Electronic Integrated Disease Early Warning System Surveillance System Evaluation, Sana'a Capital, Yemen, 2021
Bibliographic record
Abstract
Background The Electronic Integrated Disease Early Warning System (eIDEWS) is an essential system; it contributes to the better prevention and management of epidemics. Through the collection of complete, accurate, and timely data, countries are able to determine the priorities for suitable interventions that save the lives of communities. Regardless of the conflict in Yemen, the system is still functioning and is expanding to be the most effective epidemiological surveillance program. Objective We aimed to determine the usefulness of the eIDEWS, assess its performance, and identify the strengths and weaknesses of its implementation. Methods The usefulness and performance attributes of the eIDEWS were evaluated using the Centers for Disease Control and Prevention’s updated guidelines for evaluating public health surveillance systems. The evaluation was carried out in Sana’a capital from January to March 2021 by interviewing 25 stakeholders at 3 levels—the central, governorate, and health district levels—and using a semistructured questionnaire. Attributes of the system were ranked as poor (<60%), average (60% to <80%), good (80% to <90%), and excellent (≥90%) on the basis of indicators to calculate the final scores. Results The eIDEWS’ overall usefulness and performance score was 90%—an excellent rank. The mean score of system attributes was 100% for acceptability, completeness, and timeliness. The flexibility was good (83%), since the change in reporting method was applied difficultly. The system depends completely on foreign funds; thus, the system’s stability was average (75%). However, the eIDEWS was expanded recently to add new health facilities; its representativeness was average (76%). Conclusions The system is working effectively at evaluated sites. The overall system performance was excellent; however, flexibility and stability were good due to the negative adaptation of the system with regard to the reporting method and the absence of other fund resources. Therefore, evaluating the newly upgraded system, strengthening its stability by finding other supporting resources, and further expanding coverage to include all public and private health care facilities are recommended.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".