A framework for adaptive surveillance of emerging tick-borne zoonoses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.002 | 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 it