Limited impact of within-vector ecology on the evolution of malaria parasite transmission investment
Bibliographic record
Abstract
Malaria parasites spend part of their life in a vertebrate host and the rest in an arthropod vector and must successfully navigate both environments to gain fitness. In vertebrate hosts, malaria parasites infect red blood cells and can either replicate asexually or develop into the sexual form required for transmission to the vector. Despite the clear fitness benefits of onward transmission, only a small proportion of malaria parasites convert to sexual development. Mathematical models seeking to test the plausibility of various hypotheses to explain these low “conversion rates” have focused almost exclusively on the vertebrate/host half of the parasite life cycle. Here, we examined how processes occurring in the vector, including density-dependent parasite development and parasite-induced vector mortality, influence the evolution of parasite conversion rate in the host by developing a multi-scale model of within-host infection dynamics and parasite within-vector developmental processes for rodent malaria. We found that, regardless of model specifications (e.g., definitions of fitness, magnitude of parasite-induced vector mortality), considering processes within the vector had only a weak influence on the optimal conversion rate, but substantially diminished the fitness returns for all strategies and resulted in a sharper declines off the optima. Our approach allowed us to derive new metrics of parasite fitness (which we call “infectivity functions”) that link within-host gametocyte density to the probability of transmission to new hosts after passing through the vector, and that prevent overestimation of parasite transmission potential.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".