The neglected model validation of antimicrobial resistance transmission models – a systematic review - Maja L. Brinch; Andrea Palladino; Jeroen Geurtsen; Thierry Van Effelterre; Lorenzo Argante; Michael J. McConnell; Lene Christiansen; Michelle A. Pihl; Natasja K. Lund; Tine Hald (Antimicrobial Resistance and Infection Control)
Abstract Background In the fight against antimicrobial resistance, mathematical transmission models have been shown as a valuable tool to guide intervention strategies in public health. Objective This review investigates the persistence of modelling gaps identified in earlier studies. It expands the scope to include a broader range of control measures, such as monoclonal antibodies, and examines the impact of secondary infections. Methods This review was conducted according to the PRISMA guidelines. Gaps in model focus areas, dynamics, and reporting were identified and described. The TRACE paradigm was applied to selected models to discuss model development and documentation to guide future modelling efforts. Results We identified 170 transmission studies from 2010 to May 2022; Mycobacterium tuberculosis (n = 39) and Staphylococcus aureus (n = 27) resistance transmission were most commonly modelled, focusing on multi-drug and methicillin resistance, respectively. Forty-one studies examined multiple interventions, predominantly drug therapy and vaccination, showing an increasing trend. Most studies were population-based compartmental models (n = 112). The TRACE framework was applied to 39 studies, showing a general lack of description of test and verification of modelling software and comparison of model outputs with external data. Conclusion Despite efforts to model antimicrobial resistance and prevention strategies, significant gaps in scope, geographical coverage, drug-pathogen combinations, and viral-bacterial dynamics persist, along with inadequate documentation, hindering model updates and consistent outcomes for policymakers. This review highlights the need for robust modelling practices to enable model refinement as new data becomes available. Particularly, new data for validating modelling outcomes should be a focal point in future modelling research.