bioRxivMicrobiology
Drivers of ESBL-producing Escherichia coli dynamics in calf fattening farms: a modelling study
J. Bastard, M. Haenni, E. Gay, P. Glaser, J.-Y. Madec, L. Temime, L. Opatowski
Abstract
The contribution of bacteria in livestock to the global burden of antimicrobial resistance raises concerns worldwide. However, the dynamics of selection and diffusion of antimicrobial resistance in farm animals are not fully understood. Here, we used veal calf fattening farms as a model system, as they are a known reservoir of Extended Spectrum {beta}-Lactamase-producing Escherichia coli (ESBL-EC). Longitudinal data of ESBL-EC carriage and antimicrobial use (AMU) were collected from three veal calf farms during the entire fattening process. We developed 18 agent-based mechanistic models to assess different hypotheses regarding the main drivers of ESBL-EC dynamics in calves. The models were independently fitted to the longitudinal data using Markov Chain Monte Carlo and the best model was selected. Within-farm transmission between individuals and sporadic events of contamination were found to drive ESBL-EC dynamics on farms. In the absence of AMU, the median carriage duration of ESBL-EC was estimated to be 19.6 days (95% credible interval: [12.7; 33.3]). In the best model, AMU was found to influence ESBL-EC dynamics, by affecting ESBL-EC clearance rather than acquisition. This effect of AMU was estimated to decrease gradually after the end of exposure and to disappear after 62.5 days [50.0; 76.9]. Moreover, using a simulation study, we quantified the efficacy of ESBL-EC mitigation strategies. Decreasing ESBL-EC prevalence by 50% on arrival at the fattening farm reduced prevalence at slaughter age by 33.3%. Completely eliminating the use of selective antibiotics had a strong effect on average ESBL-EC prevalence (relative reduction of 79.6%), but the effect was mild if this use was only decreased by 50% compared to baseline (relative reduction of 3.7%).
- organism: E. coli
- assay: metagenomics
- research focus: host-pathogen & immune response
- approach: statistical model
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