SynthesisBMC medicine2019
Population-level mathematical modeling of antimicrobial resistance: a systematic review.
Synthesis in BMC medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 4 of them syntheses that pooled it.
What it found
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Who cites it
40 citing papers in PubMed, 4 syntheses or guidelines pooled it, 96 citations in OpenAlex.
- The neglected model validation of antimicrobial resistance transmission models - a systematic review.Antimicrobial resistance and infection control · 2025Pooled it
- A systematic review to identify research gaps in studies modeling MenB vaccinations against Neisseria infections.PloS one · 2025Pooled it
- Mathematical models of drug-resistant tuberculosis lack bacterial heterogeneity: A systematic review.PLoS pathogens · 2024Pooled it
- Costs-effectiveness and cost components of pharmaceutical and non-pharmaceutical interventions affecting antibiotic resistance outcomes in hospital patients: a systematic literature review.BMJ global health · 2024Pooled it
- Mapping the landscape of mathematical models for antimicrobial resistance: a scoping review.BMC public health · 2026Article
- Modelling the role of the microbiome in antimicrobial resistance across scales.Nature microbiology · 2026Review
- Interventions Targeting the Beef Feedlot Environment to Control Antimicrobial Resistance: A Mathematical Modelling Study.Zoonoses and public health · 2026Article
- Optimal Multi-Drug Therapies for Antimicrobial Resistance with Horizontal Transfer.Journal of optimization theory and applications · 2026Article
- Article
- One Health antimicrobial resistance modelling: from science to policy.Science in One Health · 2026Review
- Predictive modelling of the dynamics of antimicrobial resistance: creation of a bank of renewable models based on machine learning.Frontiers in pharmacology · 2026Article
- A Theoretical Framework to Quantify the Tradeoff Between Individual and Population Benefits of Expanded Antibiotic Use.Bulletin of mathematical biology · 2025Article
- Cost-effectiveness of screening, decolonisation and isolation strategies for carbapenem-resistant Enterobacterales and methicillin-resistantLancet regional health. Americas · 2025Article
- Poor hypotheses and research waste in biology: learning from a theory crisis in psychology.BMC biology · 2025Review
- Modelling the implementation of narrow versus broader spectrum antibiotics in the empiric treatment of E. coli bacteraemia.Scientific reports · 2024Article
- Antimicrobial resistance in food-borne pathogens at the human-animal interface: Results from a large surveillance study in India.One health (Amsterdam, Netherlands) · 2024Article
- Policy, practice, and prediction: model-based approaches to evaluating N. gonorrhoeae antibiotic susceptibility test uptake in Australia.BMC infectious diseases · 2024Article
- Unveiling microbial dynamics: a review of health and immune enhancement in school settings.Frontiers in microbiomes · 2024Review
- Bayesian Calibration to Address the Challenge of Antimicrobial Resistance: A Review.IEEE access : practical innovations, open solutions · 2024Article
- Analyses of Extended-Spectrum-β-Lactamase, Metallo-β-Lactamase, and AmpC-β-Lactamase Producing Enterobacteriaceae from the Dairy Value Chain in India.Antibiotics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundMathematical transmission models are increasingly used to guide public health interventions for infectious diseases, particularly in the context of emerging pathogens; however, the contribution of modeling to the growing issue of antimicrobial resistance (AMR) remains unclear. Here, we systematically evaluate publications on population-level transmission models of AMR over a recent period (2006-2016) to gauge the state of research and identify gaps warranting further work.
methodsWe performed a systematic literature search of relevant databases to identify transmission studies of AMR in viral, bacterial, and parasitic disease systems. We analyzed the temporal, geographic, and subject matter trends, described the predominant medical and behavioral interventions studied, and identified central findings relating to key pathogens.
resultsWe identified 273 modeling studies; the majority of which (> 70%) focused on 5 infectious diseases (human immunodeficiency virus (HIV), influenza virus, Plasmodium falciparum (malaria), Mycobacterium tuberculosis (TB), and methicillin-resistant Staphylococcus aureus (MRSA)). AMR studies of influenza and nosocomial pathogens were mainly set in industrialized nations, while HIV, TB, and malaria studies were heavily skewed towards developing countries. The majority of articles focused on AMR exclusively in humans (89%), either in community (58%) or healthcare (27%) settings. Model systems were largely compartmental (76%) and deterministic (66%). Only 43% of models were calibrated against epidemiological data, and few were validated against out-of-sample datasets (14%). The interventions considered were primarily the impact of different drug regimens, hygiene and infection control measures, screening, and diagnostics, while few studies addressed de novo resistance, vaccination strategies, economic, or behavioral changes to reduce antibiotic use in humans and animals.
conclusionsThe AMR modeling literature concentrates on disease systems where resistance has been long-established, while few studies pro-actively address recent rise in resistance in new pathogens or explore upstream strategies to reduce overall antibiotic consumption. Notable gaps include research on emerging resistance in Enterobacteriaceae and Neisseria gonorrhoeae; AMR transmission at the animal-human interface, particularly in agricultural and veterinary settings; transmission between hospitals and the community; the role of environmental factors in AMR transmission; and the potential of vaccines to combat AMR.
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Registered trials
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.