Epidemiological analysis of the Eyam plague outbreak of 1665–1666
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Plague, caused by the bacterium Yersinia pestis, is one of the deadliest infectious diseases in human history, and still causes worrying outbreaks in Africa and South America. Despite the historical and current importance of plague, several questions remain unanswered concerning its transmission routes and infection risk factors. The plague outbreak that started in September 1665 in the Derbyshire village of Eyam claimed 257 lives over 14 months, wiping out entire families. Since previous attempts at modelling the Eyam plague, new data have been unearthed from parish records revealing a much more complete record of the disease. Using a stochastic compartmental model and Bayesian analytical methods, we found that both rodent-to-human and human-to-human transmission played an important role in spreading the infection, and that they accounted, respectively, for a quarter and three-quarters of all infections, with a statistically significant seasonality effect. We also found that the force of infection was stronger for infectious individuals living in the same household compared with the rest of the village. Poverty significantly increased the risk of disease, whereas adulthood decreased the risk. These results on the Eyam outbreak contribute to the current debate on the relative importance of plague transmission routes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it