Spatial comparison of herald and main waves in London’s nineteenth-century cholera epidemics
Bibliographic record
Abstract
Nineteenth-century London experienced four extraordinarily severe summertime cholera epidemics. Three were preceded by less severe non-summer outbreaks. Twenty-first-century research hypothesizes them as herald waves of potentially new cholera strains. This study examined the geographical characteristics of these herald waves and compared them to their subsequent main waves to determine if there was a geographical component to the significant difference in wave severity. Cholera mortality data for London's parishes and registration districts were extracted from contemporaneous records. The data were normalized and scaled. Each epidemic wave was divided into two segments for analysis. A Spearman's rank correlation was used to assess the relationship between a herald and its subsequent main wave. Geospatial analytical tools were used to determine and display each segment's geographic distribution pattern using autocorrelation techniques to determine its central point. Results show that the herald wave of each epidemic shared characteristics similar to its following main wave. Central-point locations were similar and Spearman's rank coefficients showed high degrees of correlation. Autocorrelation results were similar, with one exception reflecting an appalling anomalous cholera outbreak at an institution for children. Because of the demonstrated similarity of each epidemic's herald and main waves, this study did not detect a spatial characteristic that could explain the observed difference in severity between the studied heralds and mains.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".