Epidemiological and evolutionary consequences of coinfection: a multi-scale modelling approach

Co-infections of multiple pathogens, such as HIV and tuberculosis, are a huge healthcare burden, worsening outcomes for patients and generating epidemics that fuel each other. Co-infections with multiple variants of the same pathogen can also be problematic: co-existence of drug-susceptible and drug-resistant strains can lead to treatment failure and cause resistance to spread among individuals.

The explosion in the amount of genetic data being generated has significantly improved our understanding of the complex processes occurring during co-infection. However, implications for population-level spread, and for the predicted impact of different control policies, remain difficult to assess. Specifically, there is a lack of suitably flexible mathematical models. I will develop novel modelling tools that can capture, in a unified framework, both detailed within-host processes and realistic features of epidemic spread. I will use this approach to study the spread of antimicrobial resistance; the evolution and spread of HIV; and the co-circulation of multiple pathogens. Importantly, I will assess the impact that co-infection has on predicted outcomes of different interventions.

This project will address one of the major modelling challenges that critically limits the predictive power of current models in epidemiology and evolutionary biology.