Mathematical modelling for antibiotic resistance control policy: do we know enough?

  10 December 2019

One way to generate the evidence base needed to inform policies for managing antibiotic resistance is by using mathematical models. These models can distil the key drivers of the dynamics of resistance transmission from complex infection and evolutionary processes, as well as predict likely responses to policy change in silico. Here, we ask whether we know enough about antibiotic resistance for mathematical modelling to robustly and effectively inform policy. We consider in turn the challenges associated with capturing antibiotic resistance evolution using mathematical models, and with translating mathematical modelling evidence into policy.


Further reading: BMC Infectious Diseases
Author(s): Gwenan M. Knight , Nicholas G. Davies, Caroline Colijn, Francesc Coll, Tjibbe Donker, Danna R. Gifford, Rebecca E. Glover, Mark Jit, Elizabeth Klemm, Sonja Lehtinen, Jodi A. Lindsay, Marc Lipsitch, Martin J. Llewelyn, Ana L. P. Mateus, Julie V. Robotham, Mike Sharland, Dov Stekel, Laith Yakob & Katherine E. Atkins
Smart Innovations  


Unrestricted financial support by:


Antimicrobial Resistance Fighter Coalition

Bangalore Bioinnovation Centre


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