Combining individual and wastewater whole genome sequencing improves SARS-CoV-2 surveillance
Bibliographic record
Abstract
ABSTRACT Background Robust methods to track pathogens support public health surveillance. Both wastewater (WW) and individual whole genome sequencing (WGS) are used to assess viral variant diversity and spread. However, their relative performance and the information provided by each approach have not been sufficiently quantified. Therefore, we conducted a comparative evaluation using extensive individual and wastewater longitudinal SARS-CoV-2 WGS datasets in Northern Ireland (NI). Methods WGS of SARS-CoV-2 was performed on >4k WW samples and >23k individuals across NI from 14 th November 2021 to 11 th March 2023. SARS-CoV-2 RNA was amplified using the ARTIC nCov-2019 protocol and sequenced on an Illumina MiSeq. Wastewater data were analysed using Freyja to determine variant compositions, which were compared to individual data through time series and correlation analyses. Inter-programme agreements were evaluated by mean absolute error (MAE) calculations. WW treatment plant (WWTP) performances were ranked by mean MAE. Volatile periods were identified using numerical derivative analyses. Geospatial spreading patterns were determined by horizontal curve shifting. Findings Strong concordance was observed between wastewater and individual variant compositions and distributions, influenced by sequencing rate and variant diversity. Overall variant compositions derived from individual sequences and each WWTP were regionally clustered rather than dominated by local population size. Both individual and WW sequencing detected common nucleotide substitutions across many variants and complementary additional substitutions. Conserved spreading patterns were identified using both approaches. Interpretation Both individual and wastewater WGS effectively monitor SARS-CoV-2 variant dynamics. Combining these approaches enhances confidence in predicting the composition and spread of major variants, particularly with higher sequencing rates. Each method detects unique mutations, and their integration improves overall genome surveillance. Funding Individual sequencing was funded via the Belfast Health and Social Care Trust (Department of Health for Northern Ireland) and the COVID-19 Genomics UK (COG-UK) consortium, which was supported by the Medical Research Council (MRC), UK Research and Innovation (UKRI), the National Institute for Health Research (NIHR), the Department of Health and Social Care (DHSC), and the Wellcome Sanger Institute. The NI Wastewater Surveillance Programme was funded by the Department of Health for Northern Ireland. EPT was supported through the COG-UK Early Career Funding Scheme.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".