About the project: 

Epidemic mechanistic models are helpful to better understand and anticipate pathogen spread in host populations under contrasted situations, e.g. to prioritize disease control strategies. To reinforce the robustness and precision of model predictions, and thus their usefulness for stakeholders, model parameter values should be inferred on observed data. However, available data sources are heterogeneous (data on host demography and location, epidemiological data, sensor data, etc.). Another challenge is to define model initial conditions according to (if possible real-time) field observations. You will develop an inference algorithm able to tackle such heterogeneous data sources and relevant for stochastic epidemic models. Several options will be considered: simple likelihood-based methods often used in experimental settings but less suitable to heterogeneous field data and stochastic models; Approximate Bayesian Computations (ABC) and its relatives (such as ABC-SMC); the recent approach developed by INRAE, which uses a criterion based on a Monte-Carlo (MC) approximation of a composite likelihood coupled to a numerical optimization algorithm (Nelder-Mead Simplex). These approaches will be compared. The simplest most relevant (and sufficiently generic) one will be used to develop the algorithm which will be used to feed by data specific mechanistic models of the case studies of a H2020 EU project (DECIDE). 

Job profile: 

– PhD in biostatistics / biomathematics or in ecology / epidemiology with strong quantitative skills
– Experience in complex system modelling and biological data analyses
– Significant computational/programming skills (C++, Python, R)
– Interest in infectious diseases, epidemiology, ecology, interdisciplinary research
– Strong organizational and written/oral communication skills
– Be highly motivated towards scientific research

Starting Date and Duration: 

January 2023 (24 Months)

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