A matter of timing: Biting by malaria-infected Anopheles mosquitoes and the use of interventions during the night in rural south-eastern Tanzania
Bibliographic record
Abstract
Knowing when and where infected mosquitoes bite is required for estimating accurate measures of malaria risk, assessing outdoor exposure, and designing intervention strategies. This study combines secondary analyses of a human behaviour survey and an entomological survey carried out in the same area to estimate human exposure to malaria-infected Anopheles mosquitoes throughout the night in rural villages in south-eastern Tanzania. Mosquitoes were collected hourly from 6PM to 6AM indoors and outdoors by human landing catches in 2019, and tested for Plasmodium falciparum sporozoite infections using ELISA. In nearby villages, a trained member in each selected household recorded the whereabouts and activities of the household members from 6PM to 6AM in 2016 and 2017. Vector control use was high: 99% of individuals were reported to use insecticide-treated nets and a recent trial of indoor residual spraying had achieved 80% coverage. The risk of being bitten by infected mosquitoes outdoors, indoors in bed, and indoors but not in bed, and use of mosquito nets was estimated for each hour of the night. Individuals were mainly outdoors before 9PM, and mainly indoors between 10PM and 5AM. The main malaria vectors caught were Anopheles funestus sensu stricto and An. arabiensis. Biting rates were higher in the night compared to the evening or early morning. Due to the high use of ITNs, an estimated 85% (95% CI 81%, 88%) of all exposure in children below school age and 76% (71%, 81%) in older household members could potentially be averted by ITNs under current use patterns. Outdoor exposure accounted for an estimated 11% (8%, 15%) of infective bites in children below school age and 17% (13%, 22%) in older individuals. Maintaining high levels of ITN access, use and effectiveness remains important for reducing malaria transmission in this area. Interventions against outdoor exposure would provide additional protection.
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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 | 0.002 |
| 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) | 0.001 | 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".