Researcher in Spatial Epidemiology

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INRAE presentation

The French National Research Institute for Agriculture, Food, and Environment (INRAE) is a major player in research and innovation. It is a community of 12,000 people with 272 research, experimental research, and support units located in 18 regional centres throughout France. Internationally, INRAE is among the top research organisations in the agricultural and food sciences, plant and animal sciences, as well as in ecology and environmental science. It is the world’s leading research organisation specialising in agriculture, food and the environment. INRAE’s goal is to be a key player in the transitions necessary to address major global challenges. Faced with a growing world population, climate change, resource scarcity, and declining biodiversity, the Institute has a major role to play in building solutions and supporting the necessary acceleration of agricultural, food and environmental transitions.

Work environment, missions and activities

You will join the Epidemiology of animal and zoonotic diseases joint research unit, which is recognised for its research combining field studies and modelling within a One Health framework. You will work in close interaction with Guillaume FOURNIÉ and Xavier BAILLY, who will provide scientific guidance for your project. You will be based on the VetAgro Sup campus in Marcy-l’Étoile, near Lyon.

The unit aims to strengthen its research in spatial epidemiology. Its goal is to establish an original and ambitious research programme in this field, focused on new methodological developments for the early detection of emerging infectious diseases, the prioritisation of surveillance, and support for animal health policies, particularly their implementation at the territorial level.

This position is part of that broader initiative. Building on ongoing funded projects, existing spatially explicit datasets, and established collaborations with stakeholders involved in animal health risk management, your mission will be to develop new approaches in spatial epidemiology to improve the surveillance of animal infectious diseases. In particular, this work will aim to strengthen the ability of surveillance systems to detect emerging threats at an early stage and to identify the populations in which surveillance should be prioritised, especially during outbreaks. The position is also intended to provide you with the opportunity to demonstrate your ability to define, develop and lead an independent research programme in spatial epidemiology.

The approaches developed will go beyond representations of epidemiological proximity based solely on geographical distance. They will incorporate different mechanisms of interaction between populations, including animal movements between farms, which can create strong epidemiological connections between geographically distant sites. The aim will therefore be to combine models describing continuous spatial variation in risk with dynamic representations of host population demography and contact networks.

Particular attention will also be paid to the integration of heterogeneous data generated by different surveillance systems. The increasing availability of complementary datasets offers opportunities to improve risk estimation but, beyond the challenge of linking these datasets, also raises important methodological issues related to source-specific biases. The work will notably involve developing hierarchical models that explicitly represent the underlying epidemiological process as well as the observation processes specific to each data source. These models will need to account for differences in coverage, spatial and temporal resolution, detection sensitivity, sampling effort, and the propensity to report health events, in order to quantify and minimise biases associated with the available data.

You will be responsible for:

1. developing innovative methodological frameworks that, on the one hand, integrate spatial dimensions and contact networks and, on the other, combine heterogeneous data sources within models that explicitly represent epidemiological and observation processes. To achieve this, you will draw on Bayesian inference methods as well as recent developments in artificial intelligence;
2. developing reproducible and regularly updateable analytical pipelines, enabling the integration of new data and the production of spatial indicators in near real time to support the prioritisation of surveillance;
3. applying and evaluating these approaches across several case studies, which may include syndromic surveillance based on mortality data from livestock and wildlife, surveillance of highly pathogenic avian influenza in wild birds, and investigation of bovine tuberculosis transmission in France. Other pathosystems may be considered depending on scientific opportunities and the needs of partners involved in animal health surveillance;
4. developing and leading a research programme in spatial epidemiology, building collaborations within the unit and with national and international partners, disseminating results through scientific publications, software and reusable methods, and contributing to the development of new research proposals and funding applications. The aim will be to establish this methodological research area as a durable component of the unit’s scientific activities.

These activities will be conducted in close collaboration with epidemiologists, modellers, ecologists, computer scientists, and stakeholders involved in animal health surveillance. You will have a high degree of scientific autonomy to define your methodological directions, develop a research programme and establish new collaborations, in line with the unit’s scientific priorities.

Training and skills

Recommended qualifications: PhD in epidemiology, ecology, statistics, physics, applied mathematics or a related quantitative discipline.

Desired knowledge and skills: Strong knowledge of spatial statistical modelling and proficiency in at least one programming language suitable for data analysis and modelling. Knowledge of Bayesian inference, network analysis and artificial intelligence would be an asset.

Relevant experience: Experience in applying spatial statistical models to address research questions in epidemiology or ecology. Experience in handling complex datasets and developing reproducible analytical pipelines would also be an advantage.

Personal and professional skills: Ability to develop and conduct research with a high degree of autonomy, ability to work effectively in teams and collaborate with researchers from a range of disciplines, as well as with stakeholders involved in animal health surveillance and management, ability to disseminate research through publications in international scientific journals, excellent command of written and spoken English.

INRAE's life quality

By joining our teams, you benefit from (depending on the type of contract and its duration):

- up to 30 days of annual leave + 15 days "Reduction of Working Time" (for a full time);
- parenting support: CESU childcare, leisure services;
- skills development systems: training, career advise;
- social support: advice and listening, social assistance and loans;
- holiday and leisure services: holiday vouchers, accommodation at preferential rates;
- sports and cultural activities;
- collective catering.

How to apply

Please submit an application including a resume, a cover letter and a scientific position paper of no more than 1,500 words presenting the main methodological challenges addressed by the position, together with the concrete and well-argued scientific strategy you propose to address them in the context of this role.