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

BMC Infect Dis. 2019 Nov 29;19(1):1011. doi: 10.1186/s12879-019-4630-y.


Background: Antibiotics 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.

Keywords: Antibiotic resistance (ABR); Antimicrobial resistance (AMR); Decision-making; Dynamic modelling.

MeSH terms

  • Anti-Bacterial Agents / pharmacology
  • Decision Making
  • Drug Resistance, Microbial / drug effects
  • Health Policy*
  • Humans
  • Models, Theoretical*


  • Anti-Bacterial Agents