Mathematical modelling of the host response to inhalational anthrax across different scales

Inhalational anthrax, caused by the bacterium Bacillus anthracis, is a disease with very high fatality rates. Due to the significant risk posed if the bacterium was to be intentionally used as a bioweapon, it is important to be able to defend against such an attack and to make optimal decisions about treatment strategies. Mechanistic mathematical models can help to quantify and improve understanding of the underlying mechanisms of the infection. In this talk, I will present a multi-scale mathematical model for the infection dynamics of inhalational anthrax. This approach involves constructing individual models for the intracellular, within-host, and population-level infection dynamics, to define key quantities characterising infection at each level, which can be used to link dynamics across scales. I will begin by introducing a model for the intracellular infection dynamics of B. anthracis, which describes the interaction between B. anthracis spores and host cells. The model can be used to predict the distribution of outcomes from this host-pathogen interaction. For example, it can be used to estimate the number of bacteria released upon rupture of an infected phagocyte, as well as the timing of phagocyte rupture and bacterial release. Next, I will show how these key outputs can be used to connect the intracellular model to a model of the infection at the within-host scale. The within-host model aims to provide an overall understanding of the early progression of the infection, and is parametrised with infection data from studies of rabbits and guinea pigs. Furthermore, this model allows the probability of infection and the time to infection to be calculated. Building a model that offers a realistic mechanistic description of these different animal responses to the inhalation of B. anthracis spores is an important step towards eventually extrapolating the model to describe the dynamics of human infection. This would enable predictions of how many individuals would become infected in different exposure scenarios and also on what timescale this would occur.