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Dernière mise à jour : Mai 2018

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PhD: Control the occurrence of a multi-pathogen respiratory syndrome in young bovine: a modelling framework

Multi-pathogen respiratory syndrome in young bovine

Unit : UMR BIOEPAR, INRAE / Oniris, site de la Chantrerie, Nantes
PhD supervisers: Ezanno Pauline (HDR) ;
        Assié Sébastien ;
Period of contract: December 2021 – November 2024

Socio-economic and scientific context

Respiratory diseases are common in young livestock (cattle, pigs, poultry) and cause production losses, animal welfare problems, and antibiotic use. The prevalence of these diseases in Europe is still poorly known, especially when multiple pathogens are involved. Furthermore, early detection of these disorders is currently not systematic, as detection is often based on the occurrence of clinical signs. This increases the need for collective or preventive antibiotic treatment, although it is unclear whether early targeted treatment would be sufficient. Private managers of farm animal health (farmers, vets, technical advisors, etc.) lack the tools to assess the risk of occurrence of these diseases over time and to prioritize management measures, as the added value of these measures varies greatly depending on the health situation.

Assumptions and scientific questions

The objective of this PhD is to predict the occurrence of respiratory diseases in young cattle over time, taking into account the diversity of health contexts (animal species, breeding system, co-infection, local/national prevalence, etc.) and the control options that can be mobilized (preventive treatment, targeted treatment, early vaccination, management of animal origins, etc.) For this purpose, a generic modelling framework representing in a mechanistic and stochastic way the occurrence of respiratory diseases in young animals will be mobilized and adapted to the case of young cattle in order to identify the most relevant management strategies according to the health context. The main assumptions are: (1) similarities between health contexts are sufficient to define a generic modelling framework; (2) data that can be routinely collected on farm can be used to calibrate and specify the models, and even to estimate the most likely pathogen(s) involved; (3) the most relevant management options vary depending on the health context.

Main steps of the PhD

1. To contribute to the development of a generic stochastic mechanistic epidemiological modelling framework that allows the prediction of respiratory diseases in young animals (cattle, pigs, poultry), at the scale of a group of animals (a batch, or even a herd), for a wide range of sanitary contexts. [3 months]
2. To apply this generic framework to the case of young dairy and suckling cattle, in collaboration with the Univ. Gent (Belgium) which has relevant data to calibrate the models. An existing mono-pathogen model will be extended to the case of co-infections, especially between viruses and bacteria. Collaborate with the DYNAMO team on the methods of inferring the model parameters and apply the proposed method to the case study. [18 months]
Depending on the profile of the successful candidate (two types of initial training possible: biology/epidemiology with a master’s degree in modelling; formal sciences with a strong taste for applications to living organisms), the emphasis will be on one or the other of the following steps:
3a. To contribute to data collection in France on young beef cattle, in collaboration with Terrena and IDELE, to apply the model of step 2 to the French case. [5 months]
3b. To generate synthetic data with the model developed in step 1 and/or 2, and thus contribute to the coupling of automatic learning approaches (early detection) and mechanistic models (projections and comparison of scenarios), in collaboration with Univ. Copenhagen. [5 months]
4. To mobilize the developed coupling method to inform the initial conditions of the model and include management strategies (preventive / targeted / systematic treatment for different detection methods, early vaccination) in the model. Compare the strategies by intensive simulations for a large panel of sanitary contexts and initial conditions, in collaboration with Univ. Gent, Ter'élevage, and IDELE. [10 months]


The generic multi-level multi-agent modelling framework EMULSION [Picault et al. 2017] will be mobilized to develop a multi-pathogen stochastic epidemiological model adapted to young cattle breeding in different health contexts. The model will be calibrated using an inference algorithm developed in the DYNAMO team. The data to be mobilized are available, routinely collected in several European countries (demographic data, trade, weight, antibiotic use and occurrence of clinical signs, pathogens involved). The initial conditions will be derived from predictions of learning models for the early detection of respiratory disorders in livestock, made by a partner team of the project. The model developed in the PhD will be analysed by intensive numerical simulations. Access to data, computing resources and servers necessary for the success of the PhD is provided. Interactions are strongly encouraged with the partners of the European project in which this PhD is included, who will mobilize the model produced to adapt it to their specific contexts.

Required skills

Candidates should have a master's degree in applied mathematics/computer science with knowledge in epidemiology, or a master's degree in ecology/epidemiology/agronomy/veterinary science with demonstrated skills in mechanistic modelling. Programming skills (Python/R) and experience in modelling applied to epidemiology or population dynamics are expected. Interest in infectious diseases and interdisciplinary research is a plus. Organizational skills and written and oral communication skills in English are highly recommended, as this PhD is part of a European project.

How to apply:

- 1st phase: CV + motivation letter + name and email address of 2 referees
- 2nd phase: full application at the doctoral school EGAAL & interview

Deadline for the 1st phase: 7th of June 2021

Download the PhD offer for more information: