Data-Driven Early Warning Approach for Antimicrobial Resistance Prediction-Anomaly Detection Based on High-Level...

Area of Science:

• Environmental Science
• Public Health
• Data Science

Background:

• Antimicrobial resistance (AMR) emergence is linked to environmental conditions.
• Early detection of high-risk AMR situations is challenging.
• Environmental factors require integrated analysis for AMR risk assessment.

Purpose of the Study:

• To develop a data-driven framework for identifying anomalous environmental profiles associated with AMR risk.
• To detect unusual environmental patterns indicative of potential AMR hotspots.
• To enable early-warning strategies for AMR surveillance.

Main Methods:

• Utilized an unsupervised anomaly detection method (Isolation Forest).
• Applied the method to multivariate environmental indicators: pesticide use, land use change, precipitation, and crop type.
• Analyzed environmental data to identify anomalous profiles without prior AMR data.

Main Results:

• Pesticide use, population density, land use change, and fertilizer application were identified as dominant environmental factors.
• These factors explained a significant share of variation in anomaly scores.
• Fertilizer and pesticide intensity strongly influenced anomalous environmental profiles, highlighting their role in AMR risk.

Conclusions:

• The developed framework can identify global drivers and context-dependent risks of AMR.
• Interpretable anomaly detection aids in understanding environmental contributions to AMR.
• The framework supports the development of proactive, data-driven AMR surveillance strategies.