Mosquito Biting and Movement Rates as an Emergent Community Property and The Implications for Malarial Interventions
Bibliographic record
Abstract
Malaria, a mosquito-vectored disease, continues to be one of the most important scourges afflicting humankind. In this paper, we take a mosquito-centric approach by studying mosquito states (i.e., energy, neurological health, and toxin information state) to demonstrate how key parameters of malaria, biting and movement rates and mosquito survival, are all emergent properties of those states when considered in the context of the background community interactions. We do so as follows: First, we develop a dynamic state variable model of mosquito biting and movement decisions that maximize mosquito expected reproductive success (fitness), and then we embed those optimal policies in a Monte Carlo simulation wherein mosquitoes attempt to feed on human hosts at domiciles where insecticide-treated bednets (ITNs) and insecticidal residual wall sprays (IRSs) are used. We find that biting rates, at the domicile level, are not impacted by mosquito state but that emigration rates from domiciles are determined by an interaction between mosquito energy state, information state, and risk of predation. This means that malaria incidence, at the village level at least, may be best understood as a response of mosquitoes to their ecological community that includes nectar-bearing plants, predators, the spatial arrangement of homes, and the protection of humans in those homes.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".