Media scanning and verification system as a supplemental tool to disease outbreak detection & reporting at National Centre for Disease Control, Delhi.
Bibliographic record
Abstract
Media scanning for unusual health events can efficiently supplement conventional communicable disease surveillance systems for early detection and response to outbreaks. There is a need to rapidly process and appropriately disseminate the media reports on unusual health events for timely action. Hence to address this need in India a Media Scanning & Verification Cell (MSVC) was established in July 2008 at the National Centre for Disease Control, Delhi. MSVC is supervised by Epidemiologists working in Central Surveillance Unit of IDSP. This unique system monitors Global and National Media sources such as National and Regional print media, news on internet, news wires and websites, news channels and news shared by partners like Global Public Health Intelligence Network (GPHIN), Canada, WHO and other International and national agencies. The information is shared to the districts affected and District Surveillance Officer (DSO) and his team is expected to investigate and revert through the internet about the correctness and action taken. A mean number of 4 Media Alert reports are generated each day. A total of 1685 alerts were reported in a period between July 2008 to December 2011. Of these 1241 (73.7%) were verified as real events and 183 (10.9%) were considered outbreaks by local health officials. Most events were captured through internet (57%) followed by the print media (24%). The most common disease events identified were food-borne and diarrhea (29.1%), dengue (10.68%), influenza & respiratory disease (8.1%) and malaria (7.4%). The sensitivity of MSVC to detect outbreaks was 14.8% with more than half of outbreaks detected before they were identified by the conventional surveillance system. It has proven to be a highly effective supplemental tool to official surveillance system in the detection of early warning signals and hence timely detection and management of public health threats in India.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".