Connecting theory with data in host-parasite evolution

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My research programme aims to understand ecological communities and disease outbreaks by building mathematical and statistical models to analyze data and connect it with theory. I propose to explore the short-term evolution of parasites (i.e., disease-causing organisms such as viruses, bacteria, or intestinal worms) and their hosts. Over the long term, parasites often evolve to intermediate levels of aggressiveness (host exploitation), compromising between underexploiting their hosts (or being outcompeted by more aggressive parasites) and killing them too quickly; hosts evolve to reduce the damaging effects of parasites either by attacking them (resistance) or learning to live with them (tolerance). Over the short term, however, e.g. during an epidemic, the direction of evolution is often surprising; I will make new models, and use existing data, to predict how parasite exploitation and host defences co-evolve. We must consider resistance, tolerance, and exploitation as interlocking parts of the host-parasite relationship, rather than as separately evolving traits. While biologists have increasingly adopted such a holistic view when measuring host resistance and tolerance, we do not yet have an integrated theoretical framework for the coevolution of these three traits. I will construct mathematical and computational models to predict joint changes in resistance, tolerance, and exploitation during epidemics. Due to data limitations, at present only simple versions of these models can be used in the real world, but developing the general models will help us understand what kinds of data we need to collect in the future in order to understand and predict the evolution of diseases and their hosts. Another recent insight of my research programme is that hosts and diseases can evolve quickly, even as an epidemic spreads in the population: these “eco-evolutionary” models combine ecological and evolutionary processes. I have previously developed simple eco-evolutionary models, but will move forward to build more complex, realistic models that can be applied and tested against data. In particular, I will develop eco-evolutionary models for the evolution of resistance, tolerance and exploitation in HIV (in humans) and in myxomatosis (in rabbits). While modellers have already begun to tackle the eco-evolutionary dynamics of these diseases, their models are missing important components. I will answer the questions: how do social characteristics like partnership duration or use of sex workers affect the rate of evolution of HIV and other sexually transmitted diseases? How do we expect myxomatosis to change over short geographic scales or over the course of a season? The answers to these questions will build our understanding of the ecology and evolution of infectious disease, helping us to predict and manage infectious diseases of wildlife, domestic plants and animals, and humans.