Options for Decentralized Testing of Suspected Secondary Outbreaks of Foot-and-mouth Disease
Bibliographic record
Abstract
This article reviews the options for use of virus detection techniques for decentralized testing of samples from suspected secondary outbreaks of foot-and-mouth disease (FMD). These options have been expanded by the advent of new tests including disposable lateral flow devices (LFDs) that detect viral proteins and portable RT-PCR equipment that detects viral RNA. LFDs have been developed with similar sensitivity to antigen detection ELISA but with the ability to provide a result 1-30 min after the addition of epithelium or vesicular fluid. Portable RT-PCR platforms are being developed that can detect FMD viral RNA in blood, epithelium or other materials with minimal sample processing and with high sensitivity, in as little as 60 min in some cases. These devices may be used on infected farms as pen-side tests, in regional, local or mobile laboratories, or in National Reference Laboratories (NRL). Advantages and disadvantages of different testing options are considered to inform decisions on the optimal strategies for different national circumstances. Issues include validation and quality control, containment needs, availability of test devices and reagents, the decision tree for declaring an outbreak, training issues and provision of samples for subsequent viral characterization. Tests to confirm the diagnosis of the index case of an outbreak of FMD should continue to be carried out in the NRL.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".