A general understanding of the factors driving Group A streptococcus (GAS) epidemiology remains elusive. In part, this is due to the complexity of the pathogen itself, with fundamental principles of transmission, carriage and immunity still not well defined. In high-burden settings such as remote Australian indigenous communities, this lack of understanding is compounded by limited knowledge about host population characteristics that facilitate transmission.
Mathematical models of infectious diseases can help to elucidate drivers of transmission and infection by providing a framework within which to incorporate existing data from observational and intervention studies. Models then enable us to extrapolate from current epidemiological information to inform thinking about effective interventions to control transmission.
In this presentation I will briefly introduce the concept of mathematical modelling, and provide an overview of modelling research being undertaken as part of project seeking to improve our understanding of GAS transmission in remote communities. The prevalence of GAS-associated skin sores in these communities is extremely high and, despite successful community-based interventions over recent decades, sustained control has not been achieved. We are using models to address a number of knowledge gaps in this area, including the role played by scabies as a driver of GAS infection, the effect of GAS carriage for epidemiological dynamics, and how household, community and regional mobility can increase reinfection risk and undermine control efforts.