Surveillance-response systems: the key to elimination of tropical diseases
Bibliographic record
Abstract
Tropical diseases remain a major cause of morbidity and mortality in developing countries. Although combined health efforts brought about significant improvements over the past 20 years, communities in resource-constrained settings lack the means of strengthening their environment in directions that would provide less favourable conditions for pathogens. Still, the impact of infectious diseases is declining worldwide along with progress made regarding responses to basic health problems and improving health services delivery to the most vulnerable populations. The London Declaration on Neglected Tropical Diseases (NTDs), initiated by the World Health Organization's NTD roadmap, set out the path towards control and eventual elimination of several tropical diseases by 2020, providing an impetus for local and regional disease elimination programmes. Tropical diseases are often patchy and erratic, and there are differing priorities in resources-limited and endemic countries at various levels of their public health systems. In order to identify and prioritize strategic research on elimination of tropical diseases, the 'First Forum on Surveillance-Response System Leading to Tropical Diseases Elimination' was convened in Shanghai in June 2012. Current strategies and the NTD roadmap were reviewed, followed by discussions on how to identify and critically examine prevailing challenges and opportunities, including inter-sectoral collaboration and approaches for elimination of several infectious, tropical diseases. A priority research agenda within a 'One Health-One World' frame of global health was developed, including (i) the establishment of a platform for resource-sharing and effective surveillance-response systems for Asia Pacific and Africa with an initial focus on elimination of lymphatic filariasis, malaria and schistosomiasis; (ii) development of new strategies, tools and approaches, such as improved diagnostics and antimalarial therapies; (iii) rigorous validation of surveillance-response systems; and (iv) designing pilot studies to transfer Chinese experiences of successful surveillance-response systems to endemic countries with limited resources.
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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 | 0.099 | 0.065 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.017 | 0.027 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.014 | 0.018 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".