A framework for predicting zoonotic hosts using pseudo-absences: the case of Echinococcus multilocularis
Bibliographic record
Abstract
Identifying the host range of zoonotic parasites is challenging due to limited data and sampling biases. In particular, while more information exists for susceptible hosts, data on resistant species is extremely scant. Echinococcus multilocularis (Leuckart, 1863) (Cestoda: Taeniidae) is the causative agent of alveolar echinococcosis, one of the most significant food-borne zoonoses worldwide. Using data on susceptibility and competence of Holarctic cricetid and murid rodents, key intermediate hosts for E. multilocularis , we developed models to predict the likelihood of infection for any rodent species in the Holarctic. These models incorporated morphological and ecological characteristics and employed two approaches: Generalized Linear Models (GLM) and Presence-Unlabeled Learning (PU-L), a machine learning technique. To train the models, we defined pseudo-absences based on the bias in research effort. We compared the two algorithms and selected GLM as the most effective, using it to map potentially susceptible rodent species across phylogeny and geographic space. Predictions identified several potentially unreported hosts, suggesting that the current understanding of E. multilocularis host distribution may underestimate the true risk. The predicted richness of intermediate hosts peaked in Central-Eastern Europe, Western North America and Central Asia, while the ratio of predicted hosts to total rodent richness was highest in the northern latitudes and the Tibetan Plateau. The average temperature in the geographic range and range size emerged as the strongest predictors of host susceptibility. The workflow demonstrates promise for application to other host-parasite systems with unknown host ranges.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| 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".