Developing a Tuberculosis Transmission Model That Accounts for Changes in Population Health
Bibliographic record
Abstract
BACKGROUND: Simulation models are useful in policy planning for tuberculosis (TB) control. To accurately assess interventions, important modifiers of the epidemic should be accounted for in evaluative models. Improvements in population health were associated with the declining TB epidemic in the pre-antibiotic era and may be relevant today. The objective of this study was to develop and validate a TB transmission model that accounted for changes in population health. METHODS: We developed a deterministic TB transmission model, using reported data from the pre-antibiotic era in England. Change in adjusted life expectancy, used as a proxy for general health, was used to determine the rate of change of key epidemiological parameters. Predicted outcomes included risk of TB infection and TB mortality. The model was validated in the setting of the Netherlands and then applied to modern Peru. RESULTS: The model, developed in the setting of England, predicted TB trends in the Netherlands very accurately. The R(2) value for correlation between observed and predicted data was 0.97 and 0.95 for TB infection and mortality, respectively. In Peru, the predicted decline in incidence prior to the expansion of "Directly Observed Treatment Short Course" (The DOTS strategy) was 3.7% per year (observed = 3.9% per year). After DOTS expansion, the predicted decline was very similar to the observed decline of 5.8% per year. CONCLUSIONS: We successfully developed and validated a TB model, which uses a proxy for population health to estimate changes in key epidemiology parameters. Population health contributed significantly to improvement in TB outcomes observed in Peru. Changing population health should be incorporated into evaluative models for global TB control.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".