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International audience; In the current world context, with a globalized production system and highly interconnected farms, understanding and controlling livestock diseases and their spreading are critical issues both for food industry and for public decision-makers. In order to prevent outbreaks, it appears essential to characterize and predict animal trade movements, as major pathogen-inducing pathways between farms. A convenient way to grasp and explore the general structure of this complex system is to use a dynamic graph representation. Indeed, the vertices of this network are the farms (and also the commercial operators such as markets and assembly centers), while the edges represent animal exchanges between farms. This graph is directed (sellers send cattle to buyers), weighted (each edge is labelled with the number of exchanged animals), and time-varying (a transaction occurs at a given time-instant). Furthermore, additional information on the vertices, such as geographical situation, type of farm, ... is usually available. With this representation, mathematical models integrating the dynamics of the network, but also the epidemics spreading on the temporal network and the farmers’ behaviour with respect to trading may be developed and investigated. However, one of the limits of these models is their scalability, whereas cattle-trading networks are usually of substantial size. For instance, the data at our disposal, concerning the exchanges involving French farms from 2005 until 2009 with a daily resolution level, contains millions of edges and hundreds of thousands of nodes. In this context, it is important to build a reduced version of this network, by identifying groups of vertices with common features (in a broad sense). In this paper, we propose an exploratory study of the French cattle-trading network using selforganizing maps and, more particularly, the recently developed R-package SOMbrero, which implements both numerical and relational versions of the algorithm. The output consists in a clustering of the vertices and a nonlinear mapping of the graph, which is a reduced version of the original network. The SOM algorithm may be trained either on vector data, which may be numerical features extracted from the dynamical graph or from snapshots of it, or on relational data, which may be any kernel or dissimilarity computed on the vertices or on the edges, such as, for example, the dynamic shortest-path distance. We will illustrate both versions of the algorithm by providing some of the outputs (see Figures 1 and 2 for an example of the algorithm trained on numerical features extracted from one yearly snapshot of the network) and explaining the benefit of each of them in the process of understanding the inner mechanisms of the network

Ce rapport décrit un modèle de simulation stochastique représentant la dynamique des populations de truies et de porcs dans un troupeau naisseur engraisseur et la transmission indirecte des Salmonelles au sein de ce troupeau. Ce modèle a été initialement développé pendant la thèse de doctorat d'Amandine Lurette Il a ensuite a été repris et étandu dans le cadre du stage de Justine Guillaumont, pour intégrer les différentes conduites en bandes rencontrées en Bretagne. L'effet du mélange de bandes et du type de conduite ont été évalués à partir de ce modèle., This report describes a stochastic simulation model which represents both the pig and sow population dynamics in a farrow-to- finish herd and the indirect Salmonella transmission within this herd. This model was initially developed during Amandine Lurette's PhD thesis . It was then picked up and extended as part of Justine Guillaumont's traineeship to integrate the various batch management systems encountered in Brittany. The effect of batch mixing and management systems were assessed using this model.

PRRSv is responsible for significant worldwide production losses and its control is a major challenge for the swine industry. Vaccination, the main control measure, does not allow to eradicate the infection and only confers a partial protection to the host. This lack of efficiency is mainly due to the strong variability in PRRSv strain virulence, which induces highly variable within-host dynamics. Consequently, there is a real need to better understand the interactions between the virus and the immune response in order to improve PRRSv control. To tackle this issue, a dynamic and deterministic modelling approach was chosen. We developed an original immunological model consisting in an integrative representation of the within-host dynamics. It describes the immune mechanisms at the between-cell scale, including the innate response, the activation and orientation of the adaptive response and their complex regulations by the major cytokines. By the definition of parameter sets based on published experimental and modelling studies, this approach allowed us to explore various scenarios in terms of host susceptibility, strain virulence, as well as dose and duration of viral exposure. Our first results show that similar infection durations associated with contrasted immune dynamics can be explained by the consideration of the immune mechanisms affected by the strain virulence. They provide new insights to explain apparent inconsistencies between experimental data. We then showed that the exposure, whose effect is often neglected, has an impact on the within-host dynamics, which varies depending on the virulence level. Finally, the within-host dynamics induced by the infection of a vaccinated pig is currently under exploration, opening up promising leads to improve vaccine efficiency. All these results provide new insights to guide further experimental and modelling approaches and they offer promising prospects to improve PRRSv control at the herd level by an immuno-epidemiological modelling approach.