Bibliographic record
Abstract
When pneumonic plague struck Surat, Gujarat, India in 1994, thousands of people panicked and fled city. We were sitting in Ottawa watching CNN showing pictures of people fleeing, said Dr Ron St John, Director-General of Centre for Emergency Preparedness and Response at Public Health Agency of Canada, recalling how news media were first source of information on outbreak for public health officials, including those at WHO. The slow response to that outbreak and an Ebola outbreak in Kikwit in Democratic Republic of Congo following year led to panic and unnecessary deaths. We had no capacity to respond to outbreaks on our own, or even to deal with information coming in. All we had was a fax machine. The switchboards at WHO were completely overwhelmed, said Dr David Heymann, acting Assistant Director-General for Communicable Diseases and WHO Director-General's Representative for Pandemic Influenza. New techniques were clearly needed to respond to disease threats, such as pandemic influenza, that could kill millions. In 1996, then director-general, Dr Hiroshi Nakajima, asked Heymann to set up a new emerging infectious disease programme to deal with outbreaks. In age of real-time electronic media and television, journalists became a vital source of instant information that public health authorities could use to detect outbreaks, in addition to information from governments, nongovernmental organizations and health-care workers, said Dr Thomas Grein, Medical Officer, Alert and Response Operations at WHO. The question was: how to search through maze of thousands of reports filed by journalists every day? The answer came from Canada. St John linked up with Dr Rudi Nowak from Health Canada, Canadian health ministry, and they proposed development of a computerized system that would collect raw news feeds from international news agencies such as Agence France Presse, Associated Press and Reuters, and scan these feeds automatically to find news of disease outbreaks. Development work on software began in 1997 funded by a Canadian government grant of 800 000 Canadian dollars (US$ 500 000 at time). The news filtering system St John and Nowak developed, known as Global Public Health Intelligence Network (GPHIN, pronounced G-finn), went live in 1999 and scanned news feeds in English and French. We were astounded at how much information we could get, said St John, adding that system collects thousands of reports everyday. But information had to be verified, and incorrect information discarded. Nowak came to WHO in Geneva for two years to work with Heymann, Dr Guenael Rodier, Special Adviser for Communicable Diseases to Regional Director, WHO Regional Office for Europe, and Dr Mike Ryan, Director of Department of Epidemic and Pandemic Alert and Response, in establishing a team that would be responsible for verification. Every weekday morning at 9am about 20 members of that team meet in Strategic Health Operations Centre, nicknamed the SHOC room, at WHO's headquarters, to discuss outbreak reports that have come in and which ones need to be verified, by contacting WHO country offices, which, in turn, contact their host governments. The reports are also analysed by relevant crisis centres set up at WHO's regional offices. Ryan and his team set up Global Outbreak Alert and Response Network of 126 institutions across globe. Its experts can be despatched to field, where they work together with WHO staff from regional offices and headquarters to stem any outbreaks. Before WHO and other subscribers receive reports from GPHIN, these have been screened by a team of eight public health specialists in Canada. The SHOC was opened in May 2004 with funding from United States. After death of WHO's Director-General Dr LEE Jong-wook in May 2006, it was renamed JW Lee Centre for Strategic Health Operations. …
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.041 | 0.016 |
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".