Tracing the Origin of the 2022 Dengue Virus Epidemic in Karachi, Pakistan, Through Genome Analyses
Bibliographic record
Abstract
BACKGROUND: The 2022 dengue outbreak in Karachi, Pakistan, posed a severe threat to the region, yet no study has investigated the origins of the causal strain. METHODS: This study aimed to trace the origins and transmission route of the dengue virus serotype 1 (DENV-1) strain responsible for the 2022 epidemic through genome sequencing and phylogenetic analyses. We successfully sequenced 135 complete DENV-1 genomes from clinical samples using long-read amplicon sequencing. RESULTS: The DENV-1 genotype III strain circulating in Pakistan likely originated from Southeast Asia, with potential connections to China-Singapore strains. Mutation comparisons using reconstructed ancestral sequences indicated that the Pakistan clade shared characteristic nonsynonymous mutations with the 2014-2015 China epidemic strains, indicating the potential origin of the currently circulating DENV-1 strain. Considering the temporal gap between the China and Pakistan epidemics, there is likely a "missing link" in the transmission route. CONCLUSIONS: To our knowledge, our findings provide critical insights into the genetic diversity and transmission patterns of DENV-1 in Pakistan and underscore the need for enhanced genomic surveillance and international collaboration to control the spread and predict outbreaks of DENV.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".