An analysis of the 1875-1877 scarlet fever epidemic of Cape Breton Island, Nova Scotia
Bibliographic record
Abstract
An epidemic of scarlet fever on Cape Breton Island, Nova Scotia, Canada between 1875 and 1877 is analyzed in the context of a larger, world-wide pandemic of scarlet fever that occurred between 1825 and 1885. Data derived from public records on national censuses, provincial vital death records and parish records suggest that the epidemic impacted the two main ethnic groups of the island, the Acadians and the Scots, in very different ways. Statistical analysis was done considering the temporal and socio-cultural context of cause of death reporting in order to examine if this initial reading is valid. A deterministic computer model was also created to analyze the effects of each factor on the overall course of the epidemic. Results suggest that although the two groups did experience the epidemic in different ways, this difference is partially attributed to the terms used to describe cause of death information. Occupation, and household type resulting from occupation, is found to be a key indicator of epidemic experience. Differences in person to person contact rate are association with the different occupations/household types. Ethnic group preferences for the occupations of fishing or farming inextricably tie the issues of ethnicity and occupation together. The number of contacts people have per unit of time was found to be one of the major factors correlated to the epidemic experience. These results emphasize the importance of socio-cultural factors in an age where drug therapies are becoming less effective. They point to a need to understand the interactions between biology and behavior when examining such complex phenomena as human epidemics.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".