VIRULENCE EVOLUTION IN EMERGING INFECTIOUS DISEASES
Bibliographic record
Abstract
Abstract Models of virulence evolution generally consider the outcome of competition between resident and mutant parasite strains at or near endemic equilibrium. Less studied is what happens during the initial phases of invasion and adaptation. Understanding initial adaptive dynamics is particularly important in the context of emerging diseases in wildlife and humans, for which rapid and accurate intervention may be of the essence. To address the question of virulence evolution in emerging diseases, we employ a simple stochastic modeling framework. As is intuitive, the pathogen strains most likely to emerge are those with the highest net reproductive rates (R0). We find, however, that stochastic events shape the properties of emerging pathogens in sometimes unexpected ways. First, the mean virulence of emerging pathogens is expected to be larger in dense host populations and/or when transmission is high, due to less restrictive conditions for the spread of the pathogen. Second, a positive correlation between average virulence and transmissibility emerges due to a combination of drift and selection. We conclude that at least in the initial phases of adaptation, special assumptions about constraints need not be invoked to explain some virulence-transmission correlations and that virulence management practices should consider how residual variation in transmission and virulence can be selected to reduce the prevalence and/or virulence of emerging infectious diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".