Workpackage 4

WP 4

WP 4

Machine learning


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


  • 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)

Modification date : 19 June 2023 | Publication date : 23 October 2020 | Redactor : C.E. Morris