Assessing Control Strategies and Timelines for Mycobacterium Tuberculosis Elimination
Bibliographic record
Abstract
Tuberculosis (TB) continues to inflict a disproportionate impact on Inuit communities in Canada, with reported rates of active TB that are over 300 times higher than those of Canadian-born, non-Indigenous populations. The Inuit Tuberculosis Elimination Framework aims to reduce the incidence of active TB by at least 50% by 2025, with the ultimate goal of eliminating it (i.e., reducing the incidence of active TB below 1 case per 1,000,000 population) by 2030. However, whether these goals can be achieved with the resources and interventions currently available has not been investigated. \n\nThis dissertation formulates an agent-based model (ABM) of TB transmission dynamics and control to assess the feasibility of achieving the goals of elimination framework in Nunavut, Canada. I applied the model to project the annual incidence of active TB from 2025 to 2040, taking into account factors such as time to case identification after developing active TB, contact tracing and testing, patient isolation and compliance, household size, and the potential impact of a therapeutic vaccine. In order to determine the potential reduction in TB incidence, various scenarios of treatment regimens were evaluated within the action plans for TB elimination. The scenario analyses demonstrate that the time-to-identification of active TB cases is a crucial factor in attainability of the goals, highlighting the importance of investment in early case detection. The findings also indicate that the goal of 50% reduction in annual incidence of TB by 2025 is only achievable under best case scenarios of combined interventions. However, TB elimination will likely exceed timelines indicated in the action plans.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".