Predictability of anthrax infection in the Serengeti, Tanzania
Bibliographic record
Abstract
Anthrax is endemic throughout Africa, causing considerable livestock and wildlife losses and severe, sometimes fatal, infection in humans. Predicting the risk of infection is therefore important for public health, wildlife conservation and livestock economies. However, because of the intermittent and variable nature of anthrax outbreaks, associated environmental and climatic conditions, and diversity of species affected, the ecology of this multihost pathogen is poorly understood.We explored records of anthrax from the Serengeti ecosystem in north-west Tanzania where the disease has been documented in humans, domestic animals and a range of wildlife. Using spatial and temporal case-detection and seroprevalence data from wild and domestic animals, we investigated spatial, environmental, climatic and species-specific associations in exposure and disease.Anthrax was detected annually in numerous species, but large outbreaks were spatially localized, mostly affecting a few focal herbivores.Soil alkalinity and cumulative weather extremes were identified as useful spatial and temporal predictors of exposure and infection risk, and for triggering the onset of large outbreaks.Interacting ecological and behavioural factors, specifically functional groups and spatiotemporal overlap, helped to explain the variable patterns of infection and exposure among species.Synthesis and applications. Our results shed light on ecological drivers of anthrax infection and suggest that soil alkalinity and prolonged droughts or rains are useful predictors of disease occurrence that could guide risk-based surveillance. These insights should inform strategies for managing anthrax including prophylactic livestock vaccination, timing of public health warnings and antibiotic provision in high-risk areas. However, this research highlights the need for greater surveillance (environmental, serological and case-detection-orientated) to determine the mechanisms underlying anthrax dynamics.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".