Adjusting vector surveillance for human behaviors reveals Anopheles funestus drove a resurgence in malaria despite IRS with clothianidin in Uganda
Bibliographic record
Abstract
After remarkable success following the implementation of indoor residual spraying (IRS) and repeated rounds of universal distribution of insecticidal treated nets in Tororo District, eastern Uganda, a switch to clothianidin-based IRS in March 2020 was associated with a resurgence of malaria transmission. A previous study suggested Anopheles funestus may be driving the resurgence. This study was undertaken to assess the role of An. funestus in the resurgence and improve our understanding of how human-vector interaction affects malaria transmission in settings with extensive vector control. Using human landing catches and human behavioral observations, we found An. funestus infective biting, calculated from human-behavior adjusted biting rates and species-specific sporozoite rates, was 4.3 (95% Confidence Interval [CI]: 1.81 to 10.33) times higher after multiple rounds of clothianidin-based IRS when transmission was high and then dropped off markedly with a switch back to the organophosphate Actellic in March 2023. This finding was bolstered by a causal analysis showing a link between clothianidin-based IRS and 8.6 (95% CI: 2.0 to 37.0) times higher human-behavior adjusted human biting rates due to An. funestus. These findings highlight the importance of integrating monitoring of human-vector interaction and vector bionomics when introducing or evaluating changes in vector control interventions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".