Multi-Host Pathogen Transmission and the Disease-Diversity Relationship
Bibliographic record
Abstract
Disease transmission in wildlife is linked to ecosystem and human health, and host community structure can mediate pathogen spread. Here, we decompose the well-known susceptible-infected (SI) compartmental models of disease transmission for multi-host communities to explore the disease-biodiversity relationship. We examine the parameters involved in interspecific transmission and link them to the multi-host outbreak potential, R0. We partition R0 into its separate elements that can be associated with either the recipient or donating host, and show how this allows us to identify maintenance versus spillover hosts, allowing us to quantify separate stages of spillover into a novel, recipient host. We further suggest that the evolutionary relatedness between donating and recipient host may play a role in the probability a pathogen establishes in a novel species, as illustrated by the observed phylogenetic signal in the host-breadth of multi-host pathogens. We discuss how anthropogenic changes to the environment and wildlife communities, such as the reduction and fragmentation of wildlife habitat, might alter the mode and dynamics of disease transmission. This discussion contributes to the ongoing debate on the relationship between disease incidence and biodiversity. Through a synthesis of classic ecological theory, we illustrate how pathogens can affect host populations through phenomena such as in/direct symbioses, that can save a pathogen from extinction or suppress competing host species, but we highlight how host diversity can simultaneosuly influence the survival and success of the pathogen, suggesting a complex feedback between hosts and parasites in multi-host communities. Our review provides a guide to common derivations of interspecific transmission, and underscores the critical connections between community and disease-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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".