Public Health Interventions, Epidemic Growth, and Regional Variation of the 1918 Influenza Pandemic Outbreak in a Swiss Canton and Its Greater Regions
Bibliographic record
Abstract
Public health interventions implemented during the coronavirus disease 2019 (COVID-19) pandemic are based on experience gained from past pandemics. The 1918 influenza pandemic is the most extensively researched historical influenza outbreak. All 9335 reports available in the State Archives on 121 152 cases of influenza-like illness from the canton of Bern from 473 of 497 municipalities (95.2%) were collected; the cases were registered between 30 June 1918 and 30 June 1919. The overall incidence rates of newly registered cases per week for the 9 greater regions of Bern for both the first and second waves of the pandemic were calculated. Relative incidence rate ratios (RIRRs) were calculated to estimate the change in the slope of incidence curves associated with public health interventions. During the first wave, school closures (RIRR, 0.16 [95% CI, 0.15 to 0.17]) and restrictions of mass gatherings (RIRR, 0.57 [CI, 0.54 to 0.61]) were associated with a deceleration of epidemic growth. During the second wave, in autumn 1918, cantonal authorities initially reacted hesitantly and delegated the responsibility to enact interventions to municipal authorities, which was associated with a lack of containment of the second wave. A premature relaxation of restrictions on mass gatherings was associated with a resurgence of the epidemic (RIRR, 1.18 [CI, 1.12 to 1.25]). Strikingly similar patterns were found in the management of the COVID-19 outbreak in Switzerland, with a considerably higher amplitude and prolonged duration of the second wave and much higher associated rates of hospitalization and mortality.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".