A nested model for tuberculosis: Combining within-host and between-host processes in a single framework
Bibliographic record
Abstract
Tuberculosis (TB) is among the 10 top causes of deaths worldwide, and one-quarter of the world population hosts latent TB pathogens. Therefore, avoiding the emergence of drug-resistant strains has become a central issue in TB control. In this work, we propose a nested model for TB transmission and control, wherein both within-host and between-host dynamics are modeled. We use the model to compare the effects of three types of antibiotic treatment protocols and combinations thereof in an in silico population. For a fixed value of antibiotics clearance rate and relative efficacy against resistant strains, the oscillating intermittent protocol, pure or combined, is the most effective against the sensitive strains. However, this protocol also creates a selective advantage for the resistant strains, returning the worst result in comparison to the other protocols. We suggest that nested models should be further developed, since they might be able to inform decision-makers regarding the optimal TB control protocols to be applied under the specific parameters and other epidemiological factors in different populations.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".