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

Author
Knight, Gwenan M.
Davies, Nicholas G.
Colijn, Caroline
Coll, Francesc
Donker, Tjibbe
Gifford, Danna R.
Glover, Rebecca E.
Jit, Mark
Klemm, Elizabeth
Lehtinen, Sonja
Lindsay, Jodi A.
Llewelyn, Martin J.
Mateus, Ana L. P.
Robotham, Julie V.
Sharland, Mike
Stekel, Dov
Yakob, Laith
Atkins, Katherine E.
Published Version
https://doi.org/10.1186/s12879-019-4630-yMetadata
Show full item recordCitation
Knight, G.M., Davies, N.G., Colijn, C. et al. Mathematical modelling for antibiotic resistance control policy: do we know enough?. BMC Infect Dis 19, 1011 (2019). https://doi.org/10.1186/s12879-019-4630-yAbstract
BackgroundAntibiotics remain the cornerstone of modern medicine. Yet there exists an inherent dilemma in their use: we are able to prevent harm by administering antibiotic treatment as necessary to both humans and animals, but we must be mindful of limiting the spread of resistance and safeguarding the efficacy of antibiotics for current and future generations. Policies that strike the right balance must be informed by a transparent rationale that relies on a robust evidence base.
Main text
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.
Conclusions
We suggest that in spite of promising advances, we lack a complete understanding of key principles. From this we advocate for priority areas of future empirical and theoretical research.
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https://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37365595
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