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Last update: May 2021

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Sensor-Enhanced Projection Tool Informed by an Epidemiological Model: A decision-support system based on the coupling between real-time sensor data and a mechanistic epidemiological model of bovine respiratory diseases (leader: S. Picault; funding: Carnot Institute F2E)


Bovine Respiratory Diseases (BRD) are a strong burden in the beef fattening sector. Their rapid spread in newly formed feedlots at the beginning of fattening period and the risk of late detection encourage farmers to use massive doses of antimicrobials. Yet, modelling approaches suggest that early detection, especially based on sensors such as intraruminal boluses or neck tags, and individualized treatments could help both to control the impact of BRD and to make a reasonable use of antimicrobials. The goal of the SEPTIME project is to design a real-time decision support tool based on the combination of a mechanistic model of BRD and real-time sensor information, so that the theoretical situations implemented in the model can actualize their projections according to the situation observed on the field. Such a tool will be the first proof of concept of 1) an innovative method to transfer epidemiological models to control at the farm level, 2) the added-value of informing mechanistic models with real-time data to better anticipate clinical episodes and enhance the cost-effectiveness of treatments (fostering the reduction of antimicrobial usage). This project will involve two F2E entities (BIOEPAR and Idele, a technical institute), several industrial partners (Allflex as project member, and two subcontracting cooperatives among Ter’Elevage, EMC2 élevage, Cialyn, ELVEA…), and two non-F2E academic partners (IRISA and LS2N). Thus, the scientific issues will be addressed through the most relevant collaborations, and the proposed solutions designed and assessed within a perspective of direct connection to field implementation. This will ensure that the targeted proof of concept (TRL3) will be easier to transfer later in industrial production conditions. Beyond this operational tool, the project will be a first practical step towards the development of generic methods for the use of sensors for disease control in livestock.