“Big Events” and HIV Transmission Dynamics: Estimating Time Since HIV Infection from Deep Sequencing Data Among Sex Workers and Their Clients in Dnipro, Ukraine
Bibliographic record
Abstract
Background: Major geopolitical events and structural shocks are thought to play a significant role in shaping HIV epidemics by influencing individual behaviours, reshaping social networks, and impacting HIV prevention and treatment programs. Here, we describe individual-level measures of estimated time since HIV infection (ETI) from viral next-generation sequencing data among female sex workers and their clients in relation to significant geopolitical events in Ukraine. Methods: The Dynamics Study is a cross-sectional integrated biological and behavioural survey conducted among female sex workers and their clients in Dnipro, Ukraine (December 2017 to March 2018). We were able to successfully sequence a portion of the HIV pol gene on dried blood spot specimens among n = 5/9 clients and n = 5/16 female sex workers who tested positive for HIV (total n = 10/25) using an in-house drug resistance genotyping assay. The “HIV EVO” Intrapatient HIV Evolution web-based tool was used to infer ETI from viral diversity. Results: The median ETIs for female sex workers and their clients were 5.4 years (IQR = 2.9, 6.6) and 6.5 years (IQR = 5.4, 10.8), respectively. Nearly all HIV acquisition events (n = 7/10; 70%) were estimated to have occurred between the Great Recession (2008–2009) and the War in Donbas (May 2014–February 2022). In general, ETI suggests that HIV acquisition occurred earlier among clients (2012 [IQR = 2007, 2013]) compared to sex workers (2013 [IQR = 2012, 2016]). Conclusion: Our findings suggest that most HIV acquisition in this small subset of female sex workers and clients living with HIV occurred during periods of economic decline. Molecular studies on timing of HIV acquisition against timing of major geopolitical events offer a novel way to contextualize how such events may shape transmission patterns.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".