Development of a New Trapping System with Potential Implementation as a Tool for Mosquito-Borne Arbovirus Surveillance
Bibliographic record
Abstract
Mosquitoes of the Aedes and Culex genera are primary vectors of arboviruses such as the dengue, Zika, chikungunya (CHIKV), Oropouche, and West Nile viruses, causing millions of infections annually. Standard virus detection in mosquitoes requires capturing, transporting, and processing samples with a cold chain to preserve RNA, which is challenging in resource-limited areas. FTA cards preserve viral RNA at room temperature and have been used to collect mosquito saliva, a key sample for assessing transmission. However, most FTA-based traps require electricity or CO2, limiting use in low-resource settings. This study adapted and evaluated the BR-ArboTrap, a low-cost trap derived from an oviposition trap, integrating a sugar-based attractant with FTA cards to collect mosquito saliva, without electricity or refrigeration. Aedes aegypti exposed to CHIKV were used in three experiments to evaluate: (i) RNA preservation under different conditions, (ii) the minimum number of positive mosquitoes for detection, and (iii) RNA amounts on FTA versus blood. RT-qPCR detected CHIKV RNA in 90% of FTA cards and 96% of exposed mosquitoes. RNA remained stable under varying conditions, with no significant difference compared to blood. BR-ArboTrap is an effective, affordable, and field-ready tool to enhance arbovirus surveillance in remote and low-resource areas.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".