Combining analysis of individual and wastewater whole genome sequencing improves SARS-CoV-2 surveillance
Bibliographic record
Abstract
Effective pathogen surveillance is critical for public health decision-making, with both individual and environmental monitoring playing essential roles. While wastewater (WW) and individual whole genome sequencing (WGS) have been used to monitor SARS-CoV-2 dynamics, their complementary potential for enhancing national-level genomic surveillance remains underexplored. This study aimed to evaluate the unique and combined contributions of WW and individual WGS to genomic surveillance. We conducted SARS-CoV-2 WGS on over 4000 WW samples and 23,000 individual samples across Northern Ireland (NI) between 2021 and 2023. SARS-CoV-2 RNA was amplified using the ARTIC nCov-2019 and Mini-XT protocols and sequenced on Illumina MiSeq. Variant compositions in WW data were analysed using Freyja and compared to individual data using time series analysis, correlation assessments, and volatility measurements via numerical derivatives, with mean absolute error (MAE) calculations used to assess concordance. Wastewater treatment plants (WWTPs) were ranked by concordance to individual WGS data. WW and individual WGS complementarity was quantified by mutation classification and overlap analysis. Temporal curve shifting was used to identify lags or leads in variant detection and to infer differences in geospatial spread between WW and individual sequencing data. We confirmed strong concordance between WW and individual variant compositions (mean MAE = 6.2 %). MAE was inversely correlated with sequencing rate (Pearson r=-0.37, p < 0.001) and increased during periods with more circulating variants, highlighting the value of increased sequencing efforts during volatile periods. The population size served by a WWTP was not a reliable indicator of how well its variant composition matched that of the national individual sequencing programme. Both individual and WW-based sequencing (WBS) detected unique, as well as common mutations. Patterns of variant spread within NI were consistent between both programmes (Pearson r = 0.63, p = 0.036), providing complementary insights into variant trends and geospatial spread. We demonstrate that integration of individual and WW WGS data offers more comprehensive SARS-CoV-2 genomic surveillance and improves confidence in predictions of variant composition and spread.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".