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Members of the jury :

• Reviewers :
  • Pascal HENDRIKX Inspector General of Veterinary Public Health, National School of Veterinary Services-IVF
  • Karine CHALVET-MONFRAY, Professor, VetAgro Sup, Marcy l’Étoile, France
• Examiners :
  • Claude SAEGERMAN, Professor at the Faculty of Veterinary Medicine, University of Liege, Belgium
  • Thimothée VERGNE, Lecturer, ENVT, Toulouse
  • Nathalie BAREILLE, Professor, Oniris, Nantes
• PhD supervisor : Christine FOURICHON, Professor, Oniris, Nantes
• PhD co-supervisor : Aurélien MADOUASSE, Lecturer, Oniris, Nantes

Abstract :

At the territorial level, collective programs for the control of unregulated infectious diseases in cattle have multiple benefits. They also create difficulties in exchanges between territories because their definitions of "infection-free" status differ. Estimating a probability (of absence) of infection for each herd, calculated independently of the surveillance modalities, would allow secure trade of animals between territories.
This type of estimation could be used for an output-based surveillance, based on a result to be achieved and not on the means implemented. The objectives of this thesis were to contribute to the development and evaluation of a
The objectives of this thesis were to contribute to the development and evaluation of a method for estimating the probability of infection at the herd level, based on heterogeneous surveillance data. Using the example of bovine viral diarrhea virus infection, relevant and available information was identified and organized. The model developed is a hidden Markov model estimating a probability of infection at the herd level from test results and risk factors of infection. Its performance was evaluated on simulated data representing a variety of infection dynamics and control programs. The evaluation showed that the added value of the model is more important when the sensitivity of the diagnostic test is low. The added value of risk factors seems limited. The use of this model requires further development for the classification of herds as free/infected based on predicted infection probabilities.