The field of computational phylodynamics has witnessed a rich development of statistical inference tools with increasing levels of sophistication that can be applied to address a variety of questions about the evolution and epidemiology of viruses. The central premise of the field is that viruses generally evolve so rapidly that epidemic processes leave an imprint in their genomes. When focusing on deep phylogenies, a rich substitution history may confound time-measured evolutionary analyses, whereas for short-term outbreaks, it may be questioned whether the imprint provides the necessary resolution for insightful evolutionary reconstructions.
Here, I will illustrate these aspects on different viral examples. The Hepatitis B virus represents an example of a deep evolutionary history that has been difficult to date accurately using sequences sampled over the last decades. Recently, ancient DNA work has resulted in the first HBV samples dating back thousands of years. Using molecular clock modeling that accommodates time-dependent evolutionary rates, I will show how recent rapid evolutionary rate estimates can be reconciled with the long-term evolutionary dynamics of the virus.
The 2013-2016 West African Ebola epidemic marked the start of real-time genomic sequencing. Using this example, I will illustrate that short-term outbreak dynamics can be investigated using viral genome sequences, but integrating various sources of information with genomic data promises to deliver more precise insights in infectious diseases. Finally, using recent work on Lassa virus in West Africa, I will further highlight how in-field, real-time molecular epidemiology may impact outbreak responses.
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