Elevated viral recombination in short-lived hosts
Bibliographic record
Abstract
Abstract Recombination (including reassortment) is a salient force in viral evolution and has been implicated in the emergence of several zoonotic pathogens in humans. Viral recombination occurs during simultaneous infection of an individual host with multiple genotypes (co-infection). Thus, processes which affect the incidence of a disease in the host population affect how often viral genotypes recombine. We investigate whether and how host traits affect the realized rate of viral recombination using a mathematical model that makes feedbacks between viral evolution and host ecology (in particular, lifespan) explicit. Our main result is that viruses of host species that are short-lived tend to recombine more frequently than those of relatively long-lived hosts. This is because of differences in population density and, thus, the prevalence of (co-)infection at equilibrium. Using highly pathogenic avian influenza sequence data, we test the prediction that recombination is elevated in short-lived hosts. In agreement with this prediction, the magnitude of statistical associations between mutations on different segments of the flu genome increases with host body size, a proxy for lifespan. Similarly, estimates of the reassortment rate from phylogenetic network analyses decrease with body size. We discuss the implications of these findings for disease emergence. Subject category Evolution Subject areas evolution, health and disease and epidemiology, ecology
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".