Delays in Global Disease Outbreak Responses: Lessons from H1N1, Ebola, and Zika
Bibliographic record
Abstract
In global disease outbreaks, there are significant time delays between the source of an outbreak and collective action. Some delay is necessary, but recent delays have been extended by insufficient surveillance capacity and time-consuming efforts to mobilize action. Three public health emergencies of international concern (PHEICs)-H1N1, Ebola, and Zika-allow us to identify and compare sources of delays and consider seven hypotheses about what influences the length of delays. These hypotheses can then motivate further research that empirically tests them. The three PHEICs suggest that deferred global mobilization is a greater source of delay than is poor surveillance capacity. These case study outbreaks support hypotheses that we see quicker responses for novel diseases when outbreaks do not coincide with holidays and when US citizens are infected. They do not support hypotheses that we see quicker responses for more severe outbreaks or those that threaten larger numbers of people. Better understanding the reason for delays can help target policy interventions and identify the kind of global institutional changes needed to reduce the spread and severity of future PHEICs.
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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 | 0.008 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".