Mapping rabies distribution in China: a geospatial analysis of national surveillance data
Bibliographic record
Abstract
OBJECTIVES: Dog-mediated human rabies remains an important public health problem in China. In this study, we aimed to understand the spatiotemporal variation of rabies and examine its nonmedical ecological factors. METHODS: In this study, we used the annual incidence data for rabies at the province level in China to describe the incidence trends for the period 2004-2019 and used a Bayesian hierarchical spatiotemporal model to determine the impacts of environmental, economic, and demographic factors. RESULTS: From 2004 to 2019, there were 26,593 cases reported in 31 provinces in Mainland China, and the annual incidence increased from 0.02 per 100,000 in 2004 to 0.14 in 2007, substantially decreased in 2008, and was gradually declining thereafter. Guizhou, Guangxi, Hunan, and Hainan were four high-risk provinces, and Yunnan and Anhui provinces showed an increased risk in 2018 and 2019. Temperature and per capita gross domestic product were significantly positively correlated with the disease risk. The standardized morbidity ratio of rabies is likely to increase by 28% (relative risk: 1.28, 95% credible interval: 1.13-1.36) for every 1°C rise in temperature, and 17% (relative risk: 1.17, 95% credible interval: 1.01-1.34) for every 10,000 yuan increase in per capita gross domestic product. CONCLUSION: In most provinces in China, the risk of rabies has been reduced to a persistently low level. However, the progress of rabies control in six provinces have been less than satisfactory. The study highlights interventions, such as enhancing animal vaccination need to be implemented in these priority areas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".