Development of simulation models for transmission of Salmonid Rickettsial Septicaemia between salt water fish farms in Chile
Bibliographic record
Abstract
This study aimed at estimating parameters representing between-farm transmission of Salmonid Rickettsial Septicaemia (SRS) in Chile, and developing and validating simulation models to predict weekly spread of SRS between farms in Los Lagos (Region 10), using InterSpread Plus. The model parameters were estimated by analyses of the historical SRS outbreak data. The models incorporated time and distance-dependent transmission kernels, representing the probabilities of waterborne spread of SRS between farms. Seven candidate transmission kernels were estimated, with varying maximum distance of between-farm SRS spread (15-60 km). Farms were categorized by size (small; medium; large) and species (Coho salmon; Atlantic salmon; rainbow trout). The time that it took a farm to recover from infection was parameterized to be shortest for small Coho farms (median: 7 weeks), followed by medium and large Coho farms (median: 25 weeks), Atlantic salmon farms (median: 42 weeks, any size) and rainbow trout farms (median: 43 weeks, any size). The relative infectiousness parameters of rainbow trout farms were 1.5-6.3 times that of Coho or Atlantic salmon, or those of large farms was 1.3-4.2 times that of small or medium farms. The models predicted SRS prevalence in Region 10 between 2013 and 2015 (79 weeks) with 76.5%-93.0% overall accuracy. The model with a transmission kernel of <20 km (P20) achieved a maximum overall accuracy (93.0%). Within each neighbourhood, the accuracy of P20 varied between 32.4% and 88.1%; 13/20 neighbourhoods had a reasonable temporal agreement between the simulated and actual dynamics of SRS (within 5th-95th percentiles), but 5/20 neighbourhoods underestimated and 2/20 overestimated the SRS spread. The model could be used for evaluation of semi-global control policies in Region 10, while addition of other factors such as seasonality, ocean currents, and movement of infected fish may improve the model performance at a finer scale.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".