ArticleeLife2015
How competition governs whether moderate or aggressive treatment minimizes antibiotic resistance.
Article in eLife, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
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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.
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Who cites it
29 citing papers in PubMed.
- Mapping the landscape of mathematical models for antimicrobial resistance: a scoping review.BMC public health · 2026Article
- Intracellular interactions shape antiviral resistance outcomes in poliovirus via eco-evolutionary feedback.Nature ecology & evolution · 2026Article
- Article
- Intracellular interactions shape antiviral resistance outcomes in poliovirus via eco-evolutionary feedback.bioRxiv : the preprint server for biology · 2025Article
- Hydrazine Derivative-Based Carbon Dots for Potent Antibacterial Activity Against Multidrug-Resistant Bacterial.Nanomaterials (Basel, Switzerland) · 2025Article
- A survey of open questions in adaptive therapy: Bridging mathematics and clinical translation.eLife · 2023Article
- The risk of drug resistance during long-acting antimicrobial therapy.Proceedings. Biological sciences · 2022Article
- Article
- Dosage strategies for delaying resistance emergence in heterogeneous tumors.FEBS open bio · 2021Article
- Within-host bacterial growth dynamics with both mutation and horizontal gene transfer.Journal of mathematical biology · 2021Article
- Article
- Drug-target binding quantitatively predicts optimal antibiotic dose levels in quinolones.PLoS computational biology · 2020Article
- Cross-feeding modulates the rate and mechanism of antibiotic resistance evolution in a model microbial community of Escherichia coli and Salmonella enterica.PLoS pathogens · 2020Article
- Antibiotics can be used to contain drug-resistant bacteria by maintaining sufficiently large sensitive populations.PLoS biology · 2020Article
- On the probability of strain invasion in endemic settings: Accounting for individual heterogeneity and control in multi-strain dynamics.Journal of theoretical biology · 2020Article
- Treatment timing shifts the benefits of short and long antibiotic treatment over infection.Evolution, medicine, and public health · 2020Article
- Mathematical modelling for antibiotic resistance control policy: do we know enough?BMC infectious diseases · 2019Article
- Aggressive or moderate drug therapy for infectious diseases? Trade-offs between different treatment goals at the individual and population levels.PLoS computational biology · 2019Article
- Suppression of bacteriocin resistance using live, heterospecific competitors.Evolutionary applications · 2019Article
- Observational
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Understanding how our use of antimicrobial drugs shapes future levels of drug resistance is crucial. Recently, there has been debate over whether an aggressive (i.e., high dose) or more moderate (i.e., lower dose) treatment of individuals will most limit the emergence and spread of resistant bacteria. In this study, we demonstrate how one can understand and resolve these apparently contradictory conclusions. We show that a key determinant of which treatment strategy will perform best at the individual level is the extent of effective competition between resistant and sensitive pathogens within a host. We extend our analysis to the community level, exploring the spectrum between strict inter-strain competition and strain independence. From this perspective as well, we find that the magnitude of effective competition between resistant and sensitive strains determines whether an aggressive approach or moderate approach minimizes the burden of resistance in the population.
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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.