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Record W2917576418 · doi:10.31646/gbio.10

Exercise Mataika: White Paper on response to a smallpox bioterrorism release in the Pacific

2019· article· en· W2917576418 on OpenAlexaboutno aff
C. Raina MacIntyre, David Heslop, Devina Nand, Craig M. Schramm, Michael Butel, William D. Rawlinson, Michael G. Baker, Tomasz Kiedrzynski, Cassidy Nelson, Alexander Rosewell, Lauasa Fotualii, Kevin Yeo, Jesper Elsgaard, Louise Fonua, John Lane

Bibliographic record

VenueGlobal Biosecurity · 2019
Typearticle
Languageen
FieldMedicine
TopicZoonotic diseases and public health
Canadian institutionsnot available
Fundersnot available
KeywordsSmallpoxPreparednessContext (archaeology)Public healthPolitical scienceTerrorismBiological warfareChristian ministryPublic relationsExcellenceBusinessEnvironmental healthMedicineGeographyLawVirologyVaccinationNursing

Abstract

fetched live from OpenAlex

Smallpox was declared eradicated in 1980, with known seed stock retained in two high security Biosafety Level 4 laboratories in the United States and Russia. Experts agree the likelihood of theft from these laboratories is low, and that synthetic creation of smallpox is a theoretical possibility. Until 2017 it was believed that synthetic smallpox was technically too complex a task to be a serious threat. However, in 2017, Canadian scientists synthesised a closely related orthopoxvirus, horsepox, using mail order DNA and $100,000. Simultaneously, terrorist groups have declared intent to conduct biological attacks. In this context an exercise was held on August 16th 2018, with international and cross-sectoral stakeholders to review preparedness for a bioterrorism attack in the Asia-Pacific region and globally. The exercise was conducted by The National Health and Medical Research Council (NHMRC) Centre for Research Excellence, Integrated Systems for Epidemic Response, with contextual input from the Ministry of Health and Medical Services Fiji. The scenario involved a deliberate release in Fiji, followed by a larger release in a more populous Asian country. Mathematical modelling was used to underpin epidemic projections under different conditions. The exercise alternated between clinical, public health, emergency and societal responses, with participants making real-time decisions on cross-sectoral response across the region and the world. Key weak points which are influential in determining the final size and impact of the epidemic were identified (based on mathematical modelling of transmission in Fiji and globally). We identified potential gaps in preparedness for smallpox and factors which influence the severity of a smallpox epidemic. This included identifying which determinants of epidemic size are potentially within our control, and which are not. Influential factors within our control include: preventing an attack through intelligence, law enforcement and legislation; 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); protecting first responders; operational support and logistics; social mobilisation and risk communication. Based on discussion at the workshop between diverse stakeholders, recommendations were made to guide improved prevention, mitigation and rapid response, thus providing a holistic, cross-sectoral framework for prevention of a worst-case scenario smallpox pandemic.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.445
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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.

Opus teacher head0.010
GPT teacher head0.278
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2019
Admission routes1
Has abstractyes

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