Phylodynamics is a new and rapidly growing field that combines epidemiology and computational biology to combat infectious disease outbreaks. The field stems from the concept of phylogeny, in which a tree represents how different populations – of virus infections, for example – are related through a series of common ancestors. The genetic similarities among populations are used to reconstruct these ancestral relationships back in time. This is particularly important for viruses, which evolve so quickly that each infection becomes genetically unique within weeks or months of being transmitted from the previous host. Consequently, the virus phylogeny can be used to estimate how the infections spread through the host population. Phylodynamics has already had an enormous impact on our understanding of outbreaks including HIV, hepatitis C virus, and Ebolavirus. Further progress is stymied, however, by simple models that can’t accommodate large data sets.
Dr. Art F.Y. Poon of Western University, Ontario, is developing a completely new approach to phylodynamics that adapts a method from pattern recognition to enable computers to “see” the shared features of different tree shapes. This approach will have an unprecedented capacity for more realistic models and larger data sets, improving global public health initiatives for infectious disease management and eradication.