What can routine surveillance data reveal about antibiotic resistance dynamics?

Introduction

This webinar will address two analyses of routine surveillance data designed to support parameterisation and construction of mechanistic mathematical models of AMR.

Time & Date

December 7th, 2023 11:00 am – 12:00 pm Eastern Time

Synopsis

Many key relationships in the dynamics of antimicrobial resistance (AMR) are still uncertain, such as the relative importance of transmission vs. selection, or the long-lasting effects of antibiotic exposure for an individual. Routine surveillance data provide a wealth of information but are limited by surveillance practices and local guidance. This webinar will address two analyses of routine surveillance data designed to support parameterisation and construction of mechanistic mathematical models of AMR:

  1. Analysis of data from Great Ormond Street Hospital on S. aureus comparing patient and hospital-level patterns.
  2. Analysis of European bloodstream infection data to explore the variance in resistance prevalence in infection by the age and gender of the patient.

The presentation will conclude with what this means for modelling AMR going forward and future implications of using routine surveillance data for AMR dynamics.

Participants are encouraged to submit questions when registering on Eventbrite or on Zoom during the event for the discussion period after the presentation. A recording of the webinar and presentation slides will be shared on the NCCID website after the event.

Moderator: Dr. Wendy Xie, Project Manager, NCCID

Speaker

Dr. Gwen Knight, PhD, is an Associate Professor at the London School of Hygiene and Tropical Medicine, and co-Director of the LSHTM Antimicrobial Resistance Centre. Gwen’s research is focused on mathematical modelling of the dynamics of antimicrobial resistance (or AMR). She currently has projects exploring the interaction between demography and AMR, One Health approaches to AMR at the farm level, and the spread of resistance genes by bacteriophage.