Cost‐effectiveness of longitudinal surveillance for <i>Piscirickettsia salmonis</i> using qPCR in Atlantic salmon farms (<i>Salmo salar</i>) in Chile
Bibliographic record
Abstract
Costs of diagnostic testing including sample collection, sampling frequency and sample size are an important consideration in the evaluation of the economic feasibility of alternative surveillance strategies for detection of infectious diseases in aquatic animals. In Chile, Piscirickettsia salmonis is the primary reason for antibiotic treatments in farmed Atlantic salmon. In 2012, a surveillance and control programme for piscirickettsiosis was established with an overall goal of reducing antibiotic use. The present study estimated the cost-effectiveness of different sampling frequencies and sample sizes to achieve at least 95% confidence of early detection of P. salmonis at the netpen and farm levels using a validated qPCR test. We developed a stochastic model that incorporated variability in test accuracy, within-pen prevalence and sampling costs. Our findings indicated that the current piscirickettsiosis surveillance programme based on risk-based sampling of five moribund or dead fish from 2 to 3 netpens is cost-effective and gives a high probability of detection of P. salmonis in Atlantic salmon farms in Chile at both the netpen and farm levels. Results from this study should incentivize salmon farmers to establish cost-effective strategies for early detection of P. salmonis infection and the application of this approach to other highly infectious diseases.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".