Identifying co-phylogenetic hotspots for zoonotic disease
Bibliographic record
Abstract
The incidence of zoonotic diseases is increasing worldwide, which makes identifying parasites likely to become zoonotic and hosts likely to harbour zoonotic parasites a critical concern. Prior work indicates that there is a higher risk of zoonotic spillover accruing from closely related hosts and from hosts that are infected with a high phylogenetic diversity of parasites. This suggests that host and parasite evolutionary history may be important drivers of spillover, but identifying whether host-parasite associations are more strongly structured by the host, parasite or both requires co-phylogenetic analyses that combine host-parasite association data with host and parasite phylogenies. Here, we use host-parasite datasets containing associations between helminth taxa and free-range mammals in combination with phylogenetic models to explore whether host, parasite, or both host and parasite evolutionary history influences host-parasite associations. We find that host phylogenetic history is most important for driving patterns of helminth-mammal association, indicating that zoonoses are most likely to come from a host's close relatives. More broadly, our results suggest that co-phylogenetic analyses across broad taxonomic scales can provide a novel perspective for surveying potential emerging infectious diseases. This article is part of the theme issue 'Infectious disease macroecology: parasite diversity and dynamics across the globe'.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".