Evaluation of a Dengue Surveillance Control Program, Yemen, Hodeiadah (2021)
Bibliographic record
Abstract
Background The number of dengue cases reported to the World Health Organization (WHO) increased over 8-fold over the past 2 decades, from 2.4 million in 2010 to 4.2 million in 2019. In Yemen, from January to December 2019, 59,486 suspected dengue cases and 219 deaths with a case fatality rate (CFR) of 0.4% were reported. The dengue surveillance system (DSS) provides necessary information for outbreak response. Objective As there was an increase in the number of dengue outbreaks, especially in Hodeida, last year, this study aims to evaluate the DSS between January and March 2021 to assess its usefulness and performance and identify its strengths and weaknesses. Methods We used the Centers for Disease Control and Prevention (CDC) updated guidelines for evaluation of surveillance systems. For data collection, desk review and interviews with stakeholders at a central level were conducted and semistructured questionnaires distributed for the sentinel site’s coordinators. Indicators were developed to evaluate the usefulness based on 8 attributes: flexibility, stability, simplicity, acceptability, sensitivity, data quality, representativeness, and overall performance. The score percentage was calculated and interpreted as poor (<60%), average (60% to <80%), or good (≥80%). Results The DSS was found to be useful (ie, using data for detecting changes in trends in morbidity and mortality). Regarding system attributes, flexibility (22.7%), stability (33.3%), sensitivity (76%), and data quality (31%) were poor, while simplicity (79%), acceptability (76%), and representativeness (65%) were average. The overall DSS performance was poor (47%). Conclusions The DSS was useful. Although acceptability and representativeness were average, flexibility, stability, sensitivity, and data quality were poor. Strengthening the DSS by providing basic infrastructure, ensuring sustainability, improving supplements, supervising laboratory testing for dengue fever, and expanding DSS coverage to include private health care facilities are necessary. For data quality, supervision and training are recommended.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.010 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".