Evaluation of ProMED-mail as an electronic early warning system for emerging animal diseases: 1996 to 2004
Bibliographic record
Abstract
OBJECTIVE: To identify emerging animal and zoonotic diseases and associated geographic distribution, disease agents, animal hosts, and seasonality of reporting in the Program for Monitoring Emerging Diseases (ProMED)-mail electronic early warning system. DESIGN: Retrospective study. SAMPLE POPULATION: 10,490 disease reports. PROCEDURES: Descriptive statistics were collated for all animal disease reports appearing on the ProMED-mail system from January 1, 1996, to December 31, 2004. RESULTS: Approximately 30% of reports concerned events in the United States; reports were next most common in the United Kingdom, Canada, Australia, Russia, and China. Rabies, bovine spongiform encephalopathy, and anthrax were reported consistently over the study period, whereas avian influenza, Ebola virus, and Hantavirus infection were reported frequently in approximately half of the study years. Reports concerning viral agents composed more than half of the postings. Humans affected by zoonotic disease accounted for a third of the subjects. Cattle were affected in 1,080 reports, and wildlife species were affected in 825 reports. For the 10,490 postings studied, there was a retraction rate of 0.01 and a correction rate of 0.02. CONCLUSIONS AND CLINICAL RELEVANCE: ProMED-mail provided global coverage, but gaps in coverage for individual countries were detected. The value of a global electronic reporting system for monitoring emerging diseases over a 9-year period illustrated how new technologies can augment disease surveillance strategies. The number of animal and zoonotic diseases highlights the importance of animals in the study of emerging diseases.
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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 | 0.026 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".