Antimicrobial resistance (AMR) stands as a profound and escalating threat to global health, an insidious pandemic that transcends human, animal, and environmental boundaries. Despite mounting scientific evidence underscoring its devastating impact, translation of AMR science into effective policy remains frustratingly stalled. A consortium of international experts from top-tier institutions, including the University of Edinburgh and the London School of Hygiene and Tropical Medicine, underscores critical shortcomings within current mathematical modeling approaches that hinder the development of robust, actionable strategies against AMR. Their call to action emphasizes the necessity for transdisciplinary integration and international cooperation to forge coherent, multidimensional modeling frameworks.
Understanding AMR demands grappling with its intrinsic complexity as what researchers describe as a wicked problem. Unlike acute infectious outbreaks, AMR’s effects accumulate insidiously, often evading immediate detection and crisis framing. The challenge is exacerbated by its heterogeneous nature; a multitude of microbial species, differing antimicrobial agents, and contextual factors intertwine to generate an evolving resistance landscape. This “bug-drug-context” variability creates formidable barriers to effective communication, scientific consensus, and policy mobilization. Moreover, the disparity between the cost of interventions, primarily antimicrobial use (AMU) reduction, and the diffuse, long-term benefits leads to institutional inertia and policy ambivalence.
At the core of AMR mitigation lies the ambiguous and poorly defined relationship between AMU and resistance emergence. Global estimates of AMU are often derived through inferential methods grounded on data with known biases and gaps, complicating accurate assessment. Resistance mechanisms extend beyond direct antimicrobial exposure to encompass co-selection pressures from environmental contaminants such as biocides, heavy metals, and other pollutants. Particularly contentious is the contribution of AMU in livestock and aquaculture to human AMR burden—a critical point of debate influencing cross-sectoral policy directions. Alarmingly, environmental components crucial for understanding transmission dynamics are largely marginalized in prevailing models and surveillance efforts.
A comprehensive analysis of 273 population-level mathematical models reveals alarming trends and gaps. A staggering 89% focus exclusively on human populations, while only a marginal fraction integrate animals (7%) or plants (2%), and none provide a fully integrated ecosystem approach. Economic analyses are notably scarce, appearing in merely 9% of models, despite the centrality of cost-benefit evaluations for policy adoption and resource allocation. Furthermore, methodological rigor is undermined by the absence of sensitivity or uncertainty assessments in 40% of models, and none fully comply with the TRACE guidelines established for transparent and credible empirical modeling.
This deficiency relates closely to the hierarchical structuring of mathematical models used in AMR research. Models range from basic theoretical frameworks at the foundation, through statistically fitted models with internal validity, culminating in externally validated models leveraging independent data. The apex comprises multi-model comparison exercises, analogous to meta-analyses, which offer critical insights by juxtaposing divergent modeling approaches. However, AMR research to date remains anchored predominantly at theoretical and internally validated levels. External validation faces formidable obstacles given scarce, heterogeneous, and fragmented data sets, while multi-model comparisons are rendered infeasible by the diversity of pathogens, interventions, and epidemiological contexts.
Drawing parallels from climate change science, the AMR research community advocates reframing AMR as an environmental pollution crisis rather than a mere medical challenge. Climate science’s success rests significantly on developing abatement cost curves and standardized social cost metrics—tools enabling policymakers to evaluate and integrate cross-sectoral mitigation strategies effectively. Coordinated by the Intergovernmental Panel on Climate Change (IPCC), this approach provides a model for integrating economic and epidemiological data into comprehensive policy frameworks. Contrastingly, AMR policy remains fragmented, often operating without robust economic efficiency metrics or integrated modeling architectures to support international consensus or prioritization.
The absence of a unified cost-benefit modeling framework for AMR signifies a critical gap, representing both a challenge and an opportunity. The emergent Independent Panel on Evidence for Action against Antimicrobial Resistance (IPEA), currently under negotiation by the UN’s Quadripartite Group, holds potential to pioneer such architectures and elevate AMR policy coherence. Establishing standardized methodologies and data-sharing protocols across nations and sectors is vital, as is fostering synergistic cooperation between epidemiologists, economists, veterinarians, environmental scientists, and policymakers.
Addressing these multifaceted challenges mandates embracing transdisciplinary and international modeling collaborations. Surveillance data integration remains a foremost obstacle, impeded by disparities in methodological approaches spanning phenotypic assays to cutting-edge whole-genome and metagenomic techniques. Present surveillance frameworks disproportionately focus on human clinical isolates, with environmental and animal samples significantly underrepresented, limiting holistic understanding of AMR ecology. Innovative digital One Health platforms offer promising avenues to harmonize data collection, sharing, and interpretation across these domains, enabling more comprehensive, real-time monitoring.
Furthermore, scientific transparency is paramount to advancing AMR modeling and policy translation. Ensuring open access to data sets, source codes, and model documentation enhances reproducibility, fosters critical peer evaluation, and accelerates innovation. It builds trust among stakeholders, including governments, researchers, and the public, facilitating uptake of evidence-based policies. The research team urges publishers, funding agencies, and institutions to mandate such transparency standards as integral components of AMR research dissemination.
In conclusion, the persistent invisibility and complexity of the AMR pandemic significantly impair its prioritization within global health agendas. Current modeling efforts are nascent and fragmented, failing to provide compelling evidence to galvanize political will and coordinated action. The study contends that only through a concerted, transdisciplinary, and multinational endeavor to develop integrated, empirically validated, and economically informed models can the AMR crisis be effectively managed. This strategic shift is essential to safeguard planetary health and secure the efficacy of antimicrobial therapies for future generations.
Subject of Research: One Health antimicrobial resistance modeling and its translation from scientific research into effective policy frameworks.
Article Title: One Health antimicrobial resistance modelling: from science to policy
News Publication Date: 25-Feb-2026
Web References: http://dx.doi.org/10.1016/j.soh.2026.100146
Image Credits: Carys J. Redman-White, Gwen Knight, Cristina Lanzas, Rodolphe Mader, Bram van Bunnik, Fernando O. Mardones, Adrian Muwonge, Guillaume Lhermie, Andrew R. Peters, Dominic Moran.
Keywords: Antimicrobial resistance, AMR modeling, One Health, mathematical modeling, policy translation, transdisciplinary collaboration, environmental health, economic evaluation, surveillance, integrated modeling architecture.
