Pacific Eclipse – A tabletop exercise on smallpox pandemic response
Bibliographic record
Abstract
BACKGROUND: In December 2019, we ran Pacific Eclipse, a pandemic tabletop exercise using smallpox originating in Fiji as a case study. Pacific Eclipse brought together international stakeholders from health, defence, law enforcement, emergency management and a range of other organisations. AIM: To review potential gaps in preparedness and identify modifiable factors which could prevent a pandemic or mitigate the impact of a pandemic. METHODS: Pacific Eclipse was held on December 9-10 in Washington DC, Phoenix and Honolulu simultaneously. The scenario began in Fiji and becomes a pandemic. Mathematical modelling of smallpox transmission was used to simulate the epidemic under different conditions and to test the effect of interventions. Live polling, using Poll Everywhere software that participants downloaded onto their smart phones, was used to gather participant decisions as the scenario unfolded. Stakeholders from state and federal government and non-government organisations from The United States, The United Kingdom, Australia, New Zealand, Canada, as well as industry and non-government organisations attended. RESULTS: The scenario progressed in three phases and participants were able to make decisions during each phase using live polling. The polling showed very diverse and sometimes conflicting decision making. Factors influential to pandemic severity were identified and categorised as modifiable or unmodifiable. A series of recommendations were made on the modifiable determinants of pandemic severity and how these can be incorporated into pandemic planning. These included preventing an attack through intelligence, law enforcement and legislation, improved speed of diagnosis, speed and completeness of case finding and case isolation, speed and security of vaccination response (including stockpiling), speed and completeness of contact tracing, protecting critical infrastructure and business continuity, non-pharmaceutical interventions (social distancing, PPE, border control) and protecting first responders. DISCUSSION: Pacific Eclipse illustrated the impact of a pandemic of smallpox under different response scenarios, which were validated to some extent by the COVID-19 pandemic. The framework developed from the scenario draws out modifiable determinants of pandemic severity which can inform pandemic planning for the ongoing COVID-19 pandemic and for future pandemics.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.004 |
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".