Demand planning for vaccinations using the example of seasonal influenza vaccination - country comparison and implications for Germany
Bibliographic record
Abstract
BACKGROUND: Annual vaccination is the most important measure to prevent influenza infection. However, demand planning of influenza vaccines is challenging due to seasonal adaptations of virus strains and long production times. The aim was to analyze how other countries manage the demand planning of seasonal influenza vaccines and to draw implications for the German demand planning system for seasonal (influenza) vaccines. METHODS: A two-stage approach has been adopted. As a first step, an analysis of the German demand planning system was carried out to identify key challenges. Second, an analysis of comparable countries with regard to solution strategies was conducted. For this, six comparator countries were selected based on different healthcare systems and structures (Australia, Canada, Great Britain, Singapore, Switzerland, USA). Targeted searches in PubMed, Google Scholar and on websites of agencies and organizations were performed. Further information was requested through e-mail correspondence with the ministries of health and other relevant institutions. In addition, experts from the pharmaceutical industry in the selected countries were approached via written survey. RESULTS: Identified challenges in the demand planning of influenza vaccines in Germany include a lack of reliability of the current demand planning system, bureaucratic burden, lack of binding orders, financial liability of GPs, vaccine discard and limited possibilities of reordering. Various approaches have been identified in six comparator countries. Some of them are already implemented in the German system, others could address the challenges in the German demand planning for influenza vaccines. These include vaccine forecast methods, monitoring systems/vaccination registers, a central platform for orders, (earlier) preorders, centralized purchase system, reimbursement of a surplus and reallocation and return systems. The different approaches are discussed and linked to address the challenges of the German system. CONCLUSIONS: Several approaches have been identified that may be suitable to address the challenges of the German system of (influenza) vaccine demand planning. In the future, further investigation is necessary to assess the potential feasibility and implementation on a health policy level.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".