Numéro spécial : Special Issue on Modelling and Inference for Infectious diseases
Statistical methods in veterinary epidemiology
[Méthodes statistiques en épidémiologie animale]
Journal de la société française de statistique, Tome 157 (2016) no. 1, pp. 153-181.

L’objectif principal de l’épidémiologie animale est de faire progresser les connaissances à la fois sur i) la dynamique et l’impact des maladies sur les productions, la santé et le bien-être des animaux ii) les risques pour la santé humaine associés aux maladies animales transmissibles à l’homme (zoonoses) et aux toxi-infections alimentaires. Parce que les animaux et les produits animaux sont une source de revenus majeure pour une partie de la population, une place plus importante est accordée aux aspects économiques en épidémiologie animale qu’en épidémiologie humaine. Pour un trouble de santé, des questions de recherche classiques auront trait à l’identification de facteurs de risque de survenue du trouble, l’estimation de son impact sur la production, l’évaluation de l’efficacité d’un traitement ou encore la détection précoce de sa survenue. A ces fins, un large éventail de méthodes statistiques est utilisé et de nombreuses sources de données existent. Une particularité des productions animales consiste en la collecte de grands volumes de données en lien avec la production tels que des productions laitiè res quotidiennes par vache, des poids vifs ou des données de reproduction. Ces grands volumes de données disponibles représentent à la fois un avantage et une difficulté pour la modélisation statistique. Pour illustrer les sources de données et les méthodes utilisables en épidémiologie animale, nous présentons des travaux effectués suite à l’émergence de la fiè vre catarrhale ovine en 2006. Dans un premier temps, nous montrons comment les impacts de la maladie sur la production laitiè re et la reproduction ont été estimés. Puis, dans l’objectif d’améliorer la précocité de la détection de telles émergence, l’application des méthodes de surveillance syndromique est présentée. Enfin des besoins de connaissances et des perspectives pour de futures recherches sont présentés.

The main aim of veterinary epidemiology is to increase knowledge on both i) the dynamics and impact of diseases on animal production, health and welfare and on ii) the risk of animal-acquired (i.e. zoonoses) and food-borne diseases in humans. Because animals and animal products are the source of income for a large sector of society, the emphasis on economic aspects is much more important in veterinary epidemiology than in human epidemiology. Typical research questions deal with the identification of risk factors for a disease, the estimation of the impact of a disease on production, the evaluation of the efficacy of a treatment or the timely identification of disease occurrence. For these purposes, a wide range of statistical techniques are used and several types of data sources exist. One peculiarity in animal productions is the collection of large production related data such as daily milk productions, live weights or reproduction data for example. These extremely large databases are both an asset and a challenge for statistical modelling. To illustrate the data sources and statistical methods used in veterinary epidemiology, we present part of the work conducted following the emergence of the bluetongue virus in cattle in 2006. First, we show how the impacts of the disease on milk production and reproduction were estimated. Then, in order to improve the timeliness of detection of such emergences, the application of syndromic surveillance methods to the bluetongue emergence is presented. Finally, some knowledge gaps and directions for future work are presented.

Keywords: animal, disease, data, model
Mot clés : animal, maladie, données, modèle
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Madouasse, Aurélien; Nusinovici, Simon; Monestiez, Pascal; Ezanno, Pauline; Lehébel, Anne. Statistical methods in veterinary epidemiology. Journal de la société française de statistique, Tome 157 (2016) no. 1, pp. 153-181. http://www.numdam.org/item/JSFS_2016__157_1_153_0/

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