as they might influence the acceptability of recommended measures. Effective communication can contribute to increasing knowledge, trust and stakeholders' engagement, thus ensuring the acceptability of the eradication programme. This study was conducted in Catalonia (Spain) and Pyrenees-Atlantiques (France) in the frame of the INNOTUB project (https://innotub.eu/) to characterise the communication on bTB in the trans-Pyrenees region and provide recommendations to improve it. The communication on bTB was characterised by analysing 153 (Spain) and 66 (France) online freely available texts, published between 2018 and 2020, through Content Analysis and Critical Metaphor Analysis. Moreover, six farmers and four veterinarians were in-depth interviewed in each area to gather information about the communication on bTB. Interviews were made in original languages and analysed using a qualitative thematic approach. A pilot participatory intervention inspired by the Systematic Tool for Behavioural Assumption Validation and Exploration (STAVE) method was used to develop a list of proposals to improve communication and to promote the creation of territorial networks/committees on bTB prevention and control. It included three focus groups with farmers and veterinarians, a meeting with representatives of the regional veterinary services, and a final deliberative workshop. Results highlight the existence of a controversial debate on bTB and a heterogeneous understanding between stakeholders. Institutional and scientific communication mainly focus on bTB detection and control while other aspects are left in the background. On the contrary, farmers extend their communication to a greater variety of topics. The metaphorical framing strongly differed among actors, while veterinary services and researchers "fight" against bTB and "progress" toward the eradication, farmers place themselves in a framework of "sacrifice" and, particularly in Spain, they play a passive role. The proposals developed by the participants to improve the current communication on bTB included: (i) create participatory meeting spaces to share opinions and information; (ii) improve data accessibility (on epidemiological situations); (iii) develop clearer and written protocols and informative visual material; (iv) redesign the training courses (v) increase the stakeholders' participation in the design of protocols.

A systematic review of mechanistic models used to study avian influenza virus transmission and control.

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.

Mathematical modeling at the livestock-wildlife interface: scoping review of drivers of disease transmission between species.

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.

"It's a habit. They've been doing it for decades and they feel good and safe.": A qualitative study of barriers and opportunities to changing antimicrobial use in the Indonesian poultry sector.

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.

Impact of palmiped farm density on the resilience of the poultry sector to highly pathogenic avian influenza H5N8 in France.

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 (R0), 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 R0 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.

Validation of an RNAscope assay for the detection of avian influenza A virus.

Highly pathogenic avian influenza (HPAI) is an acute viral disease associated with high mortality and great economic losses. Immunohistochemistry (IHC) is a common diagnostic and research tool for the demonstration of avian influenza A virus (AIAV) antigens within affected tissues, supporting etiologic diagnosis and assessing viral distribution in both naturally and experimentally infected birds. RNAscope in situ hybridization (ISH) has been used successfully for the identification of a variety of viral nucleic acids within histologic samples. We validated RNAscope ISH for the detection of AIAV in formalin-fixed, paraffin-embedded (FFPE) tissues. RNAscope ISH targeting the AIAV matrix gene and anti-IAV nucleoprotein IHC were performed on 61 FFPE tissue sections obtained from 3 AIAV-negative, 16 H5 HPAIAV, and 1 low pathogenicity AIAV naturally infected birds, including 7 species sampled between 2009 and 2022. All AIAV-negative birds were confirmed negative by both techniques. All AIAVs were detected successfully by both techniques in all selected tissues and species. Subsequently, H-score comparison was assessed through computer-assisted quantitative analysis on a tissue microarray comprised of 132 tissue cores from 9 HPAIAV-infected domestic ducks. Pearson correlation of r = 0.95 (0.94-0.97), Lin concordance coefficient of ρc = 0.91 (0.88-0.93), and Bland-Altman analysis indicated high correlation and moderate concordance between the 2 techniques. H-score values were significantly higher with RNAscope ISH compared to IHC for brain, lung, and pancreatic tissues (p ≤ 0.05). Overall, our results indicate that RNAscope ISH is a suitable and sensitive tool for in situ detection of AIAV in FFPE tissues.

Epidemiological analysis of African swine fever in the European Union during 2022.

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.

Two major epidemics of highly pathogenic avian influenza virus H5N8 and H5N1 in domestic poultry in France, 2020-2022.

The spread of highly pathogenic avian influenza (HPAI) viruses worldwide has serious consequences for animal health and a major economic impact on the poultry production sector. Since 2014, Europe has been severely hit by several HPAI epidemics, with France being the most affected country. Most recently, France was again affected by two devastating HPAI epidemics in 2020-21 and 2021-22. We conducted a descriptive analysis of the 2020-21 and 2021-22 epidemics, as a first step towards identifying the poultry sector's remaining vulnerabilities regarding HPAI viruses in France. We examined the spatio-temporal distribution of outbreaks that occurred in France in 2020-21 and 2021-22, and we assessed the outbreaks' spatial distribution in relation to the 2016-17 epidemic and to the two 'high-risk zones' recently incorporated into French legislation to strengthen HPAI prevention and control. There were 468 reported outbreaks during the 2020-21 epidemic and 1375 outbreaks during the 2021-22 epidemic. In both epidemics, the outbreaks' distribution matched extremely well that of 2016-17, and most outbreaks (80.6% and 68.4%) were located in the two high-risk zones. The southwestern high-risk zone was affected in both epidemics, while the western high-risk zone was affected for the first time in 2021-22, explaining the extremely high number of outbreaks reported. As soon as the virus reached the high-risk zones, it started to spread between farms at very high rates, with each infected farm infecting between two and three other farms at the peaks of transmission. We showed that the spatial distribution model used to create the two high-risk zones was able to predict the location of outbreaks for the 2020-21 and 2021-22 epidemics. These zones were characterized by high poultry farm densities; future efforts should, therefore, focus on reducing the density of susceptible poultry in highly dense areas.

The African swine fever modelling challenge: Model comparison and lessons learnt.

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.

The African swine fever modelling challenge: Objectives, model description and synthetic data generation.

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.

SARS-CoV-2 Infection in Companion Animals: Prospective Serological Survey and Risk Factor Analysis in France.

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.

Highly Pathogenic Avian Influenza A(H5N8) Clade 2.3.4.4b Virus in Dust Samples from Poultry Farms, France, 2021.

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.

Modelling African swine fever virus spread in pigs using time-respective network data: Scientific support for decision makers.

African swine fever (ASF) represents the main threat to swine production, with heavy economic consequences for both farmers and the food industry. The spread of the virus that causes ASF through Europe raises the issues of identifying transmission routes and assessing their relative contributions in order to provide insights to stakeholders for adapted surveillance and control measures. A simulation model was developed to assess ASF spread over the commercial swine network in France. The model was designed from raw movement data and actual farm characteristics. A metapopulation approach was used, with transmission processes at the herd level potentially leading to external spread to epidemiologically connected herds. Three transmission routes were considered: local transmission (e.g. fomites, material exchange), movement of animals from infected to susceptible sites, and transit of trucks without physical animal exchange. Surveillance was represented by prevalence and mortality detection thresholds at herd level, which triggered control measures through movement ban for detected herds and epidemiologically related herds. The time from infection to detection varied between 8 and 21 days, depending on the detection criteria, but was also dependent on the types of herds in which the infection was introduced. Movement restrictions effectively reduced the transmission between herds, but local transmission was nevertheless observed in higher proportions highlighting the need of global awareness of all actors of the swine industry to mitigate the risk of local spread. Raw movement data were directly used to build a dynamic network on a realistic timescale. This approach allows for a rapid update of input data without any pre-treatment, which could be important in terms of responsiveness, should an introduction occur.

Phylodynamic analysis of the highly pathogenic avian influenza H5N8 epidemic in France, 2016-2017.

In 2016-2017, France experienced a devastating epidemic of highly pathogenic avian influenza (HPAI) H5N8, with more than 400 outbreaks reported in poultry farms. We analyzed the spatiotemporal dynamics of the epidemic using a structured-coalescent-based phylodynamic approach that combined viral genomic data (n = 196; one viral genome per farm) and epidemiological data. In the process, we estimated viral migration rates between départements (French administrative regions) and the temporal dynamics of the effective viral population size (Ne) in each département. Viral migration rates quantify viral spread between départements and Ne is a population genetic measure of the epidemic size and, in turn, is indicative of the within-département transmission intensity. We extended the phylodynamic analysis with a generalized linear model to assess the impact of multiple factors-including large-scale preventive culling and live-duck movement bans-on viral migration rates and Ne. We showed that the large-scale culling of ducks that was initiated on 4 January 2017 significantly reduced the viral spread between départements. No relationship was found between the viral spread and duck movements between départements. The within-département transmission intensity was found to be weakly associated with the intensity of duck movements within départements. Together, these results indicated that the virus spread in short distances, either between adjacent départements or within départements. Results also suggested that the restrictions on duck transport within départements might not have stopped the viral spread completely. Overall, we demonstrated the usefulness of phylodynamics in characterizing the dynamics of a HPAI epidemic and assessing control measures. This method can be adapted to investigate other epidemics of fast-evolving livestock pathogens.