Estimation of the Potential Benefits of Meningococcal Vaccination in Children at 9 and 12 Months of Age Using a Predictive Mathematical Model
Bibliographic record
Abstract
Relevance. To address the issue of including vaccines against meningococcal infection (MI) in the Russian National Immunization Schedule (NIS), convincing arguments must be presented that demonstrate not only epidemiological, but also economic benefits. Aim of this study was conducted to confirm them. Materials & methods. For calculating epidemiological consequences, a dynamic predictive simulation model was constructed to compare the potential epidemiological burden of the disease in the current vaccination scenario (no MI vaccination in the NIS) and a new scenario involving vaccination of children aged 9 and 12 months with the MenACWY-d vaccine. The epidemiological outlook for meningococcal infection was assessed based on the dynamics of the main indicators of its prevalence in the General population that developed in previous years, taking into account the impact of double vaccination of children at 9 and 12 months on the survival period of each age cohort vaccinated in 2019–2034. The aim is to assess the predicted socio-economic consequences for different scenarios: while maintaining the current vaccination algorithm and including in the NIS vaccination against MI of all children aged 9 and 12 months using mathematical modeling. Results and discussion. The greatest impact on reducing the number of clinical cases of the disease will be achieved in the age cohorts 0–1 years (-89%), 1–2 years (-84.5%), 3–6 years (-73.6%). Model calculations show that due to double vaccination of children under one year of age, 571 deaths can be expected to be prevented by 2034, which is equivalent to a reduction in losses of 40,509 years of life ahead and a social gain of 104.7 billion rubles in the monetary equivalent of the cost of these years (cumulative total). At the same time, taking into account the prevented cases of the disease, the total monetary equivalent of the benefits of society will begin to exceed the cost of vaccination earlier than in four years. Conclusions thus, even an underestimated estimate of benefits that does not take into account the total amount of damage prevented (prevention of 571 deaths, loss of 40,509 years of life and 104.7 billion rubles in monetary terms of the cost of years of life to come), indicates the obvious importance of expanding the NIS and including vaccination of children aged 9 and 12 months from meningococcal infection.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".