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Author Spotlight: Understanding and Detecting Environmental Antimicrobial Resistance by Combining Culture-Based Techniques and Genomics
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Addressing the Unknowns of Antimicrobial Resistance: Quantifying and Mapping the Drivers of Burden

Gwenan M Knight1, Ceire Costelloe1, Kris A Murray2,3

  • 1Health Protection Research Unit in Healthcare Associated Infection and Antimicrobial Resistance, National Institute of Health Research, and Department of Infectious Diseases.

Clinical Infectious Diseases : an Official Publication of the Infectious Diseases Society of America
|October 12, 2017
PubMed

Insights

Antimicrobial resistance (AMR) is a growing global threat driven by complex factors. A systems mapping approach is proposed to better understand AMR sources and improve intervention strategies.

Area of Science:

  • Microbiology
  • Public Health
  • Epidemiology

Background:

  • Antimicrobial resistance (AMR) presents a significant global health challenge.
  • The emergence and spread of AMR are driven by numerous interconnected factors and transmission pathways.
  • Current understanding of the relative importance of these factors is limited, hindering effective risk management.

Purpose of the Study:

  • To address the urgent need for reliable data on AMR.
  • To propose a systems mapping approach for understanding AMR drivers.
  • To strengthen global response strategies for antimicrobial resistance.

Main Methods:

  • A systems mapping approach is proposed.
  • This method aims to identify and quantify the sources and transmission pathways of AMR.
  • The goal is to generate timely and reliable data for AMR management.

Main Results:

  • The study highlights a lack of quantitative knowledge on AMR drivers.
  • Current interventions may be suboptimal due to this data gap.
  • A systems mapping approach is presented as a solution.

Conclusions:

  • Effective management of AMR requires a comprehensive understanding of its complex origins.
  • Systems mapping offers a novel approach to gather essential data.
  • Improved data will enable more targeted and effective interventions against antimicrobial resistance.
Keywords:
antimicrobial resistancemappingmathematical modelingquantification

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