Factors that antimicrobial resistance in food systems: a participatory modelling approach
Bibliographic record
Abstract
Abstract Background Antimicrobial resistance (AMR) emerges from a complex web of factors; understanding their dynamics is key to determining sustainable solutions. Thus, we aimed to create a model of the diverse factors influencing AMR in two food systems and model the impacts of interventions. Methods We built a causal loop diagram (CLD) of the factors driving AMR in the food chain via 4 participatory workshops (2 in Sweden; 2 in Malaysia) with diverse stakeholders. The CLD became the structure of a compartmental model, which was populated using data from multiple sources (e.g., interviews, surveillance data). Using fuzzy set theory, quantitative and qualitative data were converted to categorical variables. The compartmental model was created in AnyLogic and was used to test how expert-selected solutions (e.g., taxation) might impact AMR under different scenarios. Results Factors identified as influencing AMR across Europe and Southeast Asia clustered around key themes: on-farm (e.g., biosecurity); social (e.g., consumer demand); research (e.g., alternatives to antimicrobials [AMs]); economic (e.g., agricultural production levels); policy (e.g., trade agreements); and environment (e.g., waste management). Differences were identified between regions, for example, regulations and standards regarding imports or food safety were more relaxed in Southeast Asia than in Europe. Identified interventions included: AMR education in schools, training diverse stakeholders in AM stewardship, increased on-farm biosecurity measures to limit disease and the need for AMs, and taxing AM-containing products. Conclusions Our model captured a range of multi-level, interlinked factors that impact AMR in the European and Southeast Asian food system contexts. Preliminary findings suggest that different principles need to be cultivated (e.g., polycentric governance, cross-sector partnerships) to ensure that interventions addressing AMR are sustainable over time. Key messages Our study visually characterized the interlinked factors that impact AMR transmission and emergence in food systems. Our approach provides a tool to model impacts of potential interventions.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".