Predicting optimal transmission investment in malaria parasites
Bibliographic record
Abstract
In vertebrate hosts, malaria parasites face a tradeoff between replicating and the production of transmission stages that can be passed onto mosquitoes. This tradeoff is analogous to growth-reproduction tradeoffs in multicellular organisms. We use a mathematical model tailored to the life cycle and dynamics of malaria parasites to identify allocation strategies that maximize cumulative transmission potential to mosquitoes. We show that plastic strategies can substantially outperform fixed allocation because parasites can achieve greater fitness by investing in proliferation early and delaying the production of transmission stages. Parasites should further benefit from restraining transmission investment later in infection, because such a strategy can help maintain parasite numbers in the face of resource depletion. Early allocation decisions are predicted to have the greatest impact on parasite fitness. If the immune response saturates as parasite numbers increase, parasites should benefit from even longer delays prior to transmission investment. The presence of a competing strain selects for consistently lower levels of transmission investment and dramatically increased exploitation of the red blood cell resource. While we provide a detailed analysis of tradeoffs pertaining to malaria life history, our approach for identifying optimal plastic allocation strategies may be broadly applicable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".