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Mapping the landscape of mathematical models for antimicrobial resistance: a scoping review

Background: Antimicrobial resistance (AMR) is a major global public health problem, contributing to an estimated 4.95 million deaths in 2019 and projected to cause up to 10 million deaths annually and 100 trillion dollars in cumulative economic losses by 2050. Its emergence and spread result from complex biological, ecological, and socioeconomic interactions. Mathematical modelling is a key tool to study AMR dynamics, yet the literature remains fragmented and methodologically limited. This review synthesizes recent mathematical modelling studies to identify trends, biases, and research gaps. Methods: A scoping review following PRISMA-ScR guidelines was conducted. PubMed, Web of Science, and Scopus were searched for studies published between 2019 and 2024 that developed mathematical models of AMR. After screening and duplicate removal, 36 studies were included. Data were extracted using a structured framework covering model context, construction and parameters, and outputs and validation. Results: Most studies relied on deterministic ordinary differential equation (ODE) models and focused on bacterial resistance in human hosts, with only one adopting a One Health perspective. Conjugation and mutation were the most commonly modelled resistance mechanisms, whereas transduction, transformation, host immunity, spatial heterogeneity, and environmental components were rarely included. Few studies incorporated economic impacts, and a strong geographic bias toward high-income countries was observed. Conclusion: Mathematical modelling of AMR is an active field but characterized by limited methodological diversity. While deterministic ODE models have advanced understanding of AMR dynamics, future work should integrate stochasticity, spatial structure, ecological interactions, and One Health perspectives, as well as economic and social variables, to inform global strategies to mitigate AMR.