The synergy of therapeutic vaccination and timely diagnosis for TB control in high-burden settings: Nunavut as a case study
Bibliographic record
Abstract
Background: Although the overall prevalence of Tuberculosis (TB) in Canada is relatively low, Indigenous communities are disproportionately burdened, with active TB rates exceeding those of Canadian-born non-Indigenous populations by more than 55-fold. In response, the Government of Canada and Inuit Tapiriit Kanatami have launched an initiative to eliminate TB among Inuit by 2030. We evaluated strategies underpinning this initiative, including the potential use of therapeutic TB vaccines that are currently undergoing clinical trials. Methods: We developed an agent-based model of TB transmission dynamics that includes the demographic distributions and household structure of the Nunavut population, as well as TB progression at the individual level. We simulated the model with combinations of interventions, including treatment of active cases, testing, and contact-tracing. We further evaluated the impact of vaccinating active TB cases and those identified with latent TB infection (LTBI) to project the reduction of TB incidence over a 20-year time horizon starting from 2025. In scenario analyses, we considered different vaccine efficacies, diagnostic delays, and rates of contact-tracing. Results: In the absence of a therapeutic vaccine, we estimated a maximum reduction of 20.0% (95% Uncertainty Range [UR]: 15.5% - 24.8%) in active TB incidence with a 4-week diagnostic delay and 75% contact-tracing rate, over the simulated time horizon. Vaccination of active TB cases and those identified with LTBI was projected to decrease the incidence by 87.0% (95% UR: 68.8% - 97.5%) by 2045. Contact-tracing of non-household members exhibited a marginal effect on reducing TB incidence in the presence of vaccination, especially when diagnostic delay was shortened. Conclusions: Our findings highlight the importance of rapid TB diagnosis. The availability of therapeutic vaccines can substantially enhance TB control efforts towards achieving the elimination goal.
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 | 0.002 | 0.007 |
| 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 | 0.000 | 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; a candidate call from one teacher head, 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".