Mathematical Models for Estimating the Risks of Bovine Spongiform Encephalopathy (BSE)
Bibliographic record
Abstract
When the bovine spongiform encephalopathy (BSE) epidemic first emerged in the United Kingdom in the mid 1980s, the etiology of animal prion diseases was largely unknown. Risk management efforts to control the disease were also subject to uncertainties regarding the extent of BSE infections and future course of the epidemic. As understanding of BSE increased, mathematical models were developed to estimate risk of BSE infection and to predict reductions in risk in response to BSE control measures. Risk models of BSE-transmission dynamics determined disease persistence in cattle herds and relative infectivity of cattle prior to onset of clinical disease. These BSE models helped in understanding key epidemiological features of BSE transmission and dynamics, such as incubation period distribution and age-dependent infection susceptibility to infection with the BSE agent. This review summarizes different mathematical models and methods that have been used to estimate risk of BSE, and discusses how such risk projection models have informed risk assessment and management of BSE. This review also provides some general insights on how mathematical models of the type discussed here may be used to estimate risks of emerging zoonotic diseases when biological data on transmission of the etiological agent are limited.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".