Combining analysis of individual and wastewater whole genome sequencing improves SARS-CoV-2 surveillance.

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.