Influenza-Associated Mortality during the 1918–1919 Influenza Pandemic in Alaska and Labrador
Bibliographic record
Abstract
Some of the most severely affected communities in the world during the 1918–19 influenza pandemic were in Labrador and Alaska. Although these two regions are on the opposite ends of North America, a cultural continuum in the Inuit populations extends throughout the North American Arctic. Both regions contain other population groups, however, and because of these similarities and differences, a comparison of their experiences during the pandemic provides new insights into how culture and environment may influence patterns of spread of infectious disease. We describe here analyses of the patterns of influenza mortality in 97 Alaska communities and 37 Labrador communities. The Alaska communities are divided into five geographic regions corresponding to recognized cultural groups in the region; the Labrador communities are separated into three regions that vary in the degree of admixture between European and indigenous (primarily Inuit) groups. In both Alaska and Labrador mortality was substantially higher than the worldwide average of 2.5–5 percent. Average mortality ranged from less than 1 percent to 38 percent at the regional level in Alaska and from 1 percent to 75 percent at the regional level in Labrador with up to 90 percent mortality in some local communities in both Alaska and Labrador. A number of factors influencing this heterogeneous experience are discussed, including the impact of weather and geography; attempts to protect communities by implementing quarantine policies; accessibility of health care; nutritional deficiencies; cultural factors, such as settlement patterns, seasonal activities, and ethnicity; and exposure to earlier outbreaks of influenza or other diseases that may have increased or lessened the impact of influenza in 1918–19.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".