Transmission Network Analysis in Tuberculosis Contact Investigations
Bibliographic record
Abstract
BACKGROUND: Social network analysis (SNA) is an innovative approach to the collection and analysis of infectious disease transmission data. We studied whether this approach can detect patterns of Mycobacterium tuberculosis transmission and play a helpful role in the complex process of prioritizing tuberculosis (TB) contact investigations. METHODS: We abstracted routine demographic and clinical variables from patient medical records and contact interview forms. We also administered a structured questionnaire about places of social aggregation to TB patients and their contacts. All case-contact, contact-contact, case-place, and contact-place dyads (pairs and links) were considered in order to analyze the structure of a social network of TB transmission. Molecular genotyping was used to confirm SNA-detected clusters of TB. RESULTS: TB patients not linked through conventional contact-investigation data were connected through mutual contacts or places of social aggregation, using SNA methods. In some instances, SNA detected connected groups prior to the availability of genotyping results. A positive correlation between positive results of contacts' tuberculin skin test (TST) and location in denser portions of the person-place network was observed (P<.01). CONCLUSIONS: Correlation between TST-positive status and dense subgroup occurrence supports the value of collecting place data to help prioritize TB contact investigations. TB controllers should consider developing social network analysis capacity to facilitate the systematic collection, analysis, and interpretation of contact-investigation data.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".