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  • member
  • 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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Scientific Fields
Diseases
Organisms
Applications
Technique
Starting Date
01
Jan 2008
Ending Date
01
Feb 2020
Status
Ongoing
Members
3
Structures
1

About

Despite advances in modern biology and medicine, antibiotic-resistant bacteria (ARB) have spread in the community and in hospitals over the last decades, becoming a major public health problem. Mathematical modeling and computer simulations are powerful tools which can help public-health practitioners examine possible courses of dissemination of ARB and assess the efficacy of control strategies.
In this series of works, we use dynamic modelling and statistical inference approaches to understand better the spread of different types of ARB integrating various scales: within hosts, individuals (including patients and health care workers) and between-individual transmission within hospital wards.

Keywords: Bacteria, Antibiotic-resistance, Hospital, Transmission, Modelling, Statistical Inference, Public Health Impact