Eyango Tabi T. G. Loïc

Thesis summary:

Effective management of infectious diseases in livestock requires detecting and forecasting outbreaks despite the complexity of host–pathogen–environment interactions and the difficulty of
extracting relevant information from farm sensors. This thesis proposes an innovative approach that
directly couples sensor data with knowledge derived from mechanistic epidemiological modeling. By
combining deep learning, which can automatically extract patterns from complex signals, with
mechanistic models based on veterinary expertise, we aim to improve both short-term diagnosis and longterm disease predictions in livestock. The main contributions of this work are: (1) coupling empirical sensor data with mechanistic simulations to fill the gap between sensor-based observations and theoretical knowledge; (2) explicitly incorporating uncertainty into predictions to enhance reliability; and (3) developing a method to differentiate pathogen-specific scenarios to guide targeted interventions. Applied to respiratory diseases in young cattle (BRD), our methods have demonstrated, under both real and simulated conditions, their ability to automate shortterm
diagnosis and long-term predictions BRD dynamics, thereby significantly reducing antibiotic use and improving farm performance. This work opens new perspectives by proposing a modular methodology that combines sensor data and knowledge, potentially serving as an innovative
decision-support tool for optimized health management.

Keywords:

Sensors, artificial intelligence, deep learning, epidemiological mechanistic modelling, animal health, BRD