Incorporating Herd Immunity Effects into Cohort Models of Vaccine Cost-Effectiveness
Bibliographic record
Abstract
BACKGROUND: Cohort models are often used in cost-effectiveness analysis (CEA) of vaccination. However, because they cannot capture herd immunity effects, cohort models underestimate the reduction in incidence caused by vaccination. Dynamic models capture herd immunity effects but are often not adopted in vaccine CEA. OBJECTIVE: The objective was to develop a pseudo-dynamic approximation that can be incorporated into an existing cohort model to capture herd immunity effects. METHODS: The authors approximated changing force of infection due to universal vaccination for a pediatric infectious disease. The projected lifetime cases in a cohort were compared under 1) a cohort model, 2) a cohort model with pseudo-dynamic approximation, and 3) an age-structured susceptible-exposed-infectious-recovered compartmental (dynamic) model. The authors extended the methodology to sexually transmitted infections. RESULTS: For average to high values of vaccine coverage (P > 60%) and small to average values of the basic reproduction number (R(0) < 10), which describes school-based vaccination programs for many common infections, the pseudo-dynamic approximation significantly improved projected lifetime cases and was close to projections of the full dynamic model. For large values of R(0) (R(0) > 15), projected lifetime cases were similar under the dynamic model and the cohort model, both with and without pseudo-dynamic approximation. The approximation captures changes in the mean age at infection in the 1st vaccinated cohort. CONCLUSIONS: This methodology allows for preliminary assessment of herd immunity effects on CEA of universal vaccination for pediatric infectious diseases. The method requires simple adjustments to an existing cohort model and less data than a full dynamic model.
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 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 | 0.035 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".