Event-based surveillance in middle- and low-income countries: An evidence map
Bibliographic record
Abstract
Event-based surveillance is an important strategy for the early detection of outbreaks of all types of diseases, especially in low- and middle-income countries. This research focuses on an evidence map, which systematizes and graphically represents the information gathered on the effectiveness of various interventions in these contexts. Key interventions include data quality, training, communication, multisectoral collaboration, timeliness, mortality and morbidity reduction, cost-effectiveness, early response to events, sensitivity, signals, and usefulness for real events. In this study, a review and evaluation of the literature was conducted on a total of 22 systematic reviews; 15 met the inclusion criteria, containing a total of 82 open-access primary articles. The quality of the evidence was assessed using the AMSTAR tool, identifying reviews with high, medium, and low reliability. The results show that event-based surveillance has been successfully implemented in countries such as the United States, Brazil, China, Australia, Canada, India, Japan, New Zealand, Taiwan, the Netherlands, the United Arab Emirates, and others. From the evidence gathered in these countries, it is clear that event-based surveillance improves early outbreak detection, alert response, and minimizes the spread of diseases. Further research and improvement of these strategies are needed for effective early detection and response to public health events.
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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 | 0.028 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.028 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".