HIV Phylogenetics Reveals Overlapping Transmission Networks among Cities and Key Populations in Pakistan
Bibliographic record
Abstract
The first case of HIV in Pakistan was documented in 1987, with multiple subtypes and circulating recombinant forms being introduced and currently circulating in the country. Since then, there has been a shift in the country from a low prevalence/high risk to a high-risk concentrated epidemic. Pakistan’s epidemic is concentrated among key populations at greater risk of HIV infection including people who inject drugs (PWID), Hijra sex workers (HSW), female sex workers (FSW), male sex workers (MSW), and men who have sex with men (MSM). This study focused on the geographical aspect as well as on the interactions between key populations at higher risk of contracting HIV. We aimed at understanding the behavior of these key populations at a molecular level with high granularity as well as investigating the possibility of multiple HIV-1 introductions in Pakistan. In this cross-sectional biological and behavioral survey, we collected dried blood spots (DBS) for the purposes of seroprevalence estimates and molecular epidemiology from individuals in 17 cities in Pakistan representing four key populations: PWID, HSW, MSW, and FSW. A total of 1153 envelope sequences (reference positions in HXB2: 7860-8274) of HIV were sequenced using a Sanger-based sequencing approach. To identify clusters based on the introduction of the virus in Pakistan from foreign countries we added 3623 publicly available HIV envelope sequences to our dataset. Phylogeographic inference suggests at least 15 independent introductions of the virus into Pakistan, with a total of 12 clusters ranging from 3 to 675 sequences in size containing sequences from Pakistan and neighboring countries exclusively. Our phylogenetic analysis shows a significant degree of connectivity and directionality suggesting broad and overlapping networks of HIV-1 transmission among cities and key populations in Pakistan.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".