The neglected model validation of antimicrobial resistance transmission models – a systematic review
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...
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| Format: | Article |
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BMC
2025-05-01
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| Series: | Antimicrobial Resistance and Infection Control |
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| Online Access: | https://doi.org/10.1186/s13756-025-01574-x |
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| author | 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 |
| author_facet | 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 |
| author_sort | Maja L. Brinch |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4bdb3bc4c4a549b08da6134712dd08f6 |
| institution | OA Journals |
| issn | 2047-2994 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
| record_format | Article |
| series | Antimicrobial Resistance and Infection Control |
| spelling | doaj-art-4bdb3bc4c4a549b08da6134712dd08f62025-08-20T02:00:14ZengBMCAntimicrobial Resistance and Infection Control2047-29942025-05-0114111110.1186/s13756-025-01574-xThe neglected model validation of antimicrobial resistance transmission models – a systematic reviewMaja L. Brinch0Andrea Palladino1Jeroen Geurtsen2Thierry Van Effelterre3Lorenzo Argante4Michael J. McConnell5Lene Christiansen6Michelle A. Pihl7Natasja K. Lund8Tine Hald9Risk-Benefit, DTU National Food InstituteGSKBacterial Vaccines Discovery & Early Development, Janssen Vaccines & Prevention B.VJohnson & Johnson, Global Commercial Strategy OrganizationGSKDepartment of Biological Sciences, University of Notre DameRisk-Benefit, DTU National Food InstituteRisk-Benefit, DTU National Food InstituteRisk-Benefit, DTU National Food InstituteRisk-Benefit, DTU National Food InstituteAbstract 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.https://doi.org/10.1186/s13756-025-01574-xAntimicrobial resistanceTransmission modellingInterventionsSystematic reviewTrace criterion |
| spellingShingle | 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 The neglected model validation of antimicrobial resistance transmission models – a systematic review Antimicrobial Resistance and Infection Control Antimicrobial resistance Transmission modelling Interventions Systematic review Trace criterion |
| title | The neglected model validation of antimicrobial resistance transmission models – a systematic review |
| title_full | The neglected model validation of antimicrobial resistance transmission models – a systematic review |
| title_fullStr | The neglected model validation of antimicrobial resistance transmission models – a systematic review |
| title_full_unstemmed | The neglected model validation of antimicrobial resistance transmission models – a systematic review |
| title_short | The neglected model validation of antimicrobial resistance transmission models – a systematic review |
| title_sort | neglected model validation of antimicrobial resistance transmission models a systematic review |
| topic | Antimicrobial resistance Transmission modelling Interventions Systematic review Trace criterion |
| url | https://doi.org/10.1186/s13756-025-01574-x |
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