How can we understand infectious disease dynamics using mathematics?
Bryan T. Grenfell proposed “phylodynamics,” a methodology that predicts infectious disease dynamics of RNA viruses by considering viral evolution, and thus contributed to the development of the research field that integrates immune dynamics, epidemiology, and evolutionary biology. By virtue of these achievements, he has been instrumental in understanding infection mechanisms and proposing effective infectious disease control policies.
I have spent my career exploring the population dynamics and evolution of infectious diseases. I begin this lecture by introducing epidemics and the rich historical data sets which illuminate their dynamics. I focus on measles as a clear exemplar of the epidemic dynamics of acute immunizing infections. I then describe my research, under three main themes. First, I and collaborators explored the non-linear temporal dynamics of childhood infections, notably measles. We used simple mathematical models and time series analysis of epidemic data to explain oscillatory, sometimes chaotic epidemic sequences and the impact of seasonal and demographic drivers and vaccination on these patterns. Second, we analyzed the spatio-temporal dynamics of regional epidemics and the determinants of local epidemic persistence. Third, we explored the evolutionary dynamics of partly-immunizing pathogens such as influenza and SARS-CoV-2. I coined the term “phylodynamics” for the interactions between viral evolution and epidemiology that drives the “escape” of viral variants from prevailing immunity; phylodynamic ideas have since been applied to a wide variety of problems in pathogen evolution. I conclude by drawing broader lessons from my career, notably the power and pleasure of interdisciplinary collaboration.
Biology is often extremely complex but, sometimes, simple models can explain some of the complexity.
Profile is at the time of the award.