Investigation of Cholera Outbreak at Rawalpindi, Pakistan - August 2017
Bibliographic record
Abstract
Background: Cholera is endemic in Pakistan with many outbreaks during the summer season. On July 29, 2017, two suspected cholera cases were reported from a tertiary care hospital in Rawalpindi. On the request of District Health Authorities, a team was constituted. Objective: To assess the magnitude of the outbreak, evaluate possible risk factors and recommend control measures. Methods: Investigation was carried out from Aug 01-15, 2017. Hospital records were reviewed, and active case-finding was conducted. A case was defined as sudden onset of loose watery stools (3 in past 24 hours) with any of the symptoms like vomiting, nausea, abdominal cramps or fever in a resident of Dhok-Paracha, Amarpura & Dhok-Chaudhriyan, Rawalpindi, from July 19-August 07, 2017. Age and sex-matched neighborhood controls were enrolled. Data was collected using a structured questionnaire. Four stool samples and three water samples were sent to National Institute of Health for microbiological analysis Results: A total of 30 cases with 02 deaths (CFR 2.2%) were identified out of which 28 cases were detected through active case-search. There was a male predominance (n=20, 66%) with mean age of 13.7 years (range: 02 months-55 years). Overall AR was 0.68% with 16-20 years being the most severely affected age group (AR 1.8%). Out of 30 cases, 14 were consuming well-water (OR 10.37, 95% CI 3.61-29.74) and 12 were consuming tap water (OR 3.94, 95% CI 1.54-10.08). Water samples showed presence of coliforms (240 CFU/100 ml). Vibrio Cholera Serotype Inaba isolated from stool samples. Heavy rainfall was recorded (455.5 ml) from June 26 to August 6, 2017. Conclusions: Consumption of contaminated water was the most probable cause of the outbreak. Contamination of water sources during recent flash floods was the source of contamination. Chlorination of water sources was conducted. Health awareness sessions on safe drinking water were conducted in the community.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".