Analyse de la distribution d'Escherichia coli, utilisée comme un marqueur microbiologique, dans un réseau de production porcin

La présence d’Escherichia coli pathogènes en élevages porcins entraine des retards de croissance et la mortalité. La transmission des E. coli pathogènes entre les élevages et l'abattoir d’un même réseau de production n'est pas bien décrite. La détection des gènes de virulence des E. coli pathogènes pourrait permettre d’identifier un marqueur de contamination dans le réseau. L’objectif de cette étude a été d’identifier un marqueur de contamination E. coli dans un réseau de production porcine défini afin de décrire certains modes de transmission des E. coli pathogènes. Pour ce faire, une région géographique comprenant 10 fermes d’engraissement, un abattoir et un réseau de transport a été sélectionnée. Trois lots de production consécutifs par ferme ont été suivis pendant 12 mois. Des échantillons environnementaux ont été prélevés à l’intérieur et à l’extérieur des fermes (3 visites d’élevage), dans la cour de l’abattoir (2 visites lors de sorties de lot) et sur le camion de transport. La détection des gènes de virulence (eltB, estA, estB, faeG, stxA, stx2A, eae, cnf, papC, iucD, tsh, fedA) dans les échantillons a été réalisée par PCR multiplexe conventionnelle. La distribution temporelle et spatiale des gènes de virulence a permis d’identifier le marqueur de contamination ETEC/F4 défini par la détection d’au moins un gène d’entérotoxine ETEC (estB, estA et eltB) en combinaison avec le gène de l’adhésine fimbriaire (faeG). La distribution des échantillons positifs ETEC/F4 qualifie la cour de l’abattoir comme un réservoir de contamination fréquenté par les transporteurs, vecteurs de contamination entre les élevages. Ceci suggère le lien microbiologique entre l’élevage, les transporteurs et l’abattoir jouant chacun un rôle dans la dissémination des microorganismes pathogènes et potentiellement zoonotiques en production porcine.

Dynamic Programming Approaches for Estimating and Applying Large-scale Discrete Choice Models

People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.