Zoonotic Barrier Disruption and the Rise of the Third Plague Pandemic: A One Health Analysis of 19th-Century Yunnan and the Emergence of Yersinia Pestis Strain 1.ORI
Bibliographic record
Abstract
The Third Plague Pandemic originated in 19th-century Yunnan, China, yet the confluence of factors that enabled the pandemic strain Yersinia pestis 1.ORI to emerge and spread globally remains unclear. Using a One Health framework, this study investigates how human-driven ecological and socioeconomic changes disrupted zoonotic barriers in Yunnan. We conduct an interdisciplinary historical analysis, triangulating evidence from Qing dynasty gazetteers, environmental reconstructions, and biological data on plague ecology, including host–vector dynamics, to model conditions for spillover and spread and to build a convergent, validated case. The analysis identifies a mid-19th-century convergence that created a high-risk interface: widespread deforestation from mining and agriculture, rapid population growth, increased synanthropic rat densities, and the turmoil of the Panthay Rebellion. Socioeconomic stressors—labour migration into mining valleys, currency devaluation undermining food security, and comorbidities such as malnutrition, heavy metal contamination, and opium use—may have further increased host susceptibility. This socio-ecological context catalysed spillover and establishment of the 1.ORI strain in commensal rat populations. The findings show the pandemic’s origin reflects spatiotemporal convergence rather than a single cause, while noting uncertainty in quantifying historical ecological and health parameters; the case offers a framework for assessing contemporary pandemic risks. It underscores how layered pressures operate across timescales.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".