Building a better global health security early-warning system post-COVID: The view from Canada
Bibliographic record
Abstract
In the years following the Severe Acute Respiratory Syndrome (SARS) outbreak in 2002–2003, the World Health Organization (WHO) created a new system for global disease outbreak surveillance. The system relied on timely reporting by nation-states and gave the WHO a leading role in the global response. It also recognized the value of a multiplicity of sources of information, including from open-source media scanning. The post-SARS system faced its most significant task with the outbreak of the COVID-19 pandemic in the People’s Republic of China and its rapid spread in 2020. The WHO architecture for early warning of disease outbreaks arguably failed and gives rise to questions about how the international community can better respond to pandemic threats in future. This article explores the inter-connectedness of Canada’s system for global health surveillance, featuring the work of the Global Public Health Intelligence Network and that of the WHO, and argues that, while Canada has positioned itself as a global leader, much work needs to be done in Canada, and globally, if the concept of collective health security and shared early warning is to be maintained in the future.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".