Enforcement of Functionality and Effectiveness of Event-Based Surveillance System (EBS), Egypt, April-September 2017
Bibliographic record
Abstract
Background: To meet the requirements of International Health Regulations 2005 (IHR), initiation of EBS is essential to complement the Indicator Based Surveillance (IBS) and to boost early detection of potential Public health threats. In November 2015, the Ministry of Health (MoH) launched the EBS at the central level. The hot line in emergency room and active browsing for electronic media are the main sources of information. Objective: To enhance early detection and rapid response to potential public health threats and to improve the performance of the EBS team. Methods: At the end of June 2017, the reporting form, database and the standard Operating Procedures (SOPs) of EBS were updated. EBS team received training for the system updates. Data from April to September was investigated for the source of information, signal filtration, verification, and timeliness of response. For the events that are routinely reported by IBS, early detection was checked for both systems. The results were compared three months before and after the training. Results: Out of 762 raw signals, 199 events were detected, from which 65% (130/199) events after June. Proportion of events captured by electronic media, health care workers and community were 75% (150/199), 20% (40/199) and 5% (9/199) respectively. Filtration of signals varied significantly from 14 % to 51% (P value < 0.0001). Verification improved from 78% to 86 % (P value= 0.154). Positive Predictive Value (PPV) significantly differed from 80% to 93% (P value = 0.012). Rapid response within 24 hours significantly improved from 53% to 88% (P value < 0.0001). Capability of EBS to capture events Preceding IBS improved from 27% (9/33) to 38% (32/85) (P value= 0.288). Conclusions: Increase EBS capacity for early detection of potential public health threats. Marked improvement of EBS team performance. Raise community awareness, expand the sources of information and shift to electronic database are highly recommended.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".