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Host-pathogen systems provide a unique opportunity to synthesize ecological and evolutionary theory with empirical data. This opportunity arises from the availability of extensive temporal and spatial infectious disease records and the relative simplicity of (some) host-pathogen interactions when compared to many other ecological systems. However, like more complicated ecological systems, host-pathogen interactions are fundamentally nonlinear and can therefore give rise to complex dynamics and evolutionary patterns. Many theoretical approaches and empirical insights that arise from a study of infectious diseases can therefore be applicable to understanding ecological and evolutionary processes more generally.

The research in my lab focuses on using mathematical and statistical approaches to understand the ecology and evolution of infectious diseases. More specifically, our interests fall into two main research areas:

* Understanding the interaction between disease dynamics and strain evolution

* Understanding the immunological and environmental factors that drive infectious disease dynamics

Understanding the interaction between disease dynamics and strain evolution

Studies of evolutionary and ecological processes have traditionally been separated in biology. Recently, a number of different studies have shown that evolution frequently acts on the same timescale as a population’s ecological dynamics, and that these evolutionary dynamics therefore need to be considered to understand and accurately predict a species’ population dynamics.

Rapid evolution is perhaps most clearly evident in multi-strain host-pathogen systems. Strain variants of many diseases (most notably RNA viruses) can emerge and replace one another over a single generation of their hosts. Research in my lab has increasingly focused on understanding the interaction between the dynamics of disease outbreaks and pathogen immune escape. We are studying this interaction using mathematical models and phylogenetic approaches.

My primary research contribution to this area has been in the design of multi-strain models that include developmental models (specifically, genotype-phenotype maps) which are consistent with empirical virological data. Specifically, instead of assuming that the number of mutations between different viral strains is a good proxy for their antigenic distance (and therefore their degree of cross-immunity), recent work of mine has incorporated alternative mappings, based on neutral networks in genotype space, to model how genetic changes affect antigenic phenotypes. The incorporation of this alternative mapping into epidemiological models of influenza has given us a new understanding of the processes that control influenza’s genetic diversity and disease dynamics.

Understanding the immunological and environmental factors that drive infectious disease dynamics

Ecological research from the 1940’s/1950’s gave rise to two alternative hypotheses for the processes that regulate a species’ population dynamics. Nicholson and Bailey’s work on parasitoids showed that intrinsic, or density-dependent, feedback can produce temporal variation of population sizes; in contrast, Andrewartha and Birch showed that extrinsic, or density-independent, factors such as climate can affect a species’ population dynamics. More recently, there is a growing recognition that both intrinsic and extrinsic factors are likely to play a role in regulating population dynamics, and that there may be a complex interplay between these factors in nonlinear ecological systems. A major research line in population ecology has therefore been the identification of the relative contribution of these two different factors, given the irregular temporal patterns of population time series.

A similar debate has taken place about the relative contributions of intrinsic factors and extrinsic drivers (in particular environmental forcing) in generating interannual variability in disease dynamics. Research in my lab addresses the roles of these factors in disease dynamics through: a) the development of theoretical models that allow for both density-dependent and density-independent factors in regulating disease dynamics, and b) the fitting of these models to disease data to understand the relative contributions of these factors and the ways in which they interact.