Modelling the impact of curtailing antibiotic usage in food animals on antibiotic resistance in humans
Abstract
1.Consumption of antibiotics in food animals is increasing worldwide and is approaching, if not already surpassing, the volume consumed by humans. It is often suggested that reducing the volume of antibiotics consumed by food animals could have a public health benefits. Although this notion is widely regarded as intuitively obvious there is a lack of robust, quantitative evidence to either support or contradict the suggestion.\n\nAs a first step towards addressing this knowledge gap, we develop a simple mathematical model for exploring the generic relationship between antibiotic consumption by food animals and levels of resistant bacterial infections in humans. We investigate the impact of restricting antibiotic consumption by animals and identify which model parameters most strongly determine that impact.\n\nOur results suggest that, for a wide range of scenarios, curtailing the volume of antibiotics consumed by food animals has, as a stand-alone measure, little impact on the level of resistance in humans. We also find that reducing the rate of transmission of resistance from animals to humans may be more effective than an equivalent reduction in the consumption of antibiotics in food animals. Moreover, the response to any intervention is strongly determined by the rate of transmission from humans to animals, an aspect which is rarely considered.
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Claude Opus 5.5 · xhigh effort · 5 Nov 2026 · from the full text of version 3 (19 Dec 2016) · CC BY-NC-ND 4.0
A two-population mathematical model indicates that curtailing antibiotic use in food animals alone has little effect on human antibiotic resistance unless cross-species transmission is also reduced.
Although agricultural antibiotic consumption rivals or exceeds human usage globally, quantitative evidence on how restricting farm antibiotic use affects human resistance prevalence has been lacking. Modeling coupled human-animal resistance dynamics identifies which transmission and exposure rates govern intervention outcomes.
Key results
ShownEquilibrium human antibiotic resistance prevalence is governed primarily by human clearance, human antibiotic exposure, and animal-to-human transmission rather than animal-side parameters.
Global extended Fourier amplitude sensitivity test (FAST) analysis over parameter values from 0.0 to 1.0 showed that steady-state human resistance (RH*) is most sensitive to human reversion rate (μH), human antibiotic acquisition (ΛH), animal-to-human transmission (βAH), and human-to-human transmission (βHH), and minimally sensitive to βAA, βHA, ΛA, and μA. · (c) Sensitivity analysis; 3 Results; Figure legends
ShownReducing animal-to-human resistance transmission lowers human resistance more effectively than an equivalent reduction in animal antibiotic consumption, though with non-linear returns.
Across parameter space from 0.0 to 1.0, equilibrium human resistance (RH*) changed little with animal antibiotic acquisition rate (ΛA) compared with animal-to-human transmission (βAH), and the non-linear response to βAH limited the benefit of partial reductions when RH* was already high. · Abstract; 3 Results; Figure legends
ShownHigh transmission of resistant bacteria from humans to food animals severely blunts the impact of eliminating agricultural antibiotic use.
When comparing complete curtailment of animal antibiotic use (ΛA = 0) between a low-impact scenario (βHA = 0.1, stabilizing at RH = 0.71 and RA = 0.71) and a high-impact scenario (βHA = 0.001, stabilizing at RH = 0.70 and RA = 0.62), lowering ΛA had virtually no effect on human resistance when βHA was 0.1. · (a) Mathematical model; (b) Scenarios; 3 Results; Figure legends
ShownEliminating antibiotic-driven resistance acquisition in humans alone cannot eradicate human resistance when agricultural reservoirs remain coupled.
Setting direct human resistance acquisition from antibiotic exposure to zero (ΛH = 0) reduced long-term human resistance prevalence from approximately 0.70-0.71 only down to 0.56 in the low-impact scenario and 0.54 in the high-impact scenario. · (b) Scenarios; 3 Results; 4. Discussion
Methods, data and reproducibility
Formulated a coupled two-equation ODE model tracking fractions of humans (RH) and food animals (RA) carrying resistant bacteria under antibiotic selection (ΛH, ΛA), transmission (βHH, βAA, βAH, βHA), and clearance (μH, μA). Solved for equilibrium human prevalence (RH*) and relative impact (ω) of ΛA = 0 in Mathematica 10.3, and ran eFAST sensitivity analysis in R for low (βHA = 0.1) and high (βHA = 0.001) cases.
- Methods & tools
- Wolfram Mathematica (version 10.3), R, extended Fourier amplitude sensitivity test (FAST)
- Reagents & environment
- Wolfram Mathematica version 10.3 for equilibrium analytical solutions and R for extended FAST global sensitivity analysis.
- Reproducibility gaps
- No Mathematica notebooks or R scripts used to generate the equilibrium surfaces and sensitivity indices are provided.
Limitations (4)
- The two-compartment model omits spatial structure, contact networks, demographic differences (such as poultry batch structure), and dynamics specific to individual drug-bug-animal combinations. · stated by the authors
- The functional relationship between antibiotic consumption volume and the animal resistance acquisition rate (ΛA) is left undefined, restricting formal impact analysis to complete elimination (ΛA = 0). · stated by the authors
- Transmission parameters aggregate clonal bacterial spread with horizontal mobile genetic element transfer, and clearance parameters combine infection recovery with host demographic replacement. · stated by the authors
- Independent empirical estimates for all eight model parameters are not available for any specific real-world setting, so baseline scenarios are anchored only to an approximate 70% prevalence. · stated by the authors
Who should read:Read this if you model antimicrobial resistance transmission or evaluate One Health interventions linking agricultural antibiotic use and human infection prevalence.
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