Assessment of novel Lehmann’s Funnel Entry Trap prototypes performance to control malaria mosquito populations
Bibliographic record
Abstract
Abstract BackgroundThere is a global consensus that new intervention tools are needed to make the final steps toward malaria elimination/eradication. In a recent study in Burkina Faso, the Lehmann Funnel Entry Trap (LFET) has shown promising results in the reduction of mosquito densities, even in areas where insecticide resistance is as high as 80%. The LFET requires no chemicals and is self-operated. However, one of the issues with the original LFET is the size of the funnel, which often occupies too much space within users’ homes. Here, we compared the performance of three new, smaller-sized LFET prototypes that combine a screening and killing effect on mosquitoes. MethodsThe study was carried out over three months during the rainy season in low and high malaria vector density sites, Soumousso and Vallée du Kou, respectively. The original LFET (or ‘Prototype 1’/’P1’) was modified to produce three new prototypes, which we will refer to as Prototype 2 (‘the Medium’ or ‘P2’), Prototype 3 (P3) and Prototype 4 (P4). Each of the new prototypes was tested on eight days per month over the three-month period to assess their effectiveness in trapping and killing mosquitoes entering houses through the windows compared to the original LFET. ResultsOverall, 78,435 mosquitoes (mainly Anopheles gambiae s.l.) were collected in the two study sites, both in the traps and in the houses. A total of 56,430 (72%) mosquitoes were collected from the traps. In Vallée du Kou, the original LFET caught a greater number of mosquitoes than the Medium (prototype 2), whereas no difference was observed between the other new prototypes (3 and 4) and the Medium. In Soumousso, the original and Medium LFETs both collected significantly greater numbers of mosquitoes compared to prototypes 3 and 4. ConclusionThis study has shown that the new LFET prototypes are effective in trapping mosquitoes in high mosquito density settings. A large-scale study with one of the prototypes will be needed to assess community acceptance of the traps and their ability to control malaria vectors.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".