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Objectives

Develop a generic supervised learning approach to conceive an epidemiological surveillance system informed by heterogeneous data.

Tasks

• Deploy the massive and diverse data gathered in or generated from the information system for supervised learning
• Develop statistical learning and optimization algorithms in space-time contexts to identify risk factors. Produce risk maps and design improved surveillance strategies
• Use artificial intelligence paradigms and tools in a loop to (i) learn the risk (and its determinants) from data, and (ii) plan data collection from the current risk evaluations
• Conceive prophylactic strategies founded on different traits of risk maps at different temporal horizons.

WP4 leader:  Edith Gabriel

Partners: BioSP, ECODEV, TETIS, MaIAGE, PHIM, SAVE, Pvbmt  (see partner information on the About Us page)