Estimation and management of pandemic influenza transmission risk at mass immunization clinics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Mass immunization clinics (MICs) have become an essential component of pandemic influenza response strategies. By deploying large volumes of vaccines at centralized locations, public health authorities can reduce the complexity of emergency vaccine distribution while also enabling rapid, large-scale vaccination. The risk of influenza transmission at MICs must be understood and mitigated to maximize their effectiveness. We have developed a discrete-event simulation of an MIC that can estimate the expected number of infections resulting from disease transmission within the facility. A simulation experiment is conducted that varies MIC crowdedness, staffing levels and the percentage of infectious individuals entering the MIC---symptomatic or not---to assess the impact of these factors on expected infections. It is shown that the number of expected infections occurring in the MIC, though a small fraction of the influenza cases likely averted due to vaccination, is large enough to warrant mitigation measures.
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.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it