Support for research towards understanding the population health vulnerabilities to vector-borne diseases: increasing resilience under climate change conditions in Africa
Bibliographic record
Abstract
BACKGROUND: Diseases transmitted to humans by vectors account for 17% of all infectious diseases and remain significant public health problems. Through the years, great strides have been taken towards combatting vector-borne diseases (VBDs), most notably through large scale and coordinated control programmes, which have contributed to the decline of the global mortality attributed to VBDs. However, with environmental changes, including climate change, the impact on VBDs is anticipated to be significant, in terms of VBD-related hazards, vulnerabilities and exposure. While there is growing awareness on the vulnerability of the African continent to VBDs in the context of climate change, there is still a paucity of research being undertaken in this area, and impeding the formulation of evidence-based health policy change. MAIN BODY: One way in which the gap in knowledge and evidence can be filled is for donor institutions to support research in this area. The collaboration between the WHO Special Programme for Research and Training in Tropical Diseases (TDR) and the International Centre for Research and Development (IDRC) builds on more than 10 years of partnership in research capacity-building in the field of tropical diseases. From this partnership was born yet another research initiative on VBDs and the impact of climate change in the Sahel and sub-Saharan Africa. This paper lists the projects supported under this research initiative and provides a brief on some of the policy and good practice recommendations emerging from the ongoing implementation of the research projects. CONCLUSION: Data generated from the research initiative are expected to be uptaken by stakeholders (including communities, policy makers, public health practitioners and other relevant partners) to contribute to a better understanding of the impacts of social, environmental and climate change on VBDs(i.e. the nature of the hazard, vulnerabilities, exposure), and improve the ability of African countries to adapt to and reduce the effects of these changes in ways that benefit their most vulnerable populations.
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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 | 0.071 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.033 | 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".