Phylogenetic surveillance of travel-related Zika virus infections through whole-genome sequencing methods
Bibliographic record
Abstract
In 2018, the World Health Organization identified the Zika virus (ZIKV) as a pathogen that should be prioritized for public health research due to its epidemic potential. In this study, whole-genome sequencing (WGS) of travel-acquired ZIKV infections was used to examine the limitations of phylogenetic analysis. WGS and phylogenetic analysis were performed to investigate geographic clustering of samples from five Canadians with travel-acquired ZIKV infections and to assess the limitations of phylogenetic analysis of ZIKV sequences using a phylogenetic cluster approach. Genomic variability of ZIKV samples was assessed and for context, compared with hepatitis C virus (HCV) samples. Phylogenetic analysis confirmed the suspected region of ZIKV infection for one of five samples and one sample failed to cluster with sequences from its suspected country of infection. Travel-acquired ZIKV samples depicted low genomic variability relative to HCV samples. A floating patristic distance threshold classified all pre-2000 ZIKV sequences into separate clusters, while only Cambodian, Peruvian, Malaysian, and South Korean sequences were similarly classifiable. While phylogenetic analysis of ZIKV data can identify the broad geographical region of ZIKV infection, ZIKV's low genomic variability is likely to limit precise interpretations of phylogenetic analysis of the origins of travel-related cases.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".