Elimination of Schistosoma japonicum Transmission in China: A Case of Schistosomiasis Control in the Severe Epidemic Area of Anhui Province
Bibliographic record
Abstract
Over the several decades, China has been incessantly optimizing control strategies in response to the varying epidemic situations of schistosomiasis. We evaluated continuously the changing prevalence under different control strategies of two villages, Sanlian and Guifan, in China through five phases lasting 37 years. We tested residents, calculated prevalence and discussed change causes. We found the prevalence in Sanlian did not differ significant from that of Guifan (p = 0.18) in 1981, but decreased to 2.66%, much lower than Guifan’s 11.25%, in 1984 (p = 0). Besides, prevalence in Guifan increased to 21.25% in 1987, while in Sanlian it rose to 20.78% until 1989. Those data confirmed that praziquantel combined with snail control could better reduce the prevalence. From 1992 to 1994, the prevalence in the two villages displayed downtrends, which showed the World Bank Loan Project worked. From 1995 to 2004, repeated oscillations with no obvious change trend was seen. Since 2005, the prevalence in both villages has shown a significant downtrend (p < 0.05), which suggests the integrated strategy is effective. We considered the control strategies were implemented suitably in the study area under changing social circumstances. Adjusting the strategy in consideration of social transformations is necessary and vital. The experience may be useful for policy making of other epidemic areas with an analogous situation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".