Chronic infections can shape epidemic exposure: Pathogen co-occurrence networks in the Serengeti lions
Bibliographic record
Abstract
Abstract Pathogens are embedded in a complex network of microparasites that can collectively or individually alter disease dynamics and outcomes. Chronic pathogens, for example, can either facilitate or compete with subsequent pathogens thereby exacerbating morbidity and mortality. Pathogen interactions are ubiquitous in nature, but poorly understood, particularly in wild populations. We report here on ten years of serological and molecular data in African lions, leveraging comprehensive demographic and behavioral data to utilize pathogen networks to test if chronic infections shape infection by acute pathogens. We combine network and community ecology approaches to assess broad network structure and characterize associations between pathogens across spatial and temporal scales. We found significant non-random structure in the lion-pathogen co-occurrence network and identified potential facilitative and competitive interactions between acute and chronic pathogens. Our results provide a novel insight for untangling the complex associations underlying pathogen co-occurrence networks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".