Dead or alive: animal sampling during Ebola hemorrhagic fever outbreaks in humans
Bibliographic record
Abstract
There are currently no widely accepted animal surveillance guidelines for human Ebola hemorrhagic fever (EHF) outbreak investigations to identify potential sources of Ebolavirus (EBOV) spillover into humans and other animals. Animal field surveillance during and following an outbreak has several purposes, from helping identify the specific animal source of a human case to guiding control activities by describing the spatial and temporal distribution of wild circulating EBOV, informing public health efforts, and contributing to broader EHF research questions. Since 1976, researchers have sampled over 10,000 individual vertebrates from areas associated with human EHF outbreaks and tested for EBOV or antibodies. Using field surveillance data associated with EHF outbreaks, this review provides guidance on animal sampling for resource-limited outbreak situations, target species, and in some cases which diagnostics should be prioritized to rapidly assess the presence of EBOV in animal reservoirs. In brief, EBOV detection was 32.7% (18/55) for carcasses (animals found dead) and 0.2% (13/5309) for live captured animals. Our review indicates that for the purposes of identifying potential sources of transmission from animals to humans and isolating suspected virus in an animal in outbreak situations, (1) surveillance of free-ranging non-human primate mortality and morbidity should be a priority, (2) any wildlife morbidity or mortality events should be investigated and may hold the most promise for locating virus or viral genome sequences, (3) surveillance of some bat species is worthwhile to isolate and detect evidence of exposure, and (4) morbidity, mortality, and serology studies of domestic animals should prioritize dogs and pigs and include testing for virus and previous exposure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".