Investigation of a Measles Outbreak Identified by Front Line Polio Workers, District Shikarpur, Sindh - 2017
Bibliographic record
Abstract
Background: An innovative strategy; to identify the vaccine preventable disease cases from the community by the front-line polio workers during door to door OPV campaign, was adopted in district Shikarpur, Sindh. During polio field work training front line workers were also briefed about sign and symptoms of vaccine preventable diseases to identify and report their cases. On January 17, 2017 (1st day of OPV campaign) front line polio teams reported three measles cases including one death from a remote rural village. A team was deployed to confirm and determine the extent of the outbreak and implement preventive and control measures. Objective: To confirm and determine the extent of the outbreak. Methods: A case was defined as a child (= 15 years of age) residing in district Shiparpur, with: a generalized rash for three or more days, fever at or above 101°F, and one or more of the associated symptoms, including cough, or coryza, or conjunctivitis from 1st January to March 6, 2017. Active case finding was done from the community and health facilities. Blood samples from fourteen willing cases were collected and sent to NIH Islamabad for Laboratory diagnosis. Results: Results Twenty cases were identified with one death (CFR 5%), 55% (n=11) were females. Mean age was 43 months (range: 11 to 108 months). Eight (40%) cases were identified by front line polio workers from remote areas. Eight cases were from two families. Fourteen (70%) cases were un-immunized and 6 (30%) partially immunized against measles. NIH Lab declared thirteen (93%) cases positive out of fourteen. Conclusions: Cases appeared due to poor vaccine coverage. Mopping-up activities were conducted in the five villages with clustering of cases and in five KM radius surrounding areas. Innovative strategy of identify the vaccine preventable disease cases from the community by the front-line polio workers have proved successful and needs to be implemented across the country.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".