| Summary: | Pandemics of plague have reemerged multiple times throughout human history with tremendous mortality and extensive geographic spread. The First Pandemic (6th - 8th century) devastated the Mediterranean world, the Second Pandemic (14th - 19th century) swept across much of Afro-Eurasia, and the Third Pandemic (19th - 20th century) reached every continent except for Antarctica, and continues to persist in various endemic foci around the world. Despite centuries of historical research, the epidemiology of these pandemics remains enigmatic. However, recent technological advancements have yielded a novel form of evidence: ancient DNA of the plague bacterium Yersinia pestis. In this thesis, I explore how genomic data can be used to unravel the mysteries of when and where this disease appeared in the past. In particular, I focus on phylogenetic approaches to studying this 'small microbe' with 'big data' (i.e. 100s - 1000s of genomes). I begin by describing novel software I developed that supports the acquisition and curation of large amounts of DNA sequences in public databases. I then use this tool to create an updated global phylogeny of Y. pestis, which includes ~600 genomes with standardized metadata. I devise and validate a new approach for temporal modeling (i.e. molecular clock) that produces robust divergence dates in pandemic lineages of Y. pestis. In addition, I critically examine the questions that genomic evidence can and cannot address in isolation, such as whether the timing and spread of short-term epidemics can be confidently reconstructed. Finally, I apply this theoretical and methodological insight to a case study in which I reconstruct the appearance, persistence, and disappearance of plague in Denmark during the Second Pandemic. The three papers enclosed in this sandwich-thesis contribute to a larger body of work on the anthropology of plague, which seeks to understand how disease exposure and experience change over time and between human populations. Furthermore, this dissertation more broadly ...
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