An epidemiological context for the consequences of phenotypic plasticity in host-pathogen interactions
Bibliographic record
Abstract
Questions: What effect do different forms of strategic plasticity have on the co-evolution of host and pathogen? We focus on the co-evolution of pathogen exploitation strategies (virulence) and the rate at which the host immune system clears the pathogen (clearance rate). Mathematical methods: Evolutionary game theory; computer simulations of host–pathogen pairs negotiating strategies using linear response rules. Key assumptions: A trade-off exists between virulence and transmission rate for pathogens, and between fecundity while infected and recovery (clearance) rate for hosts. Disease dynamics are described by a standard susceptible–infected–susceptible epidemiological model. Transmission of the pathogen is exclusively horizontal, and random mixing of the host population is assumed. Conclusions: All forms of plasticity promote the co-evolution of virulence and clearance rates that are lower than those predicted in the absence of plasticity. Plasticity promotes increased disease incidence rate (higher than those predicted in the absence of plasticity), but the way it affects case mortality depends critically on the assumed mode of plasticity.
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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 | 0.002 | 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 | 0.001 |
| 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".