A framework for adaptive surveillance of emerging tick-borne zoonoses
Bibliographic record
Abstract
Significant global ecological changes continue to drive emergence of tick-borne zoonoses around the world. This poses an important threat to both human and animal health, and highlights the need for surveillance systems that are capable of monitoring these complex diseases effectively across different stages of the emergence process. Our objective was to develop an evidence-based framework for surveillance of emerging tick-borne zoonoses. We conducted a realist review to understand the available approaches and major challenges associated with surveillance of emerging tick-borne zoonoses. Lyme disease, with a specific focus on emergence in Canada, was used as a case study to provide real-world context, since the process of disease emergence is ongoing in this country. We synthesize the results to propose a novel framework for adaptive surveillance of emerging tick-borne zoonoses. Goals for each phase of disease emergence are highlighted and approaches are suggested. The framework emphasizes the needs for surveillance systems to be inclusive, standardized, comprehensive and sustainable. We build upon a growing body of infectious disease literature that is advocating for reform to surveillance systems. Although our framework has been developed for tick-borne zoonoses, it is flexible and has the potential to be applied to a variety of other vector-borne and zoonotic diseases.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".