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Szilveszter Csorba^1,2, Krisztián Vribék^1,2, Máté Farkas^1,2

¹Department of Digital Food Science, Institute of Food Chain Science, University of Veterinary Medicine, H-1078 Budapest, Hungary.

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View abstract on PubMed

Summary

Environmental factors like pesticide use and land changes can signal antimicrobial resistance (AMR) risks. This study developed a framework to detect unusual environmental patterns, aiding early AMR surveillance and risk identification.

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.

Keywords:

AMR SHAP environmental drivers iForest

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.

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