Modelling and Mitigating Antimicrobial Resistance (Amr) Through Data-Driven Surveillance, AI-Powered Drug Discovery, and Public Health Intervention Design

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Quantitative analysis predicts AMR trends in Nigeria, suggesting AI and public interventions can mitigate resistance.

AMR prevalence reached 42.3% among studied populations, highlighting the critical need for effective interventions.
Machine learning models achieved high accuracy, predicting resistance with AUC scores as high as 0.93 using various algorithms.
Analysis of surveillance data and predictive modeling indicated that public health interventions could cut AMR rates by 55% over ten years.
The research underscores the potential of combining AI, large data analysis, and public health strategies for managing AMR effectively.

ODEKUNLE et al. (2025) studied this question.

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View Full Paper

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Authors

Quantitative analysis predicts AMR trends in Nigeria, suggesting AI and public interventions can mitigate resistance.

AMR prevalence reached 42.3% among studied populations, highlighting the critical need for effective interventions.
Machine learning models achieved high accuracy, predicting resistance with AUC scores as high as 0.93 using various algorithms.
Analysis of surveillance data and predictive modeling indicated that public health interventions could cut AMR rates by 55% over ten years.
The research underscores the potential of combining AI, large data analysis, and public health strategies for managing AMR effectively.

ODEKUNLE et al. (2025) studied this question.

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