Virulence Evolution of Pathogens That Can Grow in Reservoir Environments
Bibliographic record
Abstract
AbstractMany pathogens reside in environmental reservoirs within which they can reproduce and from which they can infect hosts. These facultative pathogens experience different selective pressures in host-associated environments and reservoir environments. Heterogeneous selective pressures have the potential to influence the virulence evolution of these pathogens. Previous research has examined how environmental transmission influences the selective pressures shaping the virulence of pathogens that cannot reproduce in environmental reservoirs, yet many pathogens of humans, crop plants, and livestock can reproduce in these environments. We build on this work to examine how reproduction in reservoirs influences disease dynamics and virulence evolution in a simple facultative pathogen model. We use adaptive dynamics to examine the evolutionary dynamics of facultative pathogens under potential trade-offs between transmission and virulence, shedding and virulence, and reservoir persistence and virulence. We then perform critical function analysis to generalize the results independent of specific trade-off assumptions. We determine that diverse virulence strategies, sometimes resulting from evolutionary bistability or evolutionary branching conditions, are expected for facultative pathogens. Our findings motivate research establishing which trade-offs most strongly influence the virulence evolution of facultative pathogens.
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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".