Ebola Again Shows the International Health Regulations Are Broken
Bibliographic record
Abstract
Epidemics are among the greatest threats to humanity, and the International Health Regulations are the world's key legal instrument for addressing this threat. Since their revision in 2005, the IHR have faced two big tests: the 2009 H1N1 influenza pandemic and the 2014 Ebola epidemic in West Africa. Both exposed major shortcomings of the IHR, and both offered profound lessons for the future. The objective of this Article is twofold. First, we seek to compare the lessons learned from H1N1 and Ebola for reforming the IHR in order to test the hypothesis that they are similar. Second, we seek to examine the barriers to implementing these lessons and to identify strategies for overcoming those barriers. We find that the lessons from H1N1 and Ebola are indeed similar, and that opportunities to act on lessons from H1N1 were woefully missed. We identify many political barriers to global collective action and implementation of lessons for the IHR. On that basis, we describe strategies to overcome these barriers, which will hopefully be deployed now to reform the IHR before the policy window following Ebola closes, and before the inevitable next epidemic comes. The emerging threat of the Zika virus underscores that we have no time to waste.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".