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  • team
  • department
  • center
  • program_project
  • nrc
  • whocc
  • project
  • software
  • tool
  • patent
  • Administrative Staff
  • Assistant Professor
  • Associate Professor
  • Clinical Research Assistant
  • Clinical Research Nurse
  • Clinician Researcher
  • Department Manager
  • Dual-education Student
  • Full Professor
  • Honorary Professor
  • Lab assistant
  • Master Student
  • Non-permanent Researcher
  • Nursing Staff
  • Permanent Researcher
  • Pharmacist
  • PhD Student
  • Physician
  • Post-doc
  • Prize
  • Project Manager
  • Research Associate
  • Research Engineer
  • Retired scientist
  • Technician
  • Undergraduate Student
  • Veterinary
  • Visiting Scientist
  • Deputy Director of Center
  • Deputy Director of Department
  • Deputy Director of National Reference Center
  • Deputy Head of Facility
  • Director of Center
  • Director of Department
  • Director of Institute
  • Director of National Reference Center
  • Group Leader
  • Head of Facility
  • Head of Operations
  • Head of Structure
  • Honorary President of the Departement
  • Labex Coordinator
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© Research
Project

Precision Public Health and One health

Scientific Fields
Diseases
Organisms
Applications
Technique
Starting Date
02
Apr 2020
Status
Ongoing
Members
1

About

Our ambition is to generate a paradigm shift in the way rabies is monitored, studied and controlled, evolving from a situation where assessments about rabies burden, epidemiology and control are based on no-, limited or segregated data towards a situation where they can be supported by rich, precise and integrated datasets that fully capture all the local features of rabies epidemiology.

Our aims are to improve the quality and exhaustivity of the epidemiological data through the development of innovative diagnostic tools, digital collection of standardized data to document patient’s information seeking post exposure prophylaxis and understanding and monitoring viral dynamics in their natural host populations.

 

Our final objective is to build a conceptual understanding of rabies epidemiological drivers based on multidisciplinary data integration and modelling. We aim to translate this understanding into robust predictions and recommendations for policymakers including intervention evaluation and novel designs for sustainable surveillance strategies, as well as prediction of remaining foci in the elimination phase.