Agent-based modelling of AMR
The emergence and transmission of antibiotic resistance bacteria is a complex real-world process that we aim to untangle through computer modelling. This modelling will use the outputs of the other workstreams to understand how the multiple factors interact and influence the development of antibiotic resistance.
Likelihood of acquiring antibiotic resistance bacteria related to many factors
The way in which antibiotic resistance bacteria emerge in the community and are transferred between humans, animals, and the environment is a complex game of chance.
At an individual level, the chance of a person acquiring drug resistant bacteria is related to how:
- They interact with their community, animals, and their environment
- Antibiotic resistance bacteria are distributed in those three sources.
Disease patterns can be obscured by chance events
In previous studies there is compelling evidence for how:
- Social networks spread influenza among schoolchildren
- Water flow within river catchment areas can describe community patterns of bacterial disease.
- Exposure to sub-optimal antibiotic therapy actively promotes the evolution of antibiotic resistance bacteria in host (human and animal) populations.
However, these patterns are often obscured in the wider community by chance events of the acquisition process and continual mutation of bacterial genomes.
Computer modelling to explore impact of chance events on antibiotic resistance susceptibility
Computer modelling of pathogen transmission within the community provides a means to untangle the real-world complexity to uncover the general principles of what makes some people more susceptible to antibiotic resistance than others.
This “agent-based” modelling approach will allow us to “caricature” individuals in terms of physical characteristics, social interaction, and behaviour (especially health-seeking) relevant to antibiotic resistance acquisition and loss.
Once constructed, we will use our in-silico virtual community to explore the efficacy of community-level responses to health interventions.