ArticleEvolutionary applications2019
Evolutionary epidemiology models to predict the dynamics of antibiotic resistance.
Article in Evolutionary applications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed, 90 citations in OpenAlex.
- Structural transition in antimicrobial resistance dynamics ofInfectious Disease Modelling · 2027Article
- Reimagining antimicrobial resistance: AI-driven predictive epidemiology and the C-AMRE framework for next-generation antibiotic discovery.The Journal of antibiotics · 2026Review
- Modelling the role of the microbiome in antimicrobial resistance across scales.Nature microbiology · 2026Review
- Evolutionary inference reveals global natural histories and predicted pathways of antimicrobial resistance in Klebsiella pneumoniae.PLoS biology · 2026Article
- From Lake Victoria to the Tap: Antibiotic Resistance and Pathogenic Contamination of Kisumu City Water Supply and Wastewater Network.Tropical medicine & international health : TM & IH · 2026Article
- Article
- Quantifying the real-world impact of antibiotic use and genetic determinants of resistance on gonococcal dynamics.Nature microbiology · 2026Article
- Article
- Impact of COVID-19 isolation measures on ICU microbial resistance dynamics: simulation-based statistical modeling analysis.Antimicrobial resistance and infection control · 2025Article
- Advancements in AI-driven drug sensitivity testing research.Frontiers in cellular and infection microbiology · 2025Review
- From Petri Dishes to Patients to Populations: Scales and Evolutionary Mechanisms Driving Antibiotic Resistance.Annual review of microbiology · 2024Review
- Recent approaches in computational modelling for controlling pathogen threats.Life science alliance · 2024Review
- Article
- Pneumococcal population dynamics: Investigating vaccine-induced changes through multiscale modelling.PLoS computational biology · 2023Article
- Linking within- and between-host scales for understanding the evolutionary dynamics of quantitative antimicrobial resistance.Journal of mathematical biology · 2023Article
- The uncertain role of substandard and falsified medicines in the emergence and spread of antimicrobial resistance.Nature communications · 2023Review
- Modelling antimicrobial resistance transmission to guide personalized antimicrobial stewardship interventions and infection control policies in healthcare setting: a pilot study.Scientific reports · 2023Review
- Recovery of Staphylococci from Teatcups in Milking Parlours in Goat Herds in Greece: Prevalence, Identification, Biofilm Formation, Patterns of Antibiotic Susceptibility, Predictors for Isolation.Antibiotics (Basel, Switzerland) · 2023Article
- Quantitative systems-based prediction of antimicrobial resistance evolution.NPJ systems biology and applications · 2023Review
- A stochastic analysis of the interplay between antibiotic dose, mode of action, and bacterial competition in the evolution of antibiotic resistance.PLoS computational biology · 2023Article
Corrections and comments
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Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The evolution of resistance to antibiotics is a major public health problem and an example of rapid adaptation under natural selection by antibiotics. The dynamics of antibiotic resistance within and between hosts can be understood in the light of mathematical models that describe the epidemiology and evolution of the bacterial population. "Between-host" models describe the spread of resistance in the host community, and in more specific settings such as hospitalized hosts (treated by antibiotics at a high rate), or farm animals. These models make predictions on the best strategies to limit the spread of resistance, such as reducing transmission or adapting the prescription of several antibiotics. Models can be fitted to epidemiological data in the context of intensive care units or hospitals to predict the impact of interventions on resistance. It has proven harder to explain the dynamics of resistance in the community at large, in particular because models often do not reproduce the observed coexistence of drug-sensitive and drug-resistant strains. "Within-host" models describe the evolution of resistance within the treated host. They show that the risk of resistance emergence is maximal at an intermediate antibiotic dose, and some models successfully explain experimental data. New models that include the complex host population structure, the interaction between resistance-determining loci and other loci, or integrating the within- and between-host levels will allow better interpretation of epidemiological and genomic data from common pathogens and better prediction of the evolution of resistance.
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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.