Mots-Clés

epidemiology mechanistic modelling disease surveillance bovine viral diarrhea (BVD)

Description

Proposal description

Epidemic mechanistic models are helpful to better understand and anticipate pathogen spread in host populations under contrasted situations, e.g. to target surveillance and rank interventions. In livestock, host populations are heterogeneous with regards to farm spatial distribution, farm structure, and animal types, with various contacts among farms through animal movements and neighboring relationships. This impacts disease spread, and therefore our ability to detect an emergence and to control pathogen spread at a large between-farm scale.

You will work at defining optimal surveillance schemes according to territorial specificities (farm size, breed, activity, location, pasture usage) and accounting for limiting resources. You will also highlight the required interventions to be implemented once a new virus introduction has been detected to quickly control the situation and ensure the disease-free status to be kept.

This work will be applied to bovine viral diarrhea (BVD), for which a mechanistic stochastic epidemiological model has been developed in C++, fed by observed and comprehensive cattle demographic data. BVD is one of the top enzootic cattle diseases in Europe in terms of economic losses for farmers, also impacting animal welfare, and has become a regulated disease since a few years in Europe. Several areas are currently free from the disease, with a challenge to have effective surveillance schemes to intervene as early as possible in case of virus introduction from other (infected) areas. A focus will be made on external and internal biosecurity measures, in the frame of a European partnership in Animal Health and Welfare. Data, computing and operating resources are available for this project.

Expected skills

• PhD in epidemiological modelling
• Excellent programming skills (Python, R, C++)
• Interest in infectious diseases, epidemiology, interdisciplinary research
• Strong organizational and written/oral communication skills, fluency in English
• Be highly motivated towards scientific research