Evidence map›Paper›PMID 41674858›Full record

ReviewScience in One Health2026

One Health antimicrobial resistance modelling: from science to policy.

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

Abstract readReview
In one paragraph

Review in Science in One Health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Carys J Redman-WhiteGlobal Agriculture and Food Systems, The Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Campus, Roslin, EH25 9RG, United Kingdom.
Gwen KnightAntimicrobial Resistance Centre, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, United Kingdom.
Cristina LanzasDepartment of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, CVM Terry Center, Raleigh, NC, 27606, United States.
Rodolphe MaderInternational Centre for Antimicrobial Resistance Solutions, 2300, Copenhagen S, Denmark.
Bram van BunnikEpidemiology Economics and Risk Assessment, The Roslin Institute, The Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Campus, Roslin, EH25 9RG, United Kingdom.
Fernando O MardonesGlobal Agriculture and Food Systems, The Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Campus, Roslin, EH25 9RG, United Kingdom.
Adrian MuwongeThe Digital One Health Laboratory, The Roslin Institute, The Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Campus, Roslin, EH25 9RG, United Kingdom.
Guillaume LhermieSchool of Public Policy, University of Calgary, Calgary, AB, T2P 1H9, Canada.
Andrew R PetersThe Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Campus, Roslin, EH25 9RG, United Kingdom.
Dominic MoranGlobal Agriculture and Food Systems, The Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Campus, Roslin, EH25 9RG, United Kingdom.

Funding

Analytical pipelines for data and model integration: finding informed pathways for antimicrobial resistance controlR35GM134934 · NIGMS · NORTH CAROLINA STATE UNIVERSITY RALEIGH · PI LANZAS, CRISTINA · 2020 to 2024
$2.1M
NIGMS NIH HHS R35 GM134934
6 · The paper itself

Abstract

Modern human and veterinary medical interventions to combat infectious diseases depend on the continued efficacy of antimicrobial drugs. Antimicrobial resistance (AMR) is the quintessential One Health challenge threatening human and animal health and welfare and has environmental effects on ecological communities in soil and water. Policy guidance on AMR needs to anticipate the likely outcomes of different interventions and courses of action. For that, transdisciplinary collaboration to understand the development, spread, and impacts of AMR is crucial. We report the outcomes of an international workshop that explored the challenges and opportunities for modelling AMR across One Health settings. They include the disparity of data quality and availability, the broader knowledge gaps in key areas such as the relationship between antimicrobial use (AMU) and AMR, and the difficulty of defining AMR as a single outcome given its heterogeneity. Differences between microbial species, resistance genes, environments (i.e., terrestrial vs. aquatic) and practical settings (e.g., human clinical vs. veterinary, or individual vs. population) complicate the generalizability of model applications. However, synoptic AMR metrics are necessary to cut through the complexity for policymaking. We discuss the status of AMR modelling with respect to a hierarchy of modelling evidence for decision-making. Finally, we consider learnings from modelling other wicked environmental challenges to develop a pragmatic approach to inform policy.

Indexed as

Antimicrobial resistanceModelsOne HealthPolicy

Identifiers

PMID41674858
PMCPMC12887169

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.