Exploring the epidemiological impact of universal access to rapid tuberculosis diagnosis using agent-based simulation
Bibliographic record
Abstract
Many high-burden countries have committed to providing universal access to rapid diagnosis of tuberculosis (TB), but the corresponding impact on population-wide incidence is unknown. We designed an agent-based simulation of drug-susceptible (DS) and drug-resistant (DR) TB in a representative Indian setting and compared the impact of Xpert testing via a decentralized (Xpert available at each local-population) versus centralized (Xpert available at the district-level serving multiple local-populations) strategy. Decentralized testing resulted in a 36% reduction in DR-TB incidence at 10 years compared to no Xpert. Depending on assumptions regarding pre-treatment loss to follow-up (ranging from 5 to 50%), the impact of centralized testing ranged from a 35% to 22% reduction in DR-TB incidence. Implementation of Xpert by either approach had a negligible impact (<5%) on DS-TB incidence. Decisions regarding choice of centralized vs. decentralized Xpert will heavily depend on operational aspects of centralized Xpert and loss to follow-up.
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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 | 0.003 | 0.014 |
| 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 | 0.001 |
| 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".