Spatiotemporal dynamics of HIV-1 transmission networks in a major migration hub: integrated phylogenetic and molecular evidence
Bibliographic record
Abstract
Objective Human Immunodeficiency Virus type 1 (HIV-1) cross-regional transmission poses a critical challenge in China, particularly in high-mobility metropolitan centers. This study aimed to characterize the transmission dynamics between Hangzhou—a megacity with 11.9 million residents (42% migrants)—and other Chinese regions using molecular epidemiology. Methods We analyzed 4,249 Hangzhou-derived and 50,898 non-Hangzhou HIV-1 pol sequences. Molecular transmission network analysis was used to identify transmission clusters, and phylogenetic and Bayesian analyses were conducted to explore lineage characteristics, origins, and expansion of major clusters. Results Molecular transmission network analysis identified 3,317 clusters, incorporating 43.5% (1,848/4,249) of Hangzhou sequences and 32.4% (16,511/50,898) of non-Hangzhou sequences. Crucially, 276 mixed-origin clusters bridged regions, comprising 1,222 (28.8%) Hangzhou and 8,954 (17.6%) non-Hangzhou individuals. Cross-regional connectivity was dominated by Shenzhen (48.1% of 46,962 edges), followed by Beijing (16.5%) and Guangzhou (7.9%). Multivariable regression revealed significantly higher odds of cross-regional connection for males versus females (aOR = 1.376, CI: 1.011–1.869, p = 0.043), homosexual transmission (aOR = 1.28, CI: 1.057–1.550, p = 0.009), non-residents (aOR = 1.207, CI: 1.040–1.402, p = 0.014), and first CD4 + T-cell count of 200–500 cells/uL (aOR = 1.348, CI: 1.057–1.718, p = 0.016). For subtypes, CRF07_BC and URF (CRF07_BC/CRF01_AE) demonstrated significant cross-regional spread versus other subtypes (aOR = 0.163–0.508, p < 0.001). Phylogenetic analysis of all Hangzhou CRF07_BC sequences identified two distinct lineages. Within the largest transmission CRF07_BC cluster, 99.5% of cross-regionally linked Hangzhou sequences (558/561) belonged to Lineage 1 indicating lineage 1 driving cross-regional spread. Bayesian dating indicated major URF clusters (HZC1-3, NHZ) originated between 2014 and 2020 (evolutionary rate: 1.73 × 10 −3 subs/site/year). Conclusion These findings identify key transmission routes connecting Hangzhou to economically developed regions and highlight CRF07_BC/URF strains and mobility as critical drivers. Targeted interventions disrupting these high-risk pathways are urgently needed to reduce regional HIV spread.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".