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How can mathematical modelling help prevent drug-resistant infections? 

A spotlight by Gwen Knight, Co-Director of the AMR Centre. 
Graphic showing how we brought together interdisciplinary AMR modelling research fields that use mathematical modelling as a tool to answer important questions about AMR.

Mathematical models of infectious diseases are increasingly being used to understand dynamics, quantify the impact of interventions, and predict the disease burden. In the antimicrobial resistance (AMR) field, there is great potential for the logical language of mathematics to translate and investigate the complex dynamics of AMR that we researchers hold in our heads. However, there are huge barriers to this in terms of a lack of knowledge and data. 

Last year, we ran a second workshop about mathematical modelling of AMR across four different themes: transmission, within-host, interventions and policy, with the idea that models need to coalesce around the central theme of how to prevent drug-resistant infections in humans. The summary infographics from this workshop can now be found via the links below: 

These give a broad overview of the featured work in the four themes and raise some important questions for consideration as well as providing a showcase of work that can be done in this area. A recording of the workshop is also available on the website.

The take homes from the workshop (RESIST2) were mixed: we have a range of brilliant modellers working in the AMR space but are still struggling with some common themes of parameter and structural uncertainty that remain the same as our first workshop in 2019. To see the thoughts raised from that first workshop check out our publication here, where we discuss if we know enough for modelling to really inform AMR policy. A big question that arose multiple times, and that I’m struggling to grapple with is: can we build models of AMR evolution without huge multi-site sampling studies of many patients over long periods of time? Or must we be much more pragmatic as modellers and adapt to build frameworks that account for sampling and uncertainty in the data we have?

If you’re interested in modelling, please watch this space for our next workshop (RESIST3) that will likely be at the end of this year or the start of 2024!

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