Mathematical modelling to inform the national seasonal influenza vaccination policy: findings from our study
Bibliographic record
Abstract
In 2017 the Commonwealth Department of Health commissioned a consortia from the WHO Influenza Centre Melbourne, Telethon Kids Institute, University of Hong Kong and the University of Western Australia to (1) determine the influenza health burden in Australia and (2) using mathematical modelling, determine the effectiveness of alternative vaccination strategies which may significantly reduce the burden. The Phase 1 epi study used data from most states and territories averaged over 10 years (2007 to 2016), giving the relationship between case numbers and hospitalisation and mortality rates. This phase also determined current vaccination coverage by age class, giving a baseline vaccination scenario. The modelling study involved developing individual-based (c.f. agent based) models for Albany, Newcastle and Cairns, with an overall population of ~400,000, following methods developed previously at UWA. The models were applied separately to determine the effectiveness of potential changes to Australia's current influenza vaccination "profile". These included increased vaccination coverage to at-risk groups, to school-age children, use of enhanced vaccines to those aged 65 and above, and LAIV replacing QIV for those aged 3 to 17 years; a total of 60 scenarios were evaluated. These results were then scaled to an Australian population of ~24. 7 million. The effectiveness of each alternative vaccination strategy was determined by the reduction in health burden, between the current vaccination baseline and the new strategy. As we model each individual in each of the 3 communities, we also determined the direct vs indirect protection afforded by vaccination, the herd immunity effect. This talk will present the key study results.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".