Integrating ecological approaches to interrupt schistosomiasis transmission: opportunities and challenges
Bibliographic record
Abstract
BACKGROUND: The development of agenda for global schistosomiasis elimination as a public health problem generates enthusiasms among global health communities, motivating great interests in both research and practice. Recent China-Africa schistosomiasis control initiatives, aiming to enhance collaboration on disease control in African countries, reflect in part that momentum. Yet there is a pressing need to know whether the Chinese experiences can be translated and applied in African settings. MAIN BODY: China's remarkable achievements in schistosomiasis control programme, associated experiences and lessons, have much to offer to those combating the disease. Central to the success of China's control programmes is a strategy termed "integrated control" - integrating environmental approaches (e.g. improved sanitation, agricultural and hydrological development and management), which target different phases of the parasite transmission system, to chemical-based drug treatment and mollusciciding. Yet, despite significant measurable public health benefits, such integration is usually based on field experience and remains largely uncharacterized in an ecological context. This has limited our knowledge on relative contributions of varying components of the integrated control programme to the suppression of disease transmission, making it challenging to generalize the strategy elsewhere. In this opinion article, we have described and discussed these challenges, along with opportunities and research needs to move forward. CONCLUSIONS: There is an urgent need to formalize an ecological framework for the integrated control programme that would allow research towards improved mechanistic understanding, quantification, and prediction of the control efforts.
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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 | 0.012 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".