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The neglected model validation of antimicrobial resistance transmission models - a systematic review
Maja L Brinch1, Andrea Palladino2, Jeroen Geurtsen3
1Risk-Benefit, DTU National Food Institute, Kgs. Lyngby, Denmark. malbri@food.dtu.dk.
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.
Insights
Mathematical transmission models are crucial for public health interventions against antimicrobial resistance. However, persistent gaps in scope, documentation, and validation hinder their effectiveness and updating. Future research must prioritize robust practices and new validation data.
Area of Science:
- Mathematical modeling
- Public health
- Epidemiology
Background:
- Mathematical transmission models are vital tools for guiding public health interventions against antimicrobial resistance.
- Previous studies have identified modeling gaps that require further investigation.
Purpose of the Study:
- To review persistent modeling gaps in antimicrobial resistance transmission studies.
- To broaden the scope to include novel control measures like monoclonal antibodies and assess the impact of secondary infections.
- To guide future modeling efforts through analysis of model development and documentation.
Main Methods:
- Systematic review conducted following PRISMA guidelines.
- Identification and description of gaps in model focus, dynamics, and reporting.
- Application of the TRACE paradigm to assess model development and documentation in selected studies.
Main Results:
- 170 transmission studies (2010-2022) were analyzed, with Mycobacterium tuberculosis and Staphylococcus aureus resistance being most frequently modeled.
- An increasing trend in studies examining multiple interventions (drug therapy, vaccination) was observed.
- Population-based compartmental models were predominant; however, the TRACE framework application revealed a lack of software testing, verification, and external data validation in many studies.
Conclusions:
- Significant gaps persist in the scope, geographical coverage, drug-pathogen combinations, and viral-bacterial dynamics of antimicrobial resistance models.
- Inadequate documentation impedes model updates and consistent policy recommendations.
- There is a critical need for robust modeling practices, including the incorporation of new data for validation, to refine models and ensure reliable outcomes for policymakers.
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