Genetic causes and consequences of ancient plague pandemics: pathogen evolution and human demographic reverberations
Bibliographic record
Abstract
The plague has caused some of the most noted catastrophes in recorded history. The Plague of Justinian (6th-8th century CE), the Black Death (14th-18th century CE) and the current third pandemic (mid-19th-mid-20th century CE), are responsible for the mortality of hundreds of millions of people. In this thesis, I begin to explore these ancient pandemics from a syndemic angle. This framework considers disease events to be caused by more than just a single pathogen, but through the complex interplay of factors, including the pathogen(s), host resistance/susceptibility, environment and socio-cultural dynamics. Presented in sandwich-thesis format, I use the tools offered by ancient DNA research, upon which I have built, to observe the genetic causes and consequences of ancient plague pandemics from three different angles. I recover and sequence the first draft genome sequence from a strain of the plague bacterium, Yersinia pestis, associated with the Plague of Justinian and place it within the context of modern strains to better understand the pattern(s) behind the dissemination of the three historic pandemics. Next, I study the demographic effects of the Black Death by sequencing the mitochondrial genome, a maternally inherited marker useful for studying migration, from 264 individuals dating to late-medieval England and Denmark to look for signals of large-scale population turnover and migration. I then compare changes in frequencies of hundreds of immune-related loci from individuals who died before, during, and after the Black Death to better understand how human immune genes were shaped by and helped to shape the mass mortality of Black Death. Finally, I outline the methodological challenges faced and improvements made during the course of these projects through the examples of a variety of diverse projects. By examining the relationships of ancient strains of Y.pestis, the changes in host immune genetics, and the demographic effects of plague, and integrating these results with those from other disciplines, we can begin to understand why and how ancient pandemics of plague were so devastating to humans in the past and how we may respond to them in the future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".