Postdoctoral position in predictive modelling of avian influenza spillover risk in mammalian hosts

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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 be welcomed in the AnimalS health -Territories - Risks - Ecosytems (ASTRE) unit. With more than 100 permanent agents, the objective of the UMR ASTRE is to improve animal health, public health and food security in the South, particularly in the context of global changes and transitions of socio-ecosystems. International health monitoring (IHS) plays a central role in the early detection of the emergence of new pathogens or the re-emergence of existing pathogens.

The post-doctoral position is integrated in the Epidemic Intelligence (EI) team of the UMR ASTRE. It is part of the European project Horizon H2025 GeoAI4EI (Leveraging Artificial Intelligence for Pandemic Preparedness and Response). This project aims to develop an open-source, reliable and ethical toolbox, based on artificial intelligence, to improve the exploitation of epidemic, epidemiological and socio-ecological intelligence data from multiple sources to strengthen preparedness and response to epidemics and pandemics in Europe.

Articifical intelligence (AI) can enhance monitoring by scanning official and unofficial sources for symptom clusters signalling emerging threats. A multisource surveillance tool (MUST) was developed to collect, compile, and visualize Highly Pathogenic Avian Influenza in mammals (HPAIM) events since January 2021, combining an official source from the World Animal Health Information System (WAHIS) with two unofficial ones (EBS tools : PADI-web, ProMED-mail).

Your mission will be to develop a global near-real-time Bayesian spatio-temporal model for assessing the risk of spillover of avian influenza viruses into mammalian hosts, using structured and unstructured data collected by MUST. The activities will be conducted with the EI team of ASTRE UMR in close collaboration with TETIS UMR in charge of integration of new fusion functionalities in MUST based on AI techniques to identify redundant and new information (e.g., BioELECTRA, EpidGPT23 and LLM). 

You will be more specifically in charge of the following activities:

1. Provide epidemiological inputs on MUST sources and data integration
2. Conduct exploratory analysis to characterize geographical and temporal coverage of the different sources of data. 
3. Developp a spatio temporal model on sporadic cases and local spread HPAIM events, and early detect anomalies
4. Identifyi covariates (e.g. host species density, land use, seasonality) and testing potential risk factors,
5. Provide  adapted surveillance strategy in using MUST outcomes in daily surveillance 
6. Provide recommandations for HPAIM early detection 

Training and skills

Recommended training: PhD in epidemiology or surveillance of animal health with strong skills in quantitative analyses and modelling, with minimum 1 year of experience

Desired knowledge: avian influenza epidemiology and various data sources related to HPAI viruses

Appreciated experience: evaluation strategies of event-based surveillance system, AI computing

Required Skills

• Classical statistical methods (hypothesis testing, linear models, multivariate analyses, etc.)
• Machine learning methods (supervised/unsupervised learning, neural networks, decision trees) and Bayesian approaches (a plus)
• Spatio-temporal analysis methods
• Data-sharing platforms (e.g., Dataverse)
• R (required) for statistical analysis, visualization, and reproducible pipeline automation (Make, targets, Quarto); 
• Git and collaborative platforms such as GitLab
• Reproducible computing environments and containers (e.g., Docker, Singularity, Apptainer); Nix/Guix (a plus)
• Good practices in code reproducibility and version control 
• FAIR principles: data structuring, documentation, and metadata; controlled vocabularies and ontologies

Technical Competencies

• Ability to select and apply statistical methods appropriate to the scientific questions at hand
• Ability to interpret and critically assess results

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

I send my CV and my motivation letter