Outbreak Investigation of Dengue Fever in Water Scarce District Tharparkar of Pakistan, 2016
Bibliographic record
Abstract
Background: On 8th December 2016, 43 cases of dengue fever were reported from Tharparkar to Director General Health Office. The very next day FELTP fellows were assigned to investigate the outbreak. Objective: Objectives were to assess the magnitude, evaluate the risk factors and recommend control measures. Methods: Review of hospital records and active case finding was done. A descriptive followed by a case control study was conducted in December 2016. A case was defined as acute fever more than 102°F lasting >3 days, plus a positive NS-1 test in a resident of Tharparkar during September to December 2016. During entomological survey objects containing water were sampled and investigated for presence of larvae or pupa. The collected vectors were examined for species identification. Results: A total of 254 cases were identified (211 by active case finding) with 73% males. Overall attack rate (AR) was 0.02 with 10-14 years being the most affected age group (AR=0.03). Out of 254 cases, 79%(n=201) had indoor water receptacles (OR 32, CI 19.6-54 with P<0.00), 61% (n=155) had potted plants inside the house (OR 8, CI 5-13, p value < 0.00), and 46% (n=118) had outdoor water receptacles (OR 3, CI 2-4 with p value < 0.00) whereas intact window nets 52% of cases (n=132) (OR 0.44, CI 0.02-0.08, P value < 0.00) were found protective against getting the dengue infection. Total 2616 Aedes larvae-(58.3 per dip) and 423 pupae-(8 per dip) were collected by 320 dips. Among 152 houses 182 breeding sites were identified. Adult Aedes were found in 12 of 230 rooms. Conclusions: The outbreak was likely caused by presence of vector breeding sites inside and outside the house. On the recommendation of the study, health authorities initiated health awareness sessions and promoted mechanical control of breeding sites as well as use of windows net.
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 | 0.001 |
| 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 | 0.001 |
| 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".