Large-scale epidemiological modelling for improving animal disease surveillance and monitoring

Large-scale epidemiological modelling for improving animal disease surveillance and monitoring

CDD·Postdoc·12 moisBac+8 / Doctorat, Grandes Écoles BIOEPAR (UMR 1300) ·Nantes (France)Basic gross salary ~3100-4000 €/month (according to experience)

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mechanistic models epidemiology disease surveillance control strategies

Description

Epidemic mechanistic models are valuable tools to better understand and anticipate pathogen spread in host populations under contrasted epidemiological and management situations, e.g. to support the design of surveillance strategies and the comparison of alternative control measures. In livestock populations, disease spread is strongly influenced by the heterogeneity of farms in terms of spatial distribution, herd size and structure, production type, grazing practices, and contacts between farms through animal movements and spatial proximity. These characteristics can substantially affect both the early detection of pathogen introductions and the effectiveness of control strategies at the regional scale.

The main objective of the postdoctoral project will be to design, simulate and compare surveillance and control scenarios adapted to different territorial and epidemiological contexts. You will review and formulate relevant surveillance strategies, accounting for territorial characteristics (farm size, production type, location, pasture use), as well as practical constraints on available surveillance resources. You will also define and assess alternative intervention strategies to be implemented following the detection of a new virus introduction, with the aim of rapidly controlling transmission and maintaining disease-free status.

The project will rely on an existing stochastic mechanistic epidemiological model, developed in C++ and parameterised using comprehensive empirical data on cattle demography and movements. You will use this model to run the different scenarios, design appropriate simulation experiments, and analyse their outputs. A major part of the work will consist in quantitatively comparing surveillance and control strategies, identifying trade-offs between epidemiological effectiveness, timeliness of detection or control, and resource requirements, and determining which strategies perform best under different territorial configurations.

The application will focus on bovine viral diarrhoea (BVD), one of the major endemic cattle diseases in Europe in terms of economic losses and animal welfare impacts. BVD has recently become a regulated disease in Europe, and several regions are now free from infection. A major challenge is therefore to develop efficient surveillance strategies able to detect virus reintroductions as early as possible and to identify the most effective measures for preventing subsequent spread. The project is designed to build rapidly on existing modelling, data and computing resources. The expected outcome is a publishable comparative assessment of surveillance and control strategies, and the work will therefore be organised from the outset with the objective of producing a scientific publication within the postdoctoral period. xuezdbg All required data, modelling tools, computing infrastructure and operational resources are already available.

• PhD in epidemiological modelling or equivalent
• Strong programming and data analysis 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