Modelling antimicrobial resistance for policymaking

Researchers at the University of Edinburgh have collaborated with experts from around the world to set out opportunities and challenges in modelling the ability of bacteria and other microbes to survive treatments aimed at stopping infections, and informing interventions to address the problem and its impacts on humans, animals and the environment.

Experts set out to explore how modelling is being used to understand antimicrobial resistance (AMR), where current approaches fall short, and what is needed to better support decision-making. Its focus was in One Health modelling – which considers the links between people, animals and the environment.

The paper originated from discussions at an Edinburgh-hosted symposium that brought together researchers in human and veterinary medicine, epidemiology, economics and mathematical modelling, with participants from Europe, Africa, North America and South America. The workshop was supported by the OECD Collaborative Research Programme. This symposium and the resulting publication explored current approaches to modelling AMR in order to develop our understanding of the problem and investigate policies to address it.

Key challenges

One of the central issues identified is the diverse nature of AMR. Resistance involves different microbes, drugs and genetic mutations, making it difficult to quantify overall levels of AMR. Another problem stemming from this diversity is that models developed for one combination of an organism, drug and setting often cannot be easily applied to others. Differences between human clinical settings, veterinary systems, agriculture and the wider environment further limit generalisability of current mathematical models.

In addition, data availability and quality remain major constraints. Surveillance of antimicrobial use and resistance is uneven across countries and sectors, with particularly limited data from low- and middle-income settings.

The paper also highlights gaps in the current understanding of the biological mechanisms that link antimicrobial use to the emergence of resistance.

While this link is well established in principle, it is difficult to quantify in practice, limiting the predictive power of models.

Model contributions

AMR models are useful for exploring mechanisms, identifying knowledge gaps, and examining plausible scenarios within specific contexts, the research team explains.

“Greater coordination between modelling groups, including systematic comparison of different models, would strengthen the evidence base,” says Dr Carys Redman-White, who led the research. “Such approaches have been used successfully in other fields, including infectious disease modelling during the Covid-19 pandemic.”

Global parallels

The research also draws lessons from modelling other One Health issues, such as climate change. International collaboration, transparency and shared standards are highlighted as important factors in building trust and ensuring research outputs are useful for informed policymaking.

The paper is published in Science in One Health, in collaboration with colleagues from the London School of Hygiene and Tropical Medicine, North Carolina State University, the International Centre for Antimicrobial Resistance Solutions, Pontificia Universidad Católica de Chile, and the University of Calgary.

Related links

Research publication

Image credit National Institute of Allergy and Infectious Diseases, USA