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R code: why sensitive bacteria are resistant to hospital infection control (v1)

Authors: Esther van Kleef; Nantasit Luangasanatip; Marc JM Bonten; Ben S Cooper;

R code: why sensitive bacteria are resistant to hospital infection control (v1)

Abstract

This model simulates the transmission dynamics of two competing strains, i.e. a hospital-adapted resistant strain and a community-adapted sensitive strain, in the hospital and its catchment area. It has been argued that since infection control measures, such as hand hygiene, should affect resistant and sensitive strains equally, observed discordant changes must have largely resulted from other factors, such as changes in antibiotic use. This model is used to test the validity of this reasoning. model_functions.R contains all functions including the model, plotting functions and functions to calculate R0. run_model.R contains the code to run the model and generate output. Parameters listed are those at baseline. plot_output.R contains the code to plot the model output. run_model_cdi.R contains the code to run the model and generate output for the Lancet ID correspondence to Dingle et al 2017 (DOI: http://dx.doi.org/10.1016/S1473-3099(16)30514-X). Parameters listed are those used to generate the plot. plot_model_cdi.R contains the code to generate the figure in the Lancet ID correspondence.

This research has received funding (EvK, BSC) from the European Community's Seventh Framework Programme FP7/2007-2013 under agreement no. 282512. BSC was also supported by The Medical Research Council and Department for International Development (grant number MR/K006924/1). This study was part of the Wellcome-Trust Major Overseas Programme in SE Asia (grant number 106698/Z/14/Z).

{"references": ["Van Kleef E, Luangasanatip, N, Bonten MJM, Cooper BS. Why susceptible bacteria are resistant to hospital infection control. Wellcome Open Res 2017"]}

Keywords

Mathematical model, Antimicrobial Resistance, Hospital infection control

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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