Role of Rapid Response Teams in Response to Outbreaks in Yemen, 2020: Descriptive Study
Bibliographic record
Abstract
Background Yemen has been increasingly reporting public health emergencies (eg, cholera). The Ministry of Public Health and Population (MoPH&P) has put in place the Rapid Response Teams (RRTs) mechanism from the national to district level to investigate and initiate the response to public health emergencies. An RRT is a technical, multidisciplinary team that is readily available for quick mobilization and deployment in case of emergencies. Objective The aim of this analysis was to summarize the role of RRTs in response to outbreaks in Yemen during 2020. Methods Data were obtained from the electronic Diseases Early Warning System (eDEWS) in Excel format covering the period from January to December 2020, including governorates, diseases, and other variables. Data were cleaned and analyzed using Excel 2013. Qualitative data are summarized as percentages. Data are presented using tables, graphs, and maps. Results A total of 39,451 field descents were performed. Nearly half of the activities (n=18,565, 47.06%) were for outbreak investigation of various infectious diseases, including cholera (n=9030), severe acute respiratory infection (n=1949), diphtheria (n=1532), measles (n=1328), malaria (n=1012), dengue fever (n=1008), pertussis (n=803), mumps (n=676), chickenpox (n=583), acute flaccid paralysis (n=482), and meningitis (n=162). Approximately 1747 (4.43%) supervision visits were implemented. Regarding health education, 19,139 (48.51%) health education sessions were executed, with 3419 (17.86%) performed at health facilities and 15,720 (82.14%) performed outside health facilities (eg, schools and outdoors). A total of 559,805 people attended the health education sessions. Conclusions RRTs support the MoPH&P in reducing or “slowing down” disease transmission as quickly as possible through various activities such as outbreak investigations and health education. Therefore, there is a strong need to continue supporting the RRTs financially and logistically by donors. In addition, governmental financial support to the RRTs is highly recommended to ensure the sustainability of the program.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".