A Portrait of Sentinel Surveillance Networks for Vector-Borne Diseases: A Scoping Review Supporting Sentinel Network Design
Bibliographic record
Abstract
Vector-borne diseases (VBDs) are continuing to emerge globally, requiring new surveillance systems to follow increasing VBD risk for human populations. Sentinel surveillance is an approach that allows tracking of disease risk through time using limited resources. However, there is no consensus on how best to design a sentinel surveillance network in the context of VBDs. We conducted a scoping review to compare VBD sentinel surveillance systems worldwide with the aim of identifying key design features associated with effective networks. Overall, VBD surveillance networks were used most commonly for malaria, West Nile virus, and lymphatic filariasis. A total of 45 criteria for the selection of sentinel unit location were identified. Risk-based criteria were the most often used, and logistic regression showed that using risk-based criteria dependent on host animals is particularly correlated with surveillance system sensitivity (p < 0.018). We identify tools that could prove valuable for sentinel surveillance network design, including a standardized approach for evaluating surveillance systems and a tool to prioritize criteria for selecting optimal geographic locations for spatial sentinel units.
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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 | 0.015 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".