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Area of Science:

  • Environmental science
  • Microbiology
  • Public health

Background:

  • Antimicrobial resistance (AMR) is a growing public health threat.
  • Existing AMR models often overlook agricultural system interconnectedness.
  • Campylobacter jejuni is a key foodborne pathogen in poultry, often developing ciprofloxacin resistance.

Purpose of the Study:

  • To develop a probabilistic framework for identifying high-risk environmental conditions for AMR.
  • To investigate the influence of agricultural system factors on ciprofloxacin resistance in Campylobacter jejuni.
  • To utilize a Bayesian approach to model complex interactions driving AMR.

Main Methods:

  • Integrated and discretized data from international databases.
  • Employed graph-based machine learning, including XGBoost, SVC, Generalized Naive Bayes (GNB), and Bayesian Networks (BN).
  • Used GNB for feature selection and BN to uncover conditional dependencies between environmental factors and resistance.

Main Results:

  • Pesticides, land use, and precipitation were identified as key features influencing resistance.
  • Bayesian networks revealed context-dependent resistance probabilities.
  • Precipitation acted as a critical effect modifier, altering resistance risk based on land use and moisture levels.

Conclusions:

  • Bayesian networks effectively model complex, non-linear relationships driving AMR.
  • Ciprofloxacin resistance in poultry is a result of system-wide interactions, not isolated factors.
  • This framework supports early-warning systems for targeted antimicrobial stewardship in poultry production.