"Antibiotic Resistance Modelling to Inform Decision-Making: Do We Know Enough?"

A one day workshop to explore the above question by bringing together mathematical modellers with the wider public health community. This is supported by the Centre for Mathematical Modelling and Antimicrobial Resistance Centre at the London School of Hygiene and Tropical Medicine.

Antibiotic resistance is one of the major challenges currently facing global health. Mathematical modelling can be used to better understand the spread of antibiotic resistance (ABR) from the underlying evolutionary perspective to the likely clinical impact of interventions. In this one-day workshop we will bring together international researchers, clinicians and policymakers to discuss how mathematical models can inform how best to intervene and resist the spread of ABR.

To answer our overarching question, we have split the programme into four sessions, each with it's own question:

(1) Policy: How could modelling outputs inform policy?

(2) ABR evolution: How do we quantify the link between ABR and antibiotic use?

(3) ABR transmission: How should interventions target ABR transmission?

(4) Novel tools and strategies: How can they be implemented effectively?

To start with we will discuss the overarching outcome - what do policy makers need from mathematical models? what data is missing to inform policy? what are the key policy decisions to resist ABR that require the quantitative insight that models can give?

In the second session we turn to evolution. Here, we want to know if we can quantify the link between antibiotic use and ABR. Our speakers here are experts at different levels of evolution - is it possible to see signals of the link at each level and what would/does that signal look like? How can we integrate these different pieces of evidence to quantify the link? Do we know enough about this link to be able to resist ABR?

Our third session focuses on the transmission and spread of ABR. Are there modelling structures that are better for ABR than others? what does the data say on where ABR is coming from? How can we use models to better understand and resist ABR?

We will end the talks by considering the future ways to resist ABR - how will new genomic data better inform models for policy? What can models say about how vaccines will impact ABR burden?

The final session will be an open discussion forum, with potential for rapid-fire one-slide input from attendees.