Population isolation predicts the severity of historical human epidemics
Bibliographic record
Abstract
Abstract Aim Infectious diseases are a major source of human mortality and have altered human history. Despite their importance, we lack a thorough understanding of why some historical epidemics were more deadly than others. For many organisms, geographically isolated populations (e.g., populations distant from the mainland) experience more severe epidemics, including after long periods of isolation. These patterns are likely to arise because geographical isolation reduces contact with infectious diseases, causing a corresponding naïvety and susceptibility to those pathogens. Here, we test for equivalent patterns in human populations, but over much longer time frames than have been considered previously. Location Global, but with a particular focus on islands in the Americas and the Pacific. Time period Approximately 1000–1920 CE. Major taxa studied Humans and two of their pathogens: smallpox and influenza. Methods We used historical mortality data from populations afflicted with smallpox or Spanish flu to test for relationships between isolation, a suite of covariates (e.g., island size, population density) and epidemic severity (mortality rate). Results We show that populations isolated from the mainland suffered greater mortality. Interestingly, New World populations suffered universally higher mortality during smallpox epidemics, probably because they became isolated before the appearance of smallpox, causing high levels of susceptibility. Conversely, by 1918 New World populations were predominantly Eurasian and thus historically exposed to influenza, causing low mortality. Conclusion Human populations exhibit patterns of epidemics analogous to those in non‐human species; isolated populations tend to suffer more severe epidemics. However, historical contingencies can cause patterns to diverge from those typically observed among other free‐living species. Generally, we suggest that geography underlies the ecology and evolution of humans and their pathogens.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".