Understanding Antimicrobial Resistance: Insights from Mathematical Models and Predictive Analytics

What is it about?

The study conducted a scoping review to synthesize evidence from existing systematic and scoping reviews on mathematical modelling and predictive analytics of antimicrobial resistance (AMR) dynamics. The research focused on mechanistic transmission models and machine-learning methods to understand the emergence, spread, and prediction of AMR in various contexts. It included ten studies: six systematic reviews of mechanistic models, three reviews of ML-based resistance prediction, and one narrative review on AMR modelling. Mechanistic models were primarily deterministic compartmental structures with limited One Health integration and external validation. ML-based models showed high discriminative performance in resistance prediction but were mostly retrospective and single-centre, with limited prospective impact assessment. The study identified significant validation, transparency, and reproducibility gaps, emphasizing the need for hybrid mechanistic-ML frameworks and One Health-oriented strategies. The research highlighted the importance of aligning modelling efforts with WHO GLASS and national AMR action plans to improve AMR policy and clinical decision-making.

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Why is it important?

This study is important because it provides a comprehensive synthesis of the existing evidence on mathematical modelling and predictive analytics for understanding and predicting antimicrobial resistance (AMR) dynamics. By evaluating both mechanistic transmission models and machine-learning methods, the study highlights significant gaps and opportunities for improvement in AMR modelling. This is crucial for informing policy and clinical decision-making, particularly as AMR poses a growing threat to global public health. The research underscores the need for integrative and validated models that align with global health frameworks, which can ultimately enhance our capacity to manage and mitigate the impacts of AMR across different settings. Key Takeaways: 1. Mechanistic Modelling Gaps: The study finds that mechanistic models predominantly use deterministic compartmental frameworks, with notable deficiencies in external validation, One Health integration, and representation of low and middle-income countries (LMICs), limiting their policy utility. 2. Machine-Learning Performance: ML-based resistance prediction models show promising discriminative performance but are primarily retrospective and single-centre, lacking prospective impact evaluations, thus limiting their generalizability across diverse healthcare settings. 3. Need for Hybrid Frameworks: The study highlights the potential benefits of developing hybrid mechanistic-ML frameworks and incorporating health-economic endpoints to address AMR's complexity and support decision-making, particularly in high-burden settings.