Co-evolution of pathogen–host interactions with vertical transmission can produce bistable outcomes
Bibliographic record
Abstract
Vertical transmission is widely predicted to select for reduced virulence of pathogens. Recent theory cast doubt on this prediction by showing that the evolutionary response of the host to vertical transmission can lead to severe disease outcomes. That theory, however, takes a simplified view of host population dynamics by assuming pathogen-induced mortality alone inhibits host population growth. The assumption limits our ability to uncover benign co-evolutionary outcomes characterized by low levels of pathogen-induced mortality. Here, we revisit the role of vertical transmission using a model that assumes host population growth is self-regulated. Our model tracks the co-evolution of pathogen-induced mortality and host recovery until both have reached an evolutionarily stable level. For any given set of model conditions, we could identify as many as two distinct pairs of stable mortality-recovery traits. Mortality and recovery were higher for one of the pairs (the 'escalated' one) and lower for the other of the pairs (the 'de-escalated' one). As the rate of vertical transmission rose, stable expression of the pathogen-induced mortality trait always decreased, while stable expression of the host-recovery trait increased for 'escalated' pairs and decreased for 'de-escalated' ones. In addition, (i) increasing the intrinsic rate of host population growth, (ii) increasing the cost of host recovery, and (iii) decreasing the efficiency of horizontal disease transmission all led to lower levels of stable trait expression for both pathogen and host. Factors (i)-(iii) also led to lower virulence, more frequent occurrence of the de-escalated (almost commensal) stable outcome, and greater disease prevalence. We conclude that (i)-(iii) promote the co-evolution of more benign interactions in keeping with previous findings. However, our new insight is that the benign nature of the host-pathogen interaction can now be understood as the more frequent occurrence of the de-escalated outcome. We discuss our findings in light of previous theory and experimental work.
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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 | 0.000 | 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 | 0.001 | 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".