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A Six-Step Protocol for Monitoring Antimicrobial Resistance Trends Using WHONET and R: Real-World Application and R

Fabio Ingravalle1, Antonio Vinci1, Marco Ciotti2

  • 1Department of Biomedicine and Prevention, Tor Vergata University of Rome, 00133 Rome, Italy.

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|October 28, 2025
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Summary
This summary is machine-generated.

Antimicrobial resistance (AMR) surveillance is crucial globally. This study details using free WHOnet and R software for reproducible AMR trend analysis, aiding public health decisions.

Keywords:
R programmingantimicrobial resistancemicrobiology laboratory dataopen-source toolssurveillance

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Area of Science:

  • Microbiology
  • Public Health
  • Bioinformatics

Background:

  • Antimicrobial resistance (AMR) is a significant global health threat.
  • Existing AMR monitoring tools are underutilized due to resource and knowledge limitations.
  • WHO has developed free software, WHOnet and R, for AMR surveillance.

Purpose of the Study:

  • To provide a standardized, reproducible workflow for retrospective AMR trend analysis using WHOnet and R.
  • To offer practical, step-by-step instructions and R code for implementing AMR surveillance.
  • To demonstrate the utility of these tools with real-world data from an Italian hospital.

Main Methods:

  • Utilizing WHOnet for laboratory data and antimicrobial susceptibility testing (AST) results management.
  • Employing R, a statistical programming language, for data analysis and visualization.
  • Combining WHOnet and R to establish a reproducible workflow for AMR trend analysis.

Main Results:

  • Demonstrated a reproducible workflow for retrospective AMR trend analysis using real-world data.
  • Provided functional R code for standardized AMR data analysis.
  • Illustrated the application of the workflow using data from an Italian hospital setting.

Conclusions:

  • Standardized AMR analysis using WHOnet and R enhances data availability for clinicians and public health professionals.
  • Rapid access to AMR data supports evidence-based empirical antimicrobial treatment decisions.
  • The described methodology facilitates effective AMR monitoring and management in diverse healthcare settings.