Latest revisions to the International Health Regulations will fail to prevent future travel chaos
Bibliographic record
Abstract
The poor management of public health risks associated with travel by most countries proved among the most contentious issue areas during the COVID-19 pandemic. Evidence from previous outbreaks suggested travel restrictions were largely unnecessary and counterproductive to timely reporting. This led to initial WHO recommendations against the use of travel restrictions. Substantial evidence of the role of human travel in spreading SARS-CoV-2 worldwide throughout the evolving pandemic supported new thinking about the use of different types of travel measures (ie, screening, restrictions, quarantine, immunity documentation) to limit the introduction of SARS-CoV-2 into jurisdictions with low incidence and onward transmission. However, governments failed to work together, undermining public health goals. In addition, profound secondary impacts were caused by uncoordinated, frequently changing and poorly evidenced use of travel measures. Alongside the need to better understand what, when and how travel measures should be used during public health emergencies of international concern, improved global governance is required. Recently adopted revisions to the International Health Regulations (IHR), notably Article 43, failed to change current rules and commitments. Travel measures are also not being addressed in the negotiation of a pandemic agreement. Evolving evidence from COVID-19 supports a risk-based approach but global consensus on a standardised methodology remains needed. Setting aside further IHR revision, this methodology and guidelines could be advanced through a WHO technical working group. A risk-based decision instrument that incorporates pathogen and jurisdictional characteristics, and public health and social, political and economic risk analysis could then be developed as a new IHR annex.
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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 | 0.078 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.012 | 0.022 |
| Insufficient payload (model declined to judge) | 0.008 | 0.009 |
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".