Evolution of parasite virulence when host responses cause disease
Bibliographic record
Abstract
The trade-off hypothesis of virulence evolution rests on the assumption that infection-induced mortality is a consequence of host exploitation by parasites. This hypothesis lies at the heart of many empirical and theoretical studies of virulence evolution, despite growing evidence that infection-induced mortality is very often a by-product of host immune responses. We extend the theoretical framework of the trade-off hypothesis to incorporate such immunopathology and explore how this detrimental aspect of host defence mechanisms affects the evolution of pathogen exploitation and hence infection-induced mortality. We argue that there are qualitatively different ways in which immunopathology can arise and suggest ways in which empirical studies can tease apart these effects. We show that immunopathology can cause infection-induced mortality to increase or decrease as a result of pathogen evolution, depending on how it covaries with pathogen exploitation strategies and with parasite killing by hosts. Immunopathology is thus an important determinant of whether public and animal health programmes will drive evolution in a clinically beneficial or detrimental direction. Immunopathology complicates our understanding of disease evolution, but can nevertheless be readily accounted for within the framework of the trade-off hypothesis.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".