Surveillance Evaluation for Severe Acute Respiratory Infection, Sana'a city, Yemen, 2021
Bibliographic record
Abstract
Background Due to the war and limited access to health facilities, the surveillance of severe acute respiratory infection (SARI) has been expanded to include all hospitals since 2017. Objective We aimed to assess the usefulness of SARI surveillance in Sana’a city and to assess its performance in terms of attributes. Methods The Centers for Disease Control and Prevention’s updated guideline was used for evaluating surveillance systems. Four qualitative attributes, including stability, simplicity, flexibility, and acceptability, and data quality as a quantitative attribute were assessed. An in-depth interview with stakeholders at the central level and self-administered questionnaires with 5 Likert scales and a register review at the peripheral level were used for collecting data. Scores for indicators were used to calculate the total gained scores for each attribute and percentages for ranking them as poor (<60%), average (60% to <80%), good (80% to <90%), and excellent (≥90%). Results SARI surveillance was useful and obtained a total gained score of 94%. The overall performance of the five attributes was average (64%). It was good (82%) at the central level where flexibility was excellent (93%) and stability was average (72%). The performance at the peripheral level was poor (51%); simplicity (61%) and acceptability (74%) were average, and the data quality was poor (20%). Conclusions Expanding SARI surveillance with a lack of staff training, central communication, and supervision might be the main reason for its weak performance at the peripheral level. Supporting SARI program activities and selecting SARI reporting sites and the surveillance team at each site based on World Health Organization criteria are highly 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".