Use of a Catalytic Model to Estimate Hepatitis A Incidence in a Low-Endemicity Country
Bibliographic record
Abstract
BACKGROUND: Evaluating the cost-effectiveness of vaccine programs with dynamic modeling requires accurate estimates of incidence over time. Because infectious diseases are often underreported, supplementary data and statistical analyses are required to estimate true incidence. This study estimates the true incidence of hepatitis A virus (HAV) infection in Canada using a catalytic model. METHODS: A catalytic model was used to reconcile HAV seroprevalence data with the corresponding true cumulative risk of infection estimated from incidence data. RESULTS: The average annual reported incidence was 6.2 cases per 100,000 from 1980 to 1989 and 7.7/100,000 from 1990 to 1999, indicating that Canada is a low-incidence country. The seroprevalence in Canadian-born individuals (n = 7 studies) was approximately 1%-8% in ages <20, 1%-11% in ages 20-29, 7%-29% in ages 30-39, and higher in older age groups. Between 1980 and 1995, the catalytic model estimated an average annual incidence of 60/100,000 (95% confidence interval, 33-524); approximately 7.73 (4.21-67.33) times the average annual reported incidence of 7.78/100,000. For a typical birth cohort of 403 434 Canadians born in 1990, the model predicted 32 750 HAV cases by age 39, with a corresponding seroprevalence of approximately 8.12% by the year 2029. IMPLICATIONS: Reliable estimates of true incidence of infectious disease are required for cost-effectiveness analysis of infectious disease programs. Catalytic models enable the synthesis of dispersed data, quantification of data limitations, and reconciliation of these limitations to estimate true incidence for economic evaluations.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".