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This scoping review is a synthesis of the evidence based on the existing systematic and scoping reviews on mathematical modelling and predictive analytics of antimicrobial resistance (AMR) dynamics. It seeks to trace the use of mechanistic transmission models and machine-learning (ML) methods to understand the emergence, spread and prediction of AMR in pathogens, hosts and settings. A total of 10 studies were included: six systematic reviews of mechanistic or transmission models, three systematic reviews of ML-based resistance prediction and one narrative review, providing a broader conceptual context on AMR modelling. Mechanistic examinations reveal that deterministic compartmental model structures in human medical centres or community contexts prevail with a paucity of One Health consolidation, limited external validation and under-representation of WHO priority Gram-negative organisms and low and middle-income countries. ML model reviews state that patient-level resistance prediction has promising discriminative performance (summary area under the receiver operating characteristic curve (AUC) of approximately 0.78–0.82 across ML-based prediction systematic reviews) based on heterogeneous data pipelines, however, the studies have mainly been applied in a retrospective, single-centre design, and are likely to be susceptible to bias and lack prospective impact assessment. In both fields, major gaps are associated with validation, transparency, reproducibility, coverage of pathogens and settings and correlation with policy-relevant economic and decision measures. Development of hybrid mechanistic-ML frameworks and explicit One Health-oriented modelling strategies, aligned with WHO GLASS and national AMR action plans, are required to strengthen AMR policy and clinical decision-making.
Mahajan et al. (Sun,) studied this question.