Control of Tungiasis in Absence of a Roadmap: Grassroots and Global Approaches
Bibliographic record
Abstract
. It inflicts misery upon tens of millions of people, mostly children, across Central and South America and sub-Saharan Africa, and yet there is no globally accepted roadmap for its control. Here we review how research in the last 15 years has developed control methods and report on new grassroots and digital mapping approaches. Treatment is now possible with a two-component dimethicone, used for the treatment of headlice in Europe, Asia and Canada, but not yet available in most tungiasis-endemic areas. Prevention is possible through the daily use of repellents based on coconut oil. A Kenyan coastal community has successfully controlled tungiasis using a neem and coconut oil mix produced locally to treat cases, combined with spraying floors with neem solution and distributing closed shoes. Development of affordable hard floor technology is underway, although not yet widely available, but is a priority to control off-host stages in the floors of homes. A new web-based digital mapping application will enable researchers and health officials to collaborate, share data and map the prevalence of tungiasis. We conclude that tungiasis can be controlled through a multi-disciplinary, One Health approach.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".