Historical Trends in the Hepatitis C Virus Epidemics in North America and Australia
Bibliographic record
Abstract
BACKGROUND: Bayesian evolutionary analysis (coalescent analysis) based on genetic sequences has been used to describe the origins and spread of rapidly mutating RNA viruses, such as influenza, Ebola, human immunodeficiency virus (HIV), and hepatitis C virus (HCV). METHODS: Full-length subtype 1a and 3a sequences from early HCV infections from the International Collaborative of Incident HIV and Hepatitis C in Injecting Cohorts (InC3), as well as from public databases from a time window of 1977-2012, were used in a coalescent analysis with BEAST software to estimate the origin and progression of the HCV epidemics in Australia and North America. Convergent temporal trends were sought via independent epidemiological modeling. RESULTS: The epidemic of subtype 3a had more recent origins (around 1950) than subtype 1a (around 1920) in both continents. In both modeling approaches and in both continents, the epidemics underwent exponential growth between 1955 and 1975, which then stabilized in the late 20th century. CONCLUSIONS: Historical events that fuelled the emergence and spread of injecting drug use, such as the advent of intravenous medical therapies and devices, and growth in the heroin trade, as well as population mixing during armed conflicts, were likely drivers for the cross-continental spread of the HCV epidemics.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".