The population dynamics of infectious diseases exhibit a rich array of temporal and spatial patterns. The quantitative understanding and prediction of these patterns are crucial to public health efforts, including the evaluation and planning of control, as well as for epidemic preparedness and prevention.
My group is particularly interested in the population dynamics of infectious diseases that are climate sensitive, especially waterborne infections such as cholera and vector-borne infections such as malaria. We seek to understand the influence of climate variability (e.g., rainfall, the El Niño Southern Oscillation) on the year-to-year variation in the size of epidemics, in the context of the dynamics of transmission itself. At the population level, disease dynamics are nonlinear because they function essentially as a consumer-resource system in which the susceptible (non-immune) individuals are the resource and the propagating infection is the consumer. The resulting waxing and waning of immunity at the population level can generate interannual cycles in the size of seasonal epidemics over time in the absence of climate forcing. These interannual cycles can in turn interact with climate influences in ways that limit correlative analyses and predictions based on climate alone. Similarly, nonlinear responses to environmental drivers can result from control efforts whose levels respond to past disease burden.
Our work relies on mathematical models of disease transmission and on computational methods for statistical inference to disentangle the roles of environmental forcing and disease dynamics from temporal or spatio-temporal records. To some extent, the disease systems we wish to understand can be compared to a "black box" for which we have only partial and noisy measurements, with multiple hidden variables. Mathematical models allow us to open this black box and make inferences on the main mechanisms that are at play. They also allow us to ask whether explaining past patterns helps us anticipate future risk.
Increasingly, infectious disease dynamics and their response to climate variability must be understood in the context of long-term change, not just in the environment but also in socioeconomic conditions and associated intervention measures. Research on cholera in Bangladesh is considering for example the interplay of climate forcing with the spatial distribution of human susceptibility in the large megacity of Dhaka. Studies on desert malaria in northwest India are addressing how irrigation-based development and associated control measures modify the influence of rainfall variability on epidemics. Research on highland malaria in East Africa asks whether the exacerbation of malaria in recent decades is in part the result of warming temperatures, despite other potential drivers such as the spread of drug resistance.
Another source of long-term change is pathogen evolution. Its interaction with epidemiological dynamics is central to another disease closer to home, seasonal influenza. With a combination of mathematical models and analyses of genetic sequence data, we are addressing how transmission dynamics influence the pathogen's (genetic and antigenic) diversity, and how this diversity in turn influences transmission dynamics. More generally, we are contributing to theory on whether and how immune selection, through competition for hosts via cross-immunity, structures pathogen populations from the perspective of antigen-encoding genes. The resulting diversity patterns and their roles in pathogen persistence are key questions with implications for the success of intervention measures.
Additional support is provided by the National Oceanic and Atmospheric Administration (Oceans and Health), the National Science Foundation (Theory in Biology), the Graham Environmental Sustainability Institute at the University of Michigan, and the James S. McDonnell Foundation (Global and Complex Systems).
As of December 11, 2012