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Record W2124877505 · doi:10.1086/524382

Evaluating Public Health Responses to Reintroduced Smallpox via Dynamic, Socially Structured, and Spatially Distributed Metapopulation Models

2008· article· en· W2124877505 on OpenAlexfundno aff
John W. Glasser, Stanley O. Foster, J. Donald Millar, J. Michael Lane

Bibliographic record

VenueClinical Infectious Diseases · 2008
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsnot available
FundersCenters for Disease Control and PreventionAGE-WELL
KeywordsVaccinationMetapopulationMedicineSmallpoxImmunizationEnvironmental healthPopulationHealth carePublic healthImmunologyDemographyNursingImmune systemEconomic growth

Abstract

fetched live from OpenAlex

The risk of smallpox reintroduction has motivated preparations in potential target countries. After reproducing the spatiotemporal pattern after the 1972 importation into Yugoslavia via coupled, biologically realistic systems of ordinary differential equations, we developed dynamic population models with current US age distributions and typical spatially distributed social structures. Surveillance and containment (S&C) coupled with vaccination of 95% of hospital-based health care workers (HCWs) within 2 days after the first diagnosis (estimated to be 18 days after aerosol release) were modeled after simulated exposure of 10, 50, or 10,000 people in various settings. If 90% of patients were isolated within days after symptom onset and 75% of contacts were vaccinated and monitored, S&C would reduce cases by 82%-99%. Preemptive immunization of HCWs, closing of schools, and even vaccination of as many as 80% within 1 week would have small marginal benefits. Preparations should emphasize stockpiling vaccine, training HCWs, improving laboratory capacity, and fostering an understanding of S&C.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.225
GPT teacher head0.481
Teacher spread0.256 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations14
Published2008
Admission routes1
Has abstractyes

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