Identifying Spatial Clusters of Schistosomiasis in Anhui Province of China: A Study from the Perspective of Application
Bibliographic record
Abstract
With the strategy shifting from morbidity control to transmission interruption, the burden of schistosomiasis in China has been declining over the past decade. However, further controls of the epidemic in the lake and marshland regions remain a challenge. Prevalence data at county level were obtained from the provincial surveillance system in Anhui during 1997-2010. Spatial autocorrelation analysis and spatial scan statistics were combined to assess the spatial pattern of schistosomiasis. The spatial-temporal cluster analysis based on retrospective space-time scan statistics was further used to detect risk clusters. The Global Moran's I coefficients were mostly statistically significant during 1997-2004 but not significant during 2005-2010. The clusters detected by two spatial cluster methods occurred in Nanling, Tongling, Qingyang and Wuhu during 1997-2004, and Guichi and Wuhu from 2005 to 2010, respectively. Spatial-temporal cluster analysis revealed 2 main clusters, namely Nanling (1999-2002) and Guichi (2005-2008). The clustering regions were significantly narrowed while the spatial extent became scattered during the study period. The high-risk areas shifted from the low reaches of the Yangtze River to the upper stream, suggesting the focus of schistosomiasis control should be shifted accordingly and priority should be given to the snail habitats within the high-risk areas of schistosomiasis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".