Canadian strategies for systematically assessing risks posed by emerging infectious diseases
Bibliographic record
Abstract
Purpose: How can the Canadian Government systematically assess and prioritize emerging infectious diseases (EID) in order to inform public health responses?Methods & Materials: The Public Health Agency of Canada (PHAC), an agency of the Government of Canada, is tasked to prevent and control infectious diseases and prepare for and respond to public health emergencies, amongst other core functions described within the mandate 1 .In recent years PHAC has been challenged to respond, in a timely manner, to a range of emerging infectious diseases and public health events.In order to enhance capacity the need for a systematic risk assessment platform for EIDs was identified.The creation of a Rapid Public Health Risk Assessment Framework (RPHRAF) involved a scan of existing frameworks in order to identify common themes and structures.A literature review was conducted to broaden the evidence base.A subject-matter working group then convened to provide feedback on a draft framework and the prototype was then tested in a tabletop exercises.Finally, the RPHRAF was piloted on several real-time EID events, including: H5N1, Ebola, Measles and the Zika virus scale-up.Results: The prototype framework proved to be effective and beneficial during the initial table-top and real-time tests.The framework promoted a shared understanding of risk assessments, a common vocabulary across a large government agency and response triggers for EID events.A subsequent real-time test during PHAC's scale-up for the Zika virus revealed some weaknesses relating to vector-borne diseases and incongruence between data inputs and algorithms and risk assessments.Conclusion: Drawing from the lessons learned during the testing phase of this framework, PHAC is now embarking on a revised version.The revisions will seek to make the framework more generic and capable of assessing a range of domestic and global EID events.As a part of this process PHAC will be developing systematic documentation standards and repositories, as well as event-specific risk scores.Canada is a Federal state, comprised of provinces, territories (PT) and the federal government.An additional requirement of the revised RPHRAF will be stakeholder input from our PT and nongovernmental counterparts to ensure inter-operability.
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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 | 0.111 | 0.174 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.034 | 0.026 |
| Science and technology studies | 0.018 | 0.012 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.010 | 0.020 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".