Modeling for antimicrobial resistance control

Modeling for antimicrobial resistance control

The overall goal of the program is to enhance antimicrobial resistance (AMR) control by improving quantitative approaches used to understand processes underpinning the emergence and persistence of AMR and its control and prevention in natural microbial populations.

Antimicrobial use is a widely accepted risk factor for AMR among bacteria. However, while antimicrobial exposure imposes selection pressures for resistant microorganisms, reduced or complete absence of antimicrobial exposure oftentimes does not correspond to either a reduction in bacterial resistance or reversion to susceptibility. Known antimicrobial exposures in a specific host species only captures a subset of all potential exposures that may drive resistance outcomes; Co-selection mediated by other selective pressures such as heavy metals and biocides, and ecological processes can influence the persistence of resistance. Advances in sequencing technologies and electronic record keeping have expanded the amount of data relevant to understand the interplay of selective pressures influencing AMR dynamics. We aim to implement and evaluate quantitative methods to infer population-level dynamics of AMR and identify the diversity of exposures and ecological drivers experienced by natural microbial populations.

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  • Cristina Lanzas
  • William Love
  • Erin Frey
  • Annie Wang
  • Alba Frias
  • Sankalp Arya
  • Liton Chandra Deb

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