Increased HIV Subtype Diversity Reflecting Demographic Changes in the HIV Epidemic in New South Wales, Australia
Bibliographic record
Abstract
Changes over time in HIV-1 subtype diversity within a population reflect changes in factors influencing the development of local epidemics. Here we report on the genetic diversity of 2364 reverse transcriptase sequences from people living with HIV-1 in New South Wales (NSW) notified between 2004 and 2018. These data represent >70% of all new HIV-1 notifications in the state over this period. Phylogenetic analysis was performed to identify subtype-specific transmission clusters. Subtype B and non-B infections differed across all demographics analysed (p < 0.001). We found a strong positive association for infections among females, individuals not born in Australia or reporting heterosexual transmission being of non-B origin. Further, we found an overall increase in non-B infections among men who have sex with men from 50 to 79% in the last 10 years. However, we also found differences between non-B subtypes; heterosexual transmission was positively associated with subtype C only. In addition, the majority of subtype B infections were associated with clusters, while the majority of non-B infections were singletons. However, we found seven non-B clusters (≥5 sequences) indicative of local ongoing transmission. In conclusion, we present how the HIV-1 epidemic has changed over time in NSW, becoming more heterogeneous with distinct subtype-specific demographic associations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.002 |
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".