Epidemiological and evolutionary consequences of coinfection: a multi-scale modelling approach (360G-Wellcome-202562_A_16_Z)

Coinfections of multiple pathogen species or variants in an infected host are common and affect how pathogens evolve, drug resistance emerges and co- epidemics unfold. The impact of coinfections on patients’ prognosis is reasonably well understood, and genetic data is quickly increasing our insight in the complex within-host dynamics of coinfection. However, predicting the population level consequences remains problematic, mostly because of a lack of suitable mathematical models that can coherently integrate realistic processes occurring at both within- and between-host scales. I will develop a novel modelling framework that can overcome these limitations, together with the necessary tools to analyse it, allowing us to capture: arbitrarily complex within-host processes; realistic population heterogeneities; and, crucially, superinfection events and their impact on within-host dynamics. Important questions in which coinfection plays a key role include: \- Is switching between moderate and aggressive therapies a better strategy for controlling the spread of antimicrobial resistance than aggressive therapy alone? \- Why has HIV drug resistance not risen to higher levels, and is this likely to change with increased interventions? \- What is the best strategy for controlling co-circulating pathogens such as HIV and malaria? This project will provide the most advanced tools to date to address these urgent questions.