Temporal-spatial distribution of epidemics in Ming and Qing dynasties (1368-1911A.D.) in China
Bibliographic record
Abstract
Human health is affected by global environmental change(GEC).The distribution and transmission of epidemics in history could be rebuilt to reveal the relationship between health and GEC from the past.This study used the collected data of epidemics in the Ming and Qing periods,when China was seriously influenced by epidemics,to set up some indicators to quantitatively analyze the temporal-spatial distribution of epidemics by GIS and geo-statistics methods.The results are as follows: (1) The indicators of of Incident of Epidemics in 10 Years and Number of Incident of Epidemics in 10 Years were set up to analyze the temporal distribution of epidemics in China.The results show that the epidemics were more and more frequent from the early Ming to late Qing Dynasty.After 1840 A.D.,they happened nearly every year.1580~1589A.D.,1639~1648 A.D.,1813~1822 A.D.and 1857~1866 A.D.were the four peaks of frequency and scale of epidemics. (2) The indicators of Percentage of Incident of Epidemics to Total Years and Percentage of of Epidemics to the Total Counties were set up to analyze the spatial distribution of epidemics.The results show that many areas were affected by epidemics in the whole Ming and Qing dynasties.But the incidental frequency verified largely in different areas.The high frequency and the high incident areas descended from the east coastal area to the inland area.The middle and lower reaches of Yellow River and Yangtze River were the regions with the highest frequency.The epidemics occurred in all the eastern and central provinces.Over 80% of the counties in Beijing,Tianjin,Shanghai,Hainan,Fujian,Anhui,Jiangxi,Shandong and Zhejiang were affected by epidemics. (3) The indicator of Proportion of Accumulated Incident of Epidemics to the Total Years was set up to quantitatively analyze both the occurrence frequency and the affected regional scales.The results show that Shanghai,Zhejiang and Shandong were the highest with Proportion of Accumulated Incident of Epidemics to the Total Years,indicating that occurrences in these provinces were both high frequency and large scales.Further studies should be done on their relations with droughts and floods,socioeconomic development and the distribution of population.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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".