Mapping out a One Health model of antimicrobial resistance in the context of the Swedish food system: A literature scan
Bibliographic record
Abstract
Abstract Background: Antimicrobial resistance (AMR) causes worsening health, environmental, and financial burdens. Modeling complex issues such as AMR can help clarify the behaviour of the system and assess the impacts of interventions. While models exist for specific AMR contexts (e.g. on-farm, in hospital), due to inadequate collaboration and data availability, how well such models cover the broader One Health system is unknown. Our study aimed to identify models of AMR across the One Health system with a focus on the Swedish food system (objective 1), and data to parameterize the models (objective 2), to ultimately inform future development of a comprehensive model of possible AMR emergence and transmission across the entire system. Methods: Using a previously developed causal loop diagram (CLD) of factors identified as important in the emergence and transmission of AMR in the Swedish food system, an extensive literature scan was performed to identify models and data from peer-reviewed and grey literature sources. Articles were searched using Google, Google Scholar, and Pubmed, screened for relevance, and the models and data were extracted and categorized in an Excel database. Visual representations of the models and data were overlayed on the existing CLD to illustrate coverage. Results: A total of 126 articles were identified, describing 106 models in various parts of the One Health system; 54 were AMR specific. Four articles described models with an economic component (e.g. cost-effectiveness of interventions, cost-analysis of disease outbreaks). Most models were limited to one sector (n=60, 57%) and were compartmental (n=73, 69%); half were deterministic (n=53, 50%). Few multi-level, multi-sector models, and models of AMR within the animal and environmental sectors, were identified. A total of 414 articles were identified that contained data to parameterize the models. There were major data gaps for factors related to the environment, wildlife, and broad, ill-defined, or abstract ideas (e.g. human experience and knowledge). Conclusions: There were no models that addressed the entire system and few that addressed the issue of AMR beyond one context or sector. Existing models have the potential to be integrated to create a mixed-methods model, provided that data gaps can be addressed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.024 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".