Impaired Humoral but Substantial Cellular Immune Response to Variants of Concern B1.1.7 and B.1.351 in Hemodialysis Patients after Vaccination with BNT162b2
Bibliographic record
Abstract
Abstract Whole genome sequencing (WGS) is increasingly used to aid in understanding pathogen transmission [1]. Very often the number of single nucleotide polymorphisms (SNPs) separating isolates collected during an epidemiological study are used to identify sets of cases that are potentially linked by direct transmission. However, there is little agreement in the literature as to what an appropriate SNP cut-off threshold should be, or indeed whether a simple SNP threshold is appropriate for identifying sets of isolates to be treated as “transmission clusters”. The SNP thresholds that have been adopted for inferring transmission vary widely even for one pathogen. As an alternative to reliance on a strict SNP threshold, we suggest that the key inferential target when studying the spread of an infectious disease is the number of transmission events separating cases. Here we describe a new framework for deciding whether two pathogen genomes should be considered as part of the same transmission cluster, based jointly on the number of SNP differences and the length of time over which those differences have accumulated. Our approach allows us to probabilistically characterize the number of inferred transmission events that separate cases. We show how this framework can be modified to consider variable mutation rates across the genome (e.g. SNPs associated with drug resistance) and we indicate how the methodology can be extended to incorporate epidemiological data such as spatial proximity. We use recent data collected from tuberculosis studies from British Columbia, Canada and the Republic of Moldova to apply and compare our clustering method to the SNP threshold approach. In the British Columbia data, different cases break off from the main clusters as cut-off thresholds are lowered; the transmission-based method obtains slightly different clusters than the SNP cut-offs. For the Moldova data, straightforward application of the methods shows no appreciable difference, but when we take into account the fact that resistance conferring sites likely do not follow the same mutation clock as most sites due to selection, the transmission-based approach differs from the SNP cut-off method. Outbreak simulations confirm that our transmission based method is at least as good at identifying direct transmissions as a SNP cut-off. We conclude that the new method is a promising step towards establishing a more robust identification of outbreaks.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".