Combining epidemiology with basic biology of sand flies, parasites, and hosts to inform leishmaniasis transmission dynamics and control
Bibliographic record
Abstract
Quantitation of the nonlinear heterogeneities in Leishmania parasites, sand fly vectors, and mammalian host relationships provides insights to better understand leishmanial transmission epidemiology towards improving its control. The parasite manipulates the sand fly via production of promastigote secretory gel (PSG), leading to the "blocked sand fly" phenotype, persistent feeding attempts, and feeding on multiple hosts. PSG is injected into the mammalian host with the parasite and promotes the establishment of infection. Animal models demonstrate that sand flies with the highest parasite loads and percent metacyclic promastigotes transmit more parasites with greater frequency, resulting in higher load infections that are more likely to be both symptomatic and efficient reservoirs. The existence of mammalian and sand fly "super-spreaders" provides a biological basis for the spatial and temporal clustering of clinical leishmanial disease. Sand fly blood-feeding behavior will determine the efficacies of indoor residual spraying, topical insecticides, and bed nets. Interventions need to have sufficient coverage to include transmission hot spots, especially in the absence of field tools to assess infectiousness. Interventions that reduce sand fly densities in the absence of elimination could have negative consequences, for example, by interfering with partial immunity conferred by exposure to sand fly saliva. A deeper understanding of both sand fly and host biology and behavior is essential to ensuring effectiveness of vector 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".