ArticleScientific reports2019
A dose response model for quantifying the infection risk of antibiotic-resistant bacteria.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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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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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.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Quantifying conjugation rates in clinical and environmental matrices: a systematic review to inform risk assessment.Frontiers in microbiomes · 2024Pooled it
- Quantifying Vibrio Infection Risks From Beach Recreation Along U.S. Eastern Seaboard in Year 2100.GeoHealth · 2026Article
- Electrochemical Immunodetection ofSensors (Basel, Switzerland) · 2025Article
- Towards the integration of antibiotic resistance gene mobility into environmental surveillance and risk assessment.npj antimicrobials and resistance · 2025Review
- Population Ecology-Quantitative Microbial Risk Assessment (QMRA) Model for Antibiotic-Resistant and SusceptibleEnvironmental science & technology · 2025Article
- Quantitative Microbial Risk Assessment of Antibiotic-ResistantEnvironmental science & technology · 2024Article
- A Meta-Analysis to Estimate Prevalence of Resistance to Tetracyclines and Third Generation Cephalosporins in Enterobacteriaceae Isolated from Food Crops.Antibiotics (Basel, Switzerland) · 2022Article
- A dose response model for Staphylococcus aureus.Scientific reports · 2021Article
- Metagenomics as a Tool To Monitor Reclaimed-Water Quality.Applied and environmental microbiology · 2020Review
- Metabolic Current Production by an Oral Biofilm PathogenMolecules (Basel, Switzerland) · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Quantifying the human health risk of microbial infection helps inform regulatory policies concerning pathogens, and the associated public health measures. Estimating the infection risk requires knowledge of the probability of a person being infected by a given quantity of pathogens, and this relationship is modeled using pathogen specific dose response models (DRMs). However, risk quantification for antibiotic-resistant bacteria (ARB) has been hindered by the absence of suitable DRMs for ARB. A new approach to DRMs is introduced to capture ARB and antibiotic-susceptible bacteria (ASB) dynamics as a stochastic simple death (SD) process. By bridging SD with data from bench experiments, we demonstrate methods to (1) account for the effect of antibiotic concentrations and horizontal gene transfer on risk; (2) compute total risk for samples containing multiple bacterial types (e.g., ASB, ARB); and (3) predict if illness is treatable with antibiotics. We present a case study of exposure to a mixed population of Gentamicin-susceptible and resistant Escherichia coli and predict the health outcomes for varying Gentamicin concentrations. Thus, this research establishes a new framework to quantify the risk posed by ARB and antibiotics.
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Registered trials
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