Adapting existing models of highly contagious diseases to countries other than their country of origin
Bibliographic record
Abstract
Many countries do not have the resources to develop epidemiological models of animal diseases. As a result, it is tempting to use models developed in other countries. However, an existing model may need to be adapted in order for it to be appropriately applied in a country, region, or situation other than that for which it was originally developed. The process of adapting a model has a number of benefits for both model builders and model users. For model builders, it provides insight into the applicability of their model and potentially the opportunity to obtain data for operational validation of components of their model. For users, it is a chance to think about the infection transmission process in detail, to review the data available for modelling, and to learn the principles of epidemiological modelling. Various issues must be addressed when considering adapting a model. Most critically, the assumptions and purpose behind the model must be thoroughly understood, so that new users can determine its suitability for their situation. The process of adapting a model might simply involve changing existing model parameter values (for example, to better represent livestock demographics in a country or region), or might require more substantial (and more labour-intensive) changes to the model code and conceptual model. Adapting a model is easier if the model has a user-friendly interface and easy-to-read user documentation. In addition, models built as frameworks within which disease processes and livestock demographics and contacts are flexible are good candidates for technology transfer projects, which lead to long-term collaborations.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".