Newcastle disease (ND) remains one of the most significant constraints to backyard poultry production in Nigeria. This study investigated the epidemiology of ND in three northern states (Plateau, Bauchi and Kano) between 2020 and 2024, using a mixed-methods approach combining conventional and participatory epidemiology.A total of 52 villages where both components were surveyed were included in this analysis. In each of them, eight birds were sampled for serological and virological testing, and focus group discussions were held with farmers to explore their knowledge and practices regarding ND. Laboratory results revealed that ND virus was actively circulating in the study area, with vaccine-like lentogenic strains predominating over virulent velogenic strains. Twelve local disease terms were identified as possibly referring to ND. They were characterized mainly by respiratory and digestive signs, weakness, and high morbidity. The farmers use different terms depending on the predominance of the observed clinical signs and on their location. Furthermore, the disease terms were likely to be often used to refer to diseases caused by infectious agents other than ND virus. Farmers frequently attributed disease outbreaks to climatic factors and commonly used herbal remedies or over-the-counter drugs, while request for veterinary services and vaccination uptake remained low.Comparison of participatory and laboratory findings revealed both convergences and discrepancies between the two sets of findings, highlighting the importance of considering both scientific and local knowledge for effective disease surveillance and control strategies. Our findings are consistent with widespread circulation of NDV in backyard poultry in northern Nigeria, and emphasize the need for context-adapted communication about disease management and improved vaccination delivery.

Since 2015, French poultry production is threatened almost every year by a reintroduction of highly pathogenic avian influenza H5 viruses. The duck sector was the most concerned by this crisis but other sectors such as broiler, layer and turkey were also affected by outbreaks. The objective of this work was to assess the risk of highly pathogenic avian influenza H5 virus transmission from one farm to another within the French broiler and layer production network. This study used the WOAH risk assessment framework. After drawing up a scenario tree of virus transmission from one farm to another, data were collected through a literature review or through experts' elicitation. Three questionnaires were developed according to the experts' field of expertise: avian influenza, broiler and layer sectors. The experts' estimates were combined using a beta distribution weighted by their confidence level. A Monte Carlo iteration process was used to combine the different probabilities of the scenario tree and to assess the transmission risk. In the broiler sector, the highest transmission probabilities were observed if the exposed farm was an indoor broiler farm and the source a broiler farm (indoor or freerange). The high transmission probability between broiler farms integrated within the same association suggests that integration is an important risk factor. Person movement, transport of feed and manure management were the pathways with the highest transmission probabilities between two integrated indoor broiler farms with good biosecurity levels. In the layer sector, the highest transmission probabilities were observed if the source farm was a free-range farm and the exposed farm a production farm (indoor or free-range). The pathways with the highest transmission probabilities were egg transport and person movement. The sensitivity analysis showed that the exposed farm's biosecurity had a significant impact on the transmission probability. Our results provide an insight on the role of each type of farms in the virus spread within the French broiler and layer production sectors and will be useful for the implementation of control measures such as movement restriction or vaccination.

Characterising the vector role of a species is critical to understand and quantify host-vector-pathogen interactions. This role has to be assessed for each suspected vector species, but also at the population level. Indeed, different populations of the same species can exhibit biological and ecological variability that can amplify or limit their epidemiological role in transmitting pathogens to human and/or animal populations. In this work, we characterise the spatio-temporal dynamics of two major viruses of veterinary interest (bluetongue virus [BTV] and epizootic hemorrhagic disease virus [EHDV]) in the five species of Culicoides (Diptera: Ceratopogonidae) present in Reunion Island, located in the Indian Ocean. Our aim is to quantify the infection rates of the two viruses in field-collected Culicoides over a 2-year period. A total of 33,358 individuals comprising 11,504 pools were molecularly screened to detect the presence of both viruses. Our work applied an original statistical approach based on the use of Bayesian inference and showed that all five Culicoides species could be involved in the transmission of the two viruses with different levels of infection. EHDV circulated within Culicoides populations for only 4 months over the study period, while BTV circulated within the same populations throughout the entire 2-year period. We hypothesized that although both viruses are transmitted by the same Culicoides species, they exhibit distinct epidemiological patterns: BTV displays enzootic circulation in Reunion Island, whereas EHDV shows an epizootic pattern.

Highly pathogenic avian influenza (HPAI) H5 viruses are a major threat to the French poultry sector. All sectors are affected and virus transmission between sectors has been reported. The objective of this work was to identify the risk of virus transmission to the broiler and layer sectors from any other poultry sector in France through nine indirect transmission pathways. We used scenario tree modelling of data collected from the literature or expert knowledge. Three questionnaires were produced, one for each field of expertise: avian influenza, the broiler and layer sectors. The experts' estimates were combined using a beta distribution weighted by their confidence level. Monte Carlo iteration was used to combine the different probabilities of the scenario tree and to assess the risk of transmission. In the broiler sector, the probability of viral transmission was the highest when indoor broiler farms were exposed farms and duck farms were source farms and high when turkey and guinea fowl farms were source farms. In the layer sector, probabilities of transmission were similar between all types of farms as source farms and layer farms as exposed farms. Probabilities were very low between breeder farms but similar in all the sectors. This work highlights the role of some pathways, especially management of manure and dead birds in the probability of introduction into broiler or layer farms. These results can be used to improve management of the risk of HPAI H5 transfer between different poultry sectors in France.

Since 2015, French poultry production is threatened almost every year by a reintroduction of highly pathogenic avian influenza H5 viruses. The duck sector was the most concerned by this crisis but other sectors such as broiler, layer and turkey were also affected by outbreaks. The objective of this work was to assess the risk of highly pathogenic avian influenza H5 virus transmission from one farm to another within the French broiler and layer production network. This study used the WOAH risk assessment framework. After drawing up a scenario tree of virus transmission from one farm to another, data were collected through a literature review or through experts' elicitation. Three questionnaires were developed according to the experts' field of expertise: avian influenza, broiler and layer sectors. The experts' estimates were combined using a beta distribution weighted by their confidence level. A Monte Carlo iteration process was used to combine the different probabilities of the scenario tree and to assess the transmission risk. In the broiler sector, the highest transmission probabilities were observed if the exposed farm was an indoor broiler farm and the source a broiler farm (indoor or free-range). The high transmission probability between broiler farm integrated within the same company highlighted the role of integration in this probability. In the layer sector, the highest transmission probabilities were observed if the source farm was a free-range farm and the exposed farm a production farm (indoor or free-range). The sensitivity analysis showed that the exposed farm's biosecurity had a significant impact on the transmission probability. Our results provide an insight on the role of each type of farms in the virus spread within the French broiler and layer production network and will be useful for the implementation of control measures such as movement restriction or vaccination. ### Competing Interest Statement The authors have declared no competing interest.

The pig sector in Corsica is based by a wide range of farming systems, mainly characterized on traditional extensive practices, which favor contacts between domestic and wild individuals. These contacts are suspected to influence the maintenance and the transmission of shared infectious diseases between both populations. Therefore, it is important to develop methods that allow to understand and anticipate their occurrence. Modeling these interactions requires accurate data on the presence, location and use of land on pig farms and farming practices, but such data are often unavailable, incomplete or outdated. In this study, we suggest a method to collect and analyze pig farming information that combines approaches from social sciences and epidemiology and enables a spatial representation of an index of potential interaction (IPI) between wild and domestic pigs at municipality level in the Corsican territory. As a first step of the process, interviews were conducted to gather information from 103 pig farms. Then, using hierarchical clustering, we identified five different clusters of pig farming practices which were evaluated and validated by local experts using participatory tools. The five pig farming clusters with their respective estimated levels of direct and indirect interactions with wild boars were combined in a linear equation with pig density to estimate a hypothetical index of potential interaction (IPI) in 155 municipalities. Our results revealed the diversity of pig farming practices across the island of Corsica and pointed out potential hotspots of interaction. Our method proved to be an effective way to collect and update information on the presence and typology of pig farms which has the potential to update official livestock production statistics. The spatial representation of an IPI between wild boars and domestic pigs in the Corsican territory could help design regional disease management strategies and policies to improve the control of certain shared pig pathogens in pig farms from Corsica.

Livestock mobility, particularly that of small and large ruminants, is one of the main pillars of production and trade in West Africa: livestock is moved around in search of better grazing or sold in markets for domestic consumption and for festival-related activities. These movements cover several thousand kilometers and have the capability of connecting the whole West African region, thus facilitating the diffusion of many animal and zoonotic diseases. Several factors shape mobility patterns even in normal years and surveillance systems need to account for such changes. In this paper, we present an approach based on temporal network theory to identify possible sentinel locations, i.e., locations where pathogens circulation can be detected in the early phase of the epidemic (before the peak), using two indicators: vulnerability (i.e., the probability of being reached by the disease) and time of infection (i.e., the time of first arrival of the disease). Using these indicators in our structural analysis of the changing network enabled us to identify a set of nodes that could be used in an early warning system. As a case study, we simulated the introduction of transboundary animal diseases in Senegal and used data taken from 2020 Sanitary certificates (laissez-passer sanitaire (LPS)) issued by the Senegalese Veterinary Services to reconstruct the national mobility network. Our analysis showed that a static approach can significantly overestimate the speed and the extent of disease propagation, whereas temporal analysis revealed that the reachability and vulnerability of the different administrative departments (used as nodes of the mobility network) change over the course of the year. For this reason, several sets of sentinel nodes were identified in different periods of the year, underlining the role of temporality in shaping patterns of disease diffusion.

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.

Over the last decade African swine fever virus, one of the most virulent pathogens known to affect pigs, has devastated pork industries and wild pig populations throughout the world. Despite a growing literature on specific aspects of African swine fever transmission dynamics, it remains unclear which methods and approaches are most effective for controlling the disease during a crisis. As a consequence, an international modelling challenge was organized in which teams analyzed and responded to a stream of data from an in silico outbreak in the fictive country of Merry Island. In response to this outbreak, we developed a modelling approach that aimed to predict the evolution of the epidemic and evaluate the impact of potential control measures. Two independent models were developed: a stochastic mechanistic space-time compartmental model for characterizing the dissemination of the virus among wild boar; and a deterministic probabilistic risk model for quantifying infection probabilities in domestic pig herds. The combined results of these two models provided valuable information for anticipating the main risks of dissemination and maintenance of the virus (speed and direction of African swine fever spread among wild boar populations, pig herds at greatest risk of infection, the size of the epidemic in the short and long terms), for evaluating the impact of different control measures and for providing specific recommendations concerning control interventions.

We developed a correlative model at high resolution for predicting the distribution of one of the main vectors of Crimean-Congo haemorrhagic fever virus (CCHFV), Hyalomma marginatum, in a recently colonised area, namely southern France. About 931 H. marginatum adult ticks were sampled on horses from 2016 to 2019 and 2021 in 14 southern French departments, which resulted in the first H. marginatum detection map on a large portion of the national territory. Such updated presence/absence data, as well as the mean number of H. marginatum per examined animal (mean parasitic load) as a proxy of tick abundance, were correlated to multiple parameters describing the climate and habitats characterising each collection site, as well as movements of horses as possible factors influencing tick exposure. In southern France, H. marginatum was likely detected in areas characterised by year-long warm temperatures and low precipitation, especially in summer and mostly concentrated in autumn, as well as moderate annual humidity, compared to other sampled areas. It confirms that even in newly invaded areas this tick remains exclusively Mediterranean and cannot expand outside this climatic range. Regarding the environment, a predominance of open natural habitats, such as sclerophyllous vegetated and sparsely vegetated areas, were also identified as a favourable factor, in opposition to urban or peri-urban and humid habitats, such as continuous urban areas and inland marshes, respectively, which were revealed to be unsuitable. Based on this model, we predicted the areas currently suitable for the establishment of the tick H. marginatum in the South of France, with relatively good accuracy using internal (AUC = 0.66) and external validation methods (AUC = 0.76 and 0.83). Concerning tick abundance, some correlative relationships were similar to the occurrence model, as well as the type of horse movements being highlighted as an important factor explaining mean parasitic load. However, the limitations of estimating and modelling H. marginatum abundance in a correlative model are discussed.

Interactions between neighbouring plants in an ecosystem can lead to competition, even in single-species stands. Genetic selection of perennial plants based on the individual values of genotypes does not usually take into account interactions that develop over time. The purpose of this study was to ascertain whether the effects of competition might affect the performance of cacao genotypes tested over long periods, and at what point those effects begin. Competition was studied on cacao trees (Theobroma cacao L) taking into account the diameter of the trees and their yields. The trial design set up in Co circle te d'Ivoire was a factorial mating design of the main cacao genetic improvement programme. The approach taken was a multivariate model based on 13 years of data gathering, including genetic, spatial and competition effects. The results revealed a gradual onset of competition starting in the early years of production up to the 4th year, when its effect became significant. It first affected growth then, 2 years later, yields. Depending on the production years, the genetic effect and the spatial effect were the greatest. In years of strong competition, it could affect up to 10 % of the annual production variability, i.e. a quarter of the variability explained by genetics. The most vigorous trees always remained highly competitive and high-yielding. The competition effect will therefore always be substantial with selections of high-yielding individuals. "Group selection" of somewhat average, less competitive individuals would help to maximize yield gains through the combined performance of the group, rather than that of individual trees.

The World Organisation for Animal Health advocates the zoning approach for the surveillance and monitoring of foot and mouth disease (FMD), a highly contagious animal disease. Our purpose is to implement the zoning approach in Tunisia by identifying existing natural and artificial barriers to the movement of live animals. A Geographic Information System (GIS)-based MultiCriteria Evaluation approach was developed. Eight national and international experts were asked to identify the barriers and prioritize them, characterized by a percentage weight between 0 and 100. These barriers were mapped and combined, taking into account their relative importance, to create a friction map that makes it possible to visualize areas where animal movements are restricted. Uncertainty analysis was performed to assess the robustness of the model. The results showed that the selected barriers were in order of decreasing importance: maritime borders with a weight of 33.5%, rivers (13.8%), slopes equal to or greater than 10% (13.8%), wetlands (13.3%), forests (7.7%), land borders (7.7%), railway networks (5%) and main roads (4.9%). The Cap Bon zone is the only favourable zoning area for the control of FMD in Tunisia. A regional approach resulting from this work could be a major asset in identifying regions suitable for zoning in North Africa.

This article presents a participative and iterative qualitative risk assessment framework that can be used to evaluate the spatial variation of the risk of infectious animal disease introduction and spread on a national scale. The framework was developed through regional training action workshops and field activities. The active involvement of national animal health services enabled the identification, collection and hierarchization of risk factors. Quantitative data were collected in the field, and expert knowledge was integrated to adjust the available data at regional level. Experts categorized and combined the risk factors into ordinal levels of risk per epidemiological unit to ease implementation of risk-based surveillance in the field. The framework was used to perform a qualitative assessment of the risk of introduction and spread of foot-and-mouth disease (FMD) in Tunisia as part of a series of workshops held between 2015 and 2018. The experts in attendance combined risk factors such as epidemiological status, transboundary movements, proximity to the borders and accessibility to assess the risk of FMD outbreaks in Tunisia. Out of the 2,075 Tunisian imadas, 23 were at a very high risk of FMD introduction, mainly at the borders; and 59 were at a very high risk of FMD spread. To validate the model, the results were compared to the FMD outbreaks notified by Tunisia during the 2014 FMD epizootic. Using a spatial Poisson model, a significant alignment between the very high and high-risk categories of spread and the occurrence of FMD outbreaks was shown. The relative risk of FMD occurrence was thus 3.2 higher for imadas in the very high and high spread risk categories than for imadas in the low and negligible spread risk categories. Our results show that the qualitative risk assessment framework can be a useful decision support tool for risk-based disease surveillance and control, in particular in scarce-data environments.

This article presents a participative and iterative qualitative risk assessment framework that can be used to evaluate the spatial variation of the risk of infectious animal disease introduction and spread on a national scale. The framework was developed through regional training action workshops and field activities. The active involvement of national animal health services enabled the identification, collection and hierarchization of risk factors. Quantitative data were collected in the field, and expert knowledge was integrated to adjust the available data at regional level. Experts categorized and combined the risk factors into ordinal levels of risk per epidemiological unit to ease implementation of risk-based surveillance in the field. The framework was used to perform a qualitative assessment of the risk of introduction and spread of foot-and-mouth disease (FMD) in Tunisia as part of a series of workshops held between 2015 and 2018. The experts in attendance combined risk factors such as epidemiological status, transboundary movements, proximity to the borders and accessibility to assess the risk of FMD outbreaks in Tunisia. Out of the 2,075 Tunisian imadas, 23 were at a very high risk of FMD introduction, mainly at the borders; and 59 were at a very high risk of FMD spread. To validate the model, the results were compared to the FMD outbreaks notified by Tunisia during the 2014 FMD epizootic. Using a spatial Poisson model, a significant alignment between the very high and high-risk categories of spread and the occurrence of FMD outbreaks was shown. The relative risk of FMD occurrence was thus 3.2 higher for imadas in the very high and high spread risk categories than for imadas in the low and negligible spread risk categories. Our results show that the qualitative risk assessment framework can be a useful decision support tool for risk-based disease surveillance and control, in particular in scarce-data environments.

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

Forest trees like poplar are particular in many ways compared to other domesticated species. They have long juvenile phases, ongoing crop-wild gene flow, extensive outcrossing, and slow growth. All these particularities tend to make the conduction of breeding programs and evaluation stages costly both in time and resources. Perennials like trees are therefore good candidates for the implementation of genomic selection (GS) which is a good way to accelerate the breeding process, by unchaining selection from phenotypic evaluation without affecting precision. In this study, we tried to compare GS to pedigree-based traditional evaluation, and evaluated under which conditions genomic evaluation outperforms classical pedigree evaluation. Several conditions were evaluated as the constitution of the training population by cross-validation, the implementation of multi-trait, single trait, additive and non-additive models with different estimation methods (G-BLUP or weighted G-BLUP). Finally, the impact of the marker densification was tested through four marker density sets. The population under study corresponds to a pedigree of 24 parents and 1,011 offspring, structured into 35 full-sib families. Four evaluation batches were planted in the same location and seven traits were evaluated on 1 and 2 years old trees. The quality of prediction was reported by the accuracy, the Spearman rank correlation and prediction bias and tested with a cross-validation and an independent individual test set. Our results show that genomic evaluation performance could be comparable to the already well-optimized pedigree-based evaluation under certain conditions. Genomic evaluation appeared to be advantageous when using an independent test set and a set of less precise phenotypes. Genome-based methods showed advantages over pedigree counterparts when ranking candidates at the within-family levels, for most of the families. Our study also showed that looking at ranking criteria as Spearman rank correlation can reveal benefits to genomic selection hidden by biased predictions.

AbstractThis study aimed at determining the seroprevalence of foot‐and‐mouth disease (FMD) in domestic ruminants and at characterizing the virus strains circulating in four areas of Chad (East Batha, West Batha, Wadi Fira and West Ennedi). The study was carried out between October and November 2016. A total of 1,520 sera samples (928 cattle, 216 goats, 254 sheep and 122 dromedaries) were collected randomly for FMD serological analyses. Nine epithelial tissue samples were also collected from cattle showing clinical signs, for FMDV isolation and characterization. Serological results showed an overall NSP seroprevalence of 40% (375/928) in cattle in our sample (95% CrI [19–63]). However, seroprevalences of 84% (27/32), 78% (35/45) and 84% (21/25) were estimated in cattle over 5 years of age in East Batha, West Batha and Wadi Fira, respectively. In cattle under 1 year of age, 67% (18/27) seroprevalence was estimated in Wadi Fira, 64% (14/22) in East Batha and 59% (13/22) in West Batha. It was found that the high seroprevalences have been obtained in areas where pastures are shared by several different herds but also in farms where two to three species (bovine, caprine and ovine) are raised together. ELISA PrioCHECK®FMDV types O and A and in‐house solid phase competition ELISA serotyping results showed that the four O, A, SAT1 and SAT2 serotypes have circulated in Chad in 2016. However, the type SAT2 dominated with an overall seroprevalence of 43% (29/67) and was present in the four areas investigated. The phylogenetic analyses of the VP1 coding sequence allowed determining the serotype SAT2 topotype VII, close to viral strains found in Cameroon in 2015 with a similarity of 98.60%.