A combined methodological approach to characterize pig farming and its influence on the occurrence of interactions between wild boars and domestic pigs in Corsican micro-regions

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location and use of land on pig farms and farming practices, but such data are often unavailable, incomplete or outdated.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

Symboling : utiliser des structures symboliques dotées d’une métrique

We consider manipulating symbolic data structure within usual machine learning algorithms, for instance considering a reactive system engaged in some open-ended, ill-defined problem-solving task. We define the problem-solving tasks at a geometric level, considering being located somewhere in a state-space, with the goal of reaching some final (unique or alternative, eventually partially defined) state, and finding a way from the former to the latter while satisfying the path constraints. The pivotal idea of this geometric definition is to consider an abstract state space where each point is a symbolic data structure, representing contextual information about the physical space and about the agent's internal state. Selecting a local trajectory corresponds to deciding, as a step in the problem-solving process, to modify some characteristics of the state space both at the external level (e.g., moving an object) and at the internal level (i.e., modifying the internal representation). The primary ingredient is to specify a distance. The lever is the notion of editing distance, i.e., the fact that a symbolic data value is step-by-step edited in order to equal another value. Moreover, given a data type, this specification includes the projection of a data value in the neighborhood of the data type region onto it, as developed here. This French report introduces these rather absconding notions making them as much as possible accessible, including showing their relation with computational neuroscience modeling and illustrating them using one drawing and one musical example.Nous proposons de manipuler des structures de données symboliques au sein d’algorithmes d'apprentissage automatique habituels, par exemple en considérant un système réactif engagé dans une tâche de résolution de problèmes ouverte et mal définie. Nous définissons les tâches de résolution de problèmes à un niveau géométrique, en considérant que nous sommes situés quelque part dans un espace d'états, dans le but d'atteindre un état final (unique ou alternatif, éventuellement partiellement défini), et de trouver un chemin du premier au dernier tout en respectant les contraintes du chemin.L'idée centrale de cette définition géométrique est de considérer un espace d'état abstrait où chaque point est une structure de données symboliques, représentant des informations contextuelles sur l'espace physique et sur l'état interne de l'agent. Sélectionner une trajectoire locale correspond à décider, comme étape du processus de résolution du problème, de modifier certaines caractéristiques de l'espace d'états tant au niveau externe (e.g., déplacer un objet) qu'au niveau interne (i.e., modifier la représentation interne).L'ingrédient principal est de spécifier une distance. Le levier est une notion de distance d'édition, c'est-à-dire le fait qu'une valeur de donnée symbolique est éditée pas à pas pour égaler une autre valeur. De plus, étant donné un type de données, cette spécification inclut la projection d'une valeur de données au voisinage de la région spécifiant un type de données sur celui-ci, comme développé ici.Ce rapport introduit ces notions plutôt abstraites en les rendant les plus accessibles possibles, notamment en montrant leur relation avec la modélisation en neuroscience computationnelle et en les illustrant à l'aide d'un exemple de dessins et d'un exemple musical

Symboling : utiliser des structures symboliques dotées d’une métrique

We consider manipulating symbolic data structure within usual machine learning algorithms, for instance considering a reactive system engaged in some open-ended, ill-defined problem-solving task. We define the problem-solving tasks at a geometric level, considering being located somewhere in a state-space, with the goal of reaching some final (unique or alternative, eventually partially defined) state, and finding a way from the former to the latter while satisfying the path constraints. The pivotal idea of this geometric definition is to consider an abstract state space where each point is a symbolic data structure, representing contextual information about the physical space and about the agent's internal state. Selecting a local trajectory corresponds to deciding, as a step in the problem-solving process, to modify some characteristics of the state space both at the external level (e.g., moving an object) and at the internal level (i.e., modifying the internal representation). The primary ingredient is to specify a distance. The lever is the notion of editing distance, i.e., the fact that a symbolic data value is step-by-step edited in order to equal another value. Moreover, given a data type, this specification includes the projection of a data value in the neighborhood of the data type region onto it, as developed here. This French report introduces these rather absconding notions making them as much as possible accessible, including showing their relation with computational neuroscience modeling and illustrating them using one drawing and one musical example.Nous proposons de manipuler des structures de données symboliques au sein d’algorithmes d'apprentissage automatique habituels, par exemple en considérant un système réactif engagé dans une tâche de résolution de problèmes ouverte et mal définie. Nous définissons les tâches de résolution de problèmes à un niveau géométrique, en considérant que nous sommes situés quelque part dans un espace d'états, dans le but d'atteindre un état final (unique ou alternatif, éventuellement partiellement défini), et de trouver un chemin du premier au dernier tout en respectant les contraintes du chemin.L'idée centrale de cette définition géométrique est de considérer un espace d'état abstrait où chaque point est une structure de données symboliques, représentant des informations contextuelles sur l'espace physique et sur l'état interne de l'agent. Sélectionner une trajectoire locale correspond à décider, comme étape du processus de résolution du problème, de modifier certaines caractéristiques de l'espace d'états tant au niveau externe (e.g., déplacer un objet) qu'au niveau interne (i.e., modifier la représentation interne).L'ingrédient principal est de spécifier une distance. Le levier est une notion de distance d'édition, c'est-à-dire le fait qu'une valeur de donnée symbolique est éditée pas à pas pour égaler une autre valeur. De plus, étant donné un type de données, cette spécification inclut la projection d'une valeur de données au voisinage de la région spécifiant un type de données sur celui-ci, comme développé ici.Ce rapport introduit ces notions plutôt abstraites en les rendant les plus accessibles possibles, notamment en montrant leur relation avec la modélisation en neuroscience computationnelle et en les illustrant à l'aide d'un exemple de dessins et d'un exemple musical

Symboling : utiliser des structures symboliques dotées d’une métrique

We consider manipulating symbolic data structure within usual machine learning algorithms, for instance considering a reactive system engaged in some open-ended, ill-defined problem-solving task. We define the problem-solving tasks at a geometric level, considering being located somewhere in a state-space, with the goal of reaching some final (unique or alternative, eventually partially defined) state, and finding a way from the former to the latter while satisfying the path constraints. The pivotal idea of this geometric definition is to consider an abstract state space where each point is a symbolic data structure, representing contextual information about the physical space and about the agent's internal state. Selecting a local trajectory corresponds to deciding, as a step in the problem-solving process, to modify some characteristics of the state space both at the external level (e.g., moving an object) and at the internal level (i.e., modifying the internal representation). The primary ingredient is to specify a distance. The lever is the notion of editing distance, i.e., the fact that a symbolic data value is step-by-step edited in order to equal another value. Moreover, given a data type, this specification includes the projection of a data value in the neighborhood of the data type region onto it, as developed here. This French report introduces these rather absconding notions making them as much as possible accessible, including showing their relation with computational neuroscience modeling and illustrating them using one drawing and one musical example.Nous proposons de manipuler des structures de données symboliques au sein d’algorithmes d'apprentissage automatique habituels, par exemple en considérant un système réactif engagé dans une tâche de résolution de problèmes ouverte et mal définie. Nous définissons les tâches de résolution de problèmes à un niveau géométrique, en considérant que nous sommes situés quelque part dans un espace d'états, dans le but d'atteindre un état final (unique ou alternatif, éventuellement partiellement défini), et de trouver un chemin du premier au dernier tout en respectant les contraintes du chemin.L'idée centrale de cette définition géométrique est de considérer un espace d'état abstrait où chaque point est une structure de données symboliques, représentant des informations contextuelles sur l'espace physique et sur l'état interne de l'agent. Sélectionner une trajectoire locale correspond à décider, comme étape du processus de résolution du problème, de modifier certaines caractéristiques de l'espace d'états tant au niveau externe (e.g., déplacer un objet) qu'au niveau interne (i.e., modifier la représentation interne).L'ingrédient principal est de spécifier une distance. Le levier est une notion de distance d'édition, c'est-à-dire le fait qu'une valeur de donnée symbolique est éditée pas à pas pour égaler une autre valeur. De plus, étant donné un type de données, cette spécification inclut la projection d'une valeur de données au voisinage de la région spécifiant un type de données sur celui-ci, comme développé ici.Ce rapport introduit ces notions plutôt abstraites en les rendant les plus accessibles possibles, notamment en montrant leur relation avec la modélisation en neuroscience computationnelle et en les illustrant à l'aide d'un exemple de dessins et d'un exemple musical

Assessment of Domestic Pigs, Wild Boars and Feral Hybrid Pigs as Reservoirs of Hepatitis E Virus in Corsica, France

In Corsica, extensive pig breeding systems allow frequent interactions between wild boars and domestic pigs, which are suspected to act as reservoirs of several zoonotic diseases including hepatitis E virus (HEV). In this context, 370 sera and 166 liver samples were collected from phenotypically characterized as pure or hybrid wild boars, between 2009 and 2012. In addition, serum and liver from 208 domestic pigs belonging to 30 farms were collected at the abattoir during the end of 2013. Anti-HEV antibodies were detected in 26% (21%–31.6%) of the pure wild boar, 43.5% (31%–56.7%) of hybrid wild boar and 88% (82.6%–91.9%) of the domestic pig sera. In addition, HEV RNA was detected in five wild boars, three hybrid wild boars and two domestic pig livers tested. Our findings provide evidence that both domestic pig and wild boar (pure and hybrid) act as reservoirs of HEV in Corsica, representing an important zoonotic risk for Corsican hunters and farmers but also for the large population of consumers of raw pig liver specialties produced in Corsica. In addition, hybrid wild boars seem to play an important ecological role in the dissemination of HEV between domestic pig and wild boar populations, unnoticed to date, that deserves further investigation

Possible Foodborne Transmission of Hepatitis E Virus from Domestic Pigs and Wild Boars from Corsica

International audienc

A combined methodological approach to characterize pig farming and its influence on the occurrence of interactions between wild boars and domestic pigs in Corsican micro-regions

International audienceThe 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

Describing fine spatiotemporal dynamics of rat fleas in an insular ecosystem enlightens abiotic drivers of murine typhus incidence in humans

Murine typhus is a flea-borne zoonotic disease that has been recently reported on Reunion Island, an oceanic volcanic island located in the Indian Ocean. Five years of survey implemented by the regional public health services have highlighted a strong temporal and spatial structure of the disease in humans, with cases mainly reported during the humid season and restricted to the dry southern and western portions of the island. We explored the environmental component of this zoonosis in an attempt to decipher the drivers of disease transmission. To do so, we used data from a previously published study (599 small mammals and 175 Xenopsylla fleas from 29 sampling sites) in order to model the spatial distribution of rat fleas throughout the island. In addition, we carried out a longitudinal sampling of rats and their ectoparasites over a 12 months period in six study sites (564 rats and 496 Xenopsylla fleas) in order to model the temporal dynamics of flea infestation of rats. Generalized Linear Models and Support Vector Machine classifiers were developed to model the Xenopsylla Genus Flea Index (GFI) from climatic and environmental variables. Results showed that the spatial distribution and the temporal dynamics of fleas, estimated through the GFI variations, are both strongly controlled by abiotic factors: rainfall, temperature and land cover. The models allowed linking flea abundance trends with murine typhus incidence rates. Flea infestation in rats peaked at the end of the dry season, corresponding to hot and dry conditions, before dropping sharply. This peak of maximal flea abundance preceded the annual peak of human murine typhus cases by a few weeks. Altogether, presented data raise novel questions regarding the ecology of rat fleas while developed models contribute to the design of control measures adapted to each micro region of the island with the aim of lowering the incidence of flea-borne diseases

Risk Factors of Extended-Spectrum β-Lactamase Producing Enterobacteriaceae Occurrence in Farms in Reunion, Madagascar and Mayotte Islands, 2016–2017

In South Western Indian ocean (IO), Extended-Spectrum β-Lactamase producing Enterobacteriaceae (ESBL-E) are a main public health issue. In livestock, ESBL-E burden was unknown. The aim of this study was estimating the prevalence of ESBL-E on commercial farms in Reunion, Mayotte and Madagascar and genes involved. Secondly, risk factors of ESBL-E occurrence in broiler, beef cattle and pig farms were explored. In 2016–2017, commercial farms were sampled using boot swabs and samples stored at 4 °C before microbiological analysis for phenotypical ESBL-E and gene characterization. A dichotomous questionnaire was performed. Prevalences observed in all production types and territories were high, except for beef cattle in Reunion, which differed significantly. The most common ESBL gene was blaCTX-M-1. Generalized linear models explaining ESBL-E occurrence varied between livestock production sectors and allowed identifying main protective (e.g., water quality control and detergent use for cleaning) and risk factors (e.g., recent antibiotic use, other farmers visiting the exploitation, pet presence). This study is the first to explore tools for antibiotic resistance management in IO farms. It provides interesting hypothesis to explore about antibiotic use in IO territories and ESBL-E transmission between pig, beef cattle and humans in Madagascar